ATDEPARTMENT OF HEALTH AND HUMAN SERVICES

FOOD AND DRUG ADMINISTRATION

CENTER FOR DRUG EVALUATION AND RESEARCH

ORALLY INHALED AND NASAL DRUG PRODUCTS SUBCOMMITTEE

OF THE ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE

Tuesday, April 26, 2000

8:30 a.m.

CDER Advisory Committee Conference Room
5630 Fishers Lane
Rockville, Maryland

PARTICIPANTS

Vincent H.L. Lee, Ph.D., Chairperson
Nancy Chamberlin, Executive Secretary

MEMBERS

Richard Ahrens, M.D.
James Li, M.D., Ph.D.
Stanley J. Szefler, M.D.

CONSUMER REPRESENTATIVE

Gloria L. Anderson, Ph.D.

INVITED GUESTS

Michael Baaske, Ph.D.
Charan Behl, Ph.D.
Richard N. Dalby, Ph.D.
Harmut Derendorf, Ph.D.
William Gore, Ph.D.
Lester I. Harrison, Ph.D.
Walter W. Hauck, Ph.D.
Sylvie Laganiere, Ph.D.
Thomas R. MacGregor, Ph.D.
Nikhil J. Parekh
Sam C.K. Shum, Ph.D.

FDA 

Wallace P. Adams, Ph.D.
Guirag Poochikian, Ph.D.
Eric Sheinin, Ph.D.

C O N T E N T S

Conflict of interest Statement:
Nancy Chamberlin	6

Call to Order: 
Vincent H.L. Lee, Ph.D.	7

Introduction and Objectives: 
Eric Sheinin, Ph.D.	10

Chemistry, Manufacturing and Controls:
Content Uniformity

Current FDA Practices for NDAs: 
Guirag Poochikian, Ph.D.	16

Alternative Statistical Approaches: 
Walter W. Hauck, Ph.D.	25

Subcommittee Discussion 	37

In Vitro BA and BE Testing

Profile Analysis of Cascade Impactor Data,
Proposed FDA Approach: 
Yi Tsong, Ph.D.	48

Subcommittee Discussion(Continued)	65

Bioavailability (BA) and Bioequivalence (BE)

Current FDA BA/BE Background and Issues: 
Wallace P. Adams, Ph.D.	80

In Vitro BA and BE Testing

Profile Analysis of Cascade Impactor Data,
An Alternative View: 
Andrew R. Clark, Ph.D.	88

DPIs: In Vitro Tests for Performance and Comparability:
David Ganderton, Ph.D.	104

Subcommittee Discussion	118

Open Public Hearing

Data Related to BE Testing of Nasal Sprays
and Comments on the BE Studies of Nasal Sprays
for Systemic Action:
Abdul Zahir, Ph.D.	140

C O N T E N T S(Continued)

Uniqueness of Lingual Spray Delivery:
Harry Dugger, Ph.D.	145

APPS Inhalation Technology Focus Group (ITFG)/
International Pharmaceutical Aerosol Consortium (IPAC)
Collaborative Technology Teams

Overview of the ITFG/IPAC Collaboration:
R. Harris Cummings, Ph.D.	151

Presentation of the Work of the BA/BE Team:
Stephen J. Farr, Ph.D.	155

Presentation on the Work of the Specifications Team
(Dose Content Uniformity/Particle Size Distribution):
Bo Olsson, Ph.D.	158

Presentation on the Work of the Tests and Methods Team:
Carole Evans, Ph.D.	161

Presentation on the Work of the Leachables and 
Extractables Team:
Kaushik J. Dave, R. Ph.	164

Presentation on the Work of the Supplier Quality Control
Team:
Gordon Hansen	167

Concluding Presentation on ITFG/IPAC Collaboration:
Cynthia Flynn, Ph.D.	170

CMC Issues:
Kenneth B. Neugebauer	175

Growth Effects of Nasal Steroids in Children and
Differences among the Steroid Preparations:
Eric J. Schenkel, M.D.	178

In Vivo BA and BE

Clinical Studies for Local Delivery of Nasal Aerosols
and Sprays:
Izabela Roman, M.D., Ph.D.	182

PK and PD Studies for Systemic Exposure of
Locally Acting Drugs

Academic View:
Harmut Derendorf, Ph.D.	194

C O N T E N T S(Continued)

In Vivo BA and BE

Clinical Studies for Local Delivery of Orally Inhaled
Corticosteroids:
Richard C. Ahrens, M.D.	213

Subcommittee Discussion 	230

PK and PD Studies for Systemic Exposure of
Locally Acting Drugs

Current PK and PD Practices:
Venkata R.S. Uppoor, Ph.D.	254

Industry View:
Lester I. Harrison	261

Subcommittee Discussion 	275

P R O C E E D I N G S
Conflict of Interest Statement
MS. CHAMBERLIN:  Good morning.  We are ready to start.  Bear with us with the sound system.  It is very questionable if it is working.  We are testing this room and it is testing our limits.
It turns out the room is full.  We have set up a few chairs in the overflow room, but your seats are like gold.  There will be a change in a few speakers so please bear with us.  Yi Tsong will be speaking before Dr. Adams today.  There is a change this afternoon.  We will have Dr. Derendorf speaking after Dr. Roman.
I am going to go ahead and read the purpose statement for the conflict of interest and then, after that, Dr. Lee will have introductions and open the meeting.
The following announcement addresses conflict of interest with regard to this meeting and is made a part of the record to preclude even the appearance of such at this meeting. 
In accordance with 18 USC 208, general‑matters waivers have been granted to all committee participants who have interests in companies or organizations which could be affected by the committee's discussion of specific scientific issues where the additional expertise of the subcommittee is sought to aid the agency in refining draft guidance for orally inhaled and nasal drug products in certain areas of chemistry, manufacturer and controls, and in vitro and in vivo bioavailability/bioequivalence.
A copy of these waiver statements may be obtained by submitting a written request to the agency Freedom of Information Office, Room 12A‑30, Parklawn Building.  In the event that the discussions involve any other products or firms not already on the agenda for which an FDA participant has financial interest, the participants are aware of the need to exclude themselves from such involvement and their exclusion will be noted for the record.
With respect to all participants, we ask, in the interest of fairness, that they address any current or previous financial involvement with any firm whose products they may wish to comment upon.
I just want to explain in a nutshell what we were trying to say with our legal words, and that is we are not discussing specific products at this committee.  This is a subcommittee that is made up of industry.  Because we are not discussing specific products, we have given the committee general matters.
This is a subcommittee that will not vote.  It will have discussions on issues as the FDA presents them.
Call to Order
DR. LEE:  Thank you, Nancy.  I think, in the interest of time, I will just introduce myself.  I am Vincent Lee, the Chair of this subcommittee.  I do have a full‑time job which is I am Chairman and Professor at the University of Southern California.
I think it might be useful to go around the table and everyone introduce himself or herself, where they are from, and we will go from there.
DR. MacGREGOR:  I'm Tom MacGregor.  I am a Highly Distinguished Scientist at Boehringer‑Ingelheim.  That is the title.  [Laughter.]
DR. ANDERSON:  I am Gloria Anderson.  I am Callaway Professor of Chemistry and Chair at Morris Brown College in Atlanta, Georgia.
DR. BAASKE:  I am Michael Baaske.  I am with Alpharma USPD.
DR. LAGANIERE:  Good morning.  I am Sylvie Laganiere, Director of Pharmacokinetics at Phoenix International now, under new merger, MDS.
DR. DALBY:  I am Richard Dalby.  I am Vice Chair of Pharmaceutical Sciences at the University of Maryland, completely undistinguished.  [Laughter.]
DR. GORE:  My name is Bill Gore.  I am Director of Analytical Sciences at Boehringer‑Ingelheim Pharmaceuticals.
MR. PAREKH:  I am Nikhil Parekh.  I am Director of Analytical Development at Whitehall‑Robins and representing CHPA.
DR. ADAMS:  Wallace Adams, Food and Drug Administration and Office of Pharmaceutical Science.
DR. POOCHIKIAN:  Guirag Poochikian, Chemistry Team Leader in Pulmonary and Allergy Drug Products in FDA.
DR. HAUCK:  I am Walter Hauck.  I am Professor and Head of Biostatistics at Thomas Jefferson University in Philadelphia.
DR. HARRISON:  Les Harrison, Division Scientist, 3‑M Pharmaceuticals.  I am representing the IPAC Bioequivalence Component.
DR. DERENDORF:  I am Helmut Derendorf, Professor and Chairman of the Department of Pharmaceutics, University of Florida.
DR. SHEININ:  Eric Sheinin, Deputy Director of the Office of Pharmaceutical Science, CDER in FDA.
DR. SZEFLER:  Stan Szefler at the National Jewish Medical and Research Center and also a member of the Pulmonary and Allergy Drug Advisory Panel.
DR. BEHL:  Charan Behl, EVP and R&D of Nastech Pharmaceutical Company, also representing Nasal Drug Relief Focus Group from the AFBS.
DR. LI:  I am James Lee.  I am an allergist and internist at the Mayo Clinic, formerly of the Pulmonary and Allergy Drug Advisory Committee.
DR. SHUM:  My name is Sam Shum, Director of Analytical Chemistry for Aerosols of KOS Pharmaceuticals.
DR. LEE:  Thank you very much.  I do want to remind everybody that these proceedings are being taped.  Let me open the meeting by inviting Dr. Eric Sheinin from the FDA to give an introduction and state the objectives of the meeting.
Introduction and Objectives
DR. SHEININ:  Good morning.  I have to say I am a little overwhelmed by the size of the audience.  I don't know that we expected quite this many people here.  It is very rewarding and encouraging to see the tremendous amount of interest in this area.
[Slide.]
What I would like to do is kind of set the stage for the discussions that we are going to have throughout the day today.  
[Slide.]
The responsibilities for the subcommittee are mainly three.  One is to, certainly, address and discuss the questions that have been raised and presented to the subcommittee that are related to the issue of content uniformity for both orally inhaled and nasal drug products.  There are, I believe, two questions that need to be addressed in this area.
Once we are finished with that, we will ask the subcommittee to go on and address and discuss the questions that have been developed and submitted to them that are related to both in vitro and in vivo bioavailability and bioequivalence for these types of drug products.
As Dr. Lee mentioned, there will not be a vote taken at this subcommittee meeting.  What we want from the subcommittee is a thorough scientific discussion of the questions and the issues.  There will be transcripts of what takes place today and this information will be used by the agency as we continue to go forward with the development of guidances and policy related to these types of drug products.
Finally, there will be a presentation by the subcommittee.  As to who will actually make the presentation, it may be the Chair.  It may be somebody else that is designated by the subcommittee to represent them at a formal meeting of the Advisory Committee for Pharmaceutical Science.
The next meeting has tentatively been scheduled for August of this year but there is a possibility that we may postpone this until September or October because there are other issues that we would like to take to that advisory committee meeting and we may not be quite ready by August to discuss those at the advisory committee.
[Slide.]
To date, we have issues three draft guidances in the area of orally inhaled and nasal drug products.  Two are related to chemistry and manufacturing and controls and one is related to bioavailability and bioequivalence.  We are planning to issue another guidance on bioavailability and bioequivalence for the nasal spray and inhalation, solution, suspension and spray drug products.  That will be issued for public comment at some point in the future.
[Slide.]
Just to quickly go over what the questions are.  I presume everybody has these questions now.  There are the content‑uniformity questions.  These are the CMC or chemistry, manufacturing and controls questions.
Should there be a single content uniformity standard for all orally inhaled and nasal drug products or, conversely, one could look at it, should there be different content uniformity standards depending on each individual product or type of product.  And should the FDA continue to develop a proposed statistical approach to evaluate the data that are obtained when content‑uniformity testing is performed?
So those are the two CMC questions that need be addressed by the subcommittee this morning.
[Slide.]
Then we will go on to the bioavailability and bioequivalence.  The first set of questions are related to in vitro bioavailability and bioequivalence testing.  The first set will deal with profile analysis.  Should all the stages of the cascade impactor be considered when looking at the data and evaluating, comparing the reference product and the product under development or discussion, or the product that is the subject of an application submitted to the agency.
Should there be a statistical approach as opposed to a qualitative comparison for these profiles.  If the answer is yes, then is the chi‑square comparative‑profile appropriate or should there be some other approach?  Is chi square, by itself, sufficient or should we go on from there?
[Slide.]
Also, under in vitro testing, for dry‑powder inhalers, the comparability of them.  But, prior to doing in vivo studies to establish the equivalence of these products, a firm would need to design its product to have the best likelihood of being found equivalent in these in vivo studies.
There is a type on this slide and I would ask everybody‑‑the second line from the bottom where it says, "What comparative in vivo tests should be conducted?" that should be in vitro tests.  If everybody would please correct that.  There is a significant difference there.
Also, what design features of the device and formulation and what parameters should be considered by the firm developing the product in trying to determine pharmaceutical equivalence of these products.
[Slide.]
So those are the in vitro bioavailability and bioequivalence testing questions that we would like the subcommittee to address.  Once that portion of the discussion is completed, we will go on to in vivo testing questions. 
The first set deal with clinical studies that would be designed for local delivery of nasal aerosols and sprays.  In the draft guidance, three study designs have been proposed for drugs that are intended to have local action.  These are traditional treatment study, days‑in‑the‑park study and environmental‑exposure unit study.
All these designs are based on seasonal allergic rhinitis.  The first question is, is it feasible to demonstrate a dose response for these locally acting nasal drugs.  If it is not, what other approaches would the subcommittee recommend?  What else could we and the industry rely on to establish that these are equivalent.
[Slide.]
The next question is can bioequivalence be established based on seasonal allergic rhinitis.  Can this assure bioequivalence for other indications?  I think that is a very significant question and we would like the subcommittee to address that today.
[Slide.]
In terms of clinical studies for local delivery of orally inhaled corticosteroids, again, a number of approaches have been proposed to assess bioequivalence of these products; as examples, clinical trials, bronchoprovocation tests, steroid‑reduction model, trials with surrogate measures such as exhaled nitric oxide.
We would like the subcommittee to address these in terms of are any of these study designs proven to offer better discrimination in terms of dose‑response sensitivity.
[Slide.]
Continuing with the clinical studies for orally inhaled corticosteroids, are there any other in vivo approaches.  Again, there are some examples given, surrogate markers that might be sufficiently sensitive and validated to establish in vivo bioequivalence and bioavailability for these inhaled corticosteroids.
We would very interested in any advice and counsel that the subcommittee can present today during their discussions.
[Slide.]
Finally, in terms of the area of PK or PD studies for systemic exposure of locally acting drugs, are there any situations where in vitro data plus systemic PK and PD data can be relied on to assure local drug delivery for either nasal or inhaled products.
These are the questions that we would like to have addressed.  It is a very, very full agenda.  It certainly is all of our sincere hope that the subcommittee will be able to get through all of this today.  Again, as Dr. Lee said, in the interest of time, I think we should proceed to the first topic and, hopefully, we will get through everything by sometime late this afternoon.
If you notice there is no adjournment time given.  I assume people have flights to catch, so we will do our best to stay on schedule.
Thank you.
DR. LEE:  Thank you very much, Dr. Sheinin, for this very nice introduction.  
I would like now to move to the first section of this meeting which is the CMC on content uniformity.  Dr. Guirag Poochikian is going to provide us with the current FDA practices for NDAs.
Chemistry, Manufacturing and Controls: Content Uniformity
Current FDA Practices for NDAs
DR. POOCHIKIAN:  Good morning.
[Slide.]
First I would like to give you a brief background information concerning the genesis of the guidance.
[Slide.]
Because of the Montreal protocol and the various proposed phase‑out programs of the CFC‑containing products, the non‑CFC‑containing drug products such as MDIs, DPIs and other inhalation drug products have received a great deal of attention such as alterative formulations and container‑closure systems to deliver the required dose to the biological target, appropriate regions of the lungs or the nasal airways.
Due to these activities, the agency took the initiative in drafting two of these guidances.  They are called metered‑dose inhalers and the IN DPI drug products CMC documentation, and the second one is nasal‑spray inhalation, suspension and spray‑drug products, again from a CMC perspective.  They are cited on the website.  The address is there.
The purpose of this is to cover essentially most of the inhalation drug products which are currently available or are under investigation.  As all of us are aware, these drug products are complex units with many challenges.  They do have unique features compared to other more conventional drug products with respect to formulation components and their suitability for inhalation use with regard to container‑closure systems, delivery systems and with regard to the controls of each of these components as well as the drug product, itself.
We, at the agency, are interested in publishing, both scientifically and regulatorywise, sound guidances, always having in mind, of course, the public‑health interest.
We, at the agency, are of the opinion that such guidances will help drug‑development efforts for these unique drug products, facilitate submission and review of these applications, expedite the approval of these important drugs and make them available in high quality to the public.
The content of these guidances are based upon experiences, issues that have been dealt with and challenges that have been faced during the development and review of numerous and different types of drug applications, particularly in the last decade.
Essentially, these two guidances summarize and organize the information acquired in the last decade in a user‑friendly manner to be easily and equally accessible to the interested parties.  In a nutshell, these guidance delineate the current practices for NDAs.
[Slide.]
The scope of these guidances are outlined on this slide.  As you see, there are two sets.  The first set covers the NDIs and DPIs, non‑aqueous based and the second set covers the aqueous‑based preparations.
[Slide.]
I would like to say a few words about the guidance philosophy because it is important to our discussion.  As any other guideline, these guidances also set forth approaches which are acceptable to the agency for submission of the CMC information.  Also it presents the agency's current thinking on the CMC documentation for inhalation drug products.  Also it indicates that alternative approaches may be used.
Also, in conjunction with that, it encourages discussion with the agency review division for significant departures.  Like any other guidance, also there is a statement saying that it does not create or confer any right on any person and does not operate by FDA or the public.
[Slide.]
What are the activities since the publication of these guidances?  The first NDI/DPI was published in late October and the public comment period was closed in early March, 1999.  A workshop sponsored by AAPS/FDA/USP was held in early June and, similarly, the public comments for the second guidance which was issued on June 2 of 1999 was closed in early September of 1999. There was a preliminary subcommittee OINDP meeting in early November.
[Slide.]
As to the dose‑content uniformity, to insure the drug‑product quality in terms of dose consistency, the dose‑content uniformity issues need to be addressed from three different perspectives.  First is unit‑to‑unit dose‑content uniformity within a batch‑‑that is inter‑unit or inter‑container or intra‑batch dose‑to‑dose uniformity.
The second is dose‑to‑dose content uniformity within a unit, within a container, intra‑unit from the beginning to the end of a unit.  The third one is batch‑to‑batch dose‑content uniformity which is inter‑batch which is not the topic of discussion today.  That is usually handled through stability studies.
[Slide.]
What are the acceptance criteria currently being used for NDAs at FDA?  First, with regard to inter‑container dose‑content uniformity.  It consists of two tiers.  In the first tier, there are ten containers or ten units and one determination from each unit.
That particular batch would be considered acceptable if not more than 10 percent, outside 18 to 120Êpercent of the target‑emitted dose and none outside plus‑or‑minus 25 percent of the labeled claim provided the mean of the ten determinations are within plus‑or‑minus 15Êpercent of the labeled claim.
If 20 or 30 percent of those determinations are outside plus‑or‑minus 20 percent of the labeled claim and none outside plus‑or‑minus 25 percent of the labeled claim, and provided the mean still is within plus‑or‑minus 15Êpercent of the labeled claim, then the second tier may be utilized by doubling the sample size to twenty.
So, in total, there will be thirty determinations.  Out of those thirty determinations, an oral batch will be considered adequate and acceptable if not more than 10Êpercent is outside plus‑or‑minus 20 percent of the labeled claim and none outside plus‑or‑minus 25 percent.  Again, the mean shall be plus‑or‑minus 15 percent of the labeled claim.
[Slide.]
With regard to intra‑container dose‑content uniformity from the beginning to the end of a unit, again, it consists of two tiers.  The first tier uses three samples and taking samples from the beginning, middle and end so there will be three determinations per unit.  In total, there will be nine determinations.
The batch will be considered acceptable for intra‑container dose‑content uniformity if not more than one out of nine shall be outside plus‑or‑minus 20 percent of the labeled claim and none outside plus‑or‑minus 25 percent provided each of the means, beginning, middle and end separately are within plus‑or‑minus 15 percent of the target‑emitted dose.
If two or three of those determinations are outside 80 to 120, provided those are not outside plus‑or‑minus 25 percent and provided each of the means are within plus‑or‑minus 15 percent, then a second tier may be utilized, again by doubling the sample size.
So, in total, there will be 27 determinations considering the initial tier 1.  In that case, the overall batch will be acceptable if not more than three are outside plus‑or‑minus 20 percent of the target‑emitted dose and none outside plus‑or‑minus 25 percent of the emitted dose.
The mean for each at beginning, middle and end shall be plus‑or‑minus 15 percent of the target‑emitted dose.
[Slide.]
I would like to say a couple of words about the testing conditions.  When these acceptance criteria were specified, we had in mind certain assumptions.  First, the samples are stored under specified storage conditions and orientations because it is well‑known that some of these products will have significant variability, high variability, if this is not done.  So that particular variability has been removed from these test conditions.
Second, trained personnel are used to follow certain standard operating procedures for testing each of these units because, again, it is known that high variability will be obtained if these procedures are not followed in terms of uniform shaking, how long they shake it, how frequently the mouthpiece or the actuator is cleaned or new actuators are used, what is the depression force and the actuation force, what is the store plant and so forth.
All of these conditions will impact negatively if they are not controlled.  Again, these factors have been eliminated from these test results.
More importantly, all these units are fully primed before testing and all of us know what is the significance of priming because unprimed units will give totally aberrant results, also.
Next, the test results are obtained under specified testing conditions; predefined flow rates and predefined duration.  Of course, this applies mostly to DPI situations.  So we need to consider all these factors in our deliberations.
[Slide.]
As to the public comments, I would like to summarize the various categories concerning dose‑content‑uniformity specifications.  One category of comments, actual specifications for DCU, should not be incorporated into the guidance.  That category, they didn't want these specs in the guidance.  One explanation, which is in quotations, says, "Note that each drug is unique with respect to the capabilities and reproducibility of the manufacturing process, device components and analytical methodology."
Of course, that is a disturbing comment, but that is what the explanation is.  "And that these parameters should be considered in establishing appropriate specifications."
[Slide.]
Another category says to establish a process by which DCU specs may be determined on a case‑by‑case basis.  However, that category of comments did not provide what process they had in mind.  
The next category recommends to retain the guidance specifications, however, to widen the individual dose‑acceptance criteria.  Here, they are referring to the inner and outer limits of plus‑or‑minus 20 percent and plus‑or‑minus 25 percent, respectively.
The next category of comments indicates to recommend, retain guidance specifications in the draft, but,  However, to delete the mean criterion for the first tier.  As you heard, in our proposal, there is a mean criterion at each tier, tier 1 and tier 2.  This particular one recommends to delete from tier 1 and applied only to tierÊ2. 	The last category I will comment on was the recommendation to provide a process for setting dose‑containing‑uniformity specifications using statistical procedures.  That, Dr. Hauck is going to discuss.
Thank you.
DR. LEE:  Thank you.
Dr. Hauck:
Alternative Statistical Approaches
DR. HAUCK:  Good morning.
[Slide.]
We are feeling a little bit on the wrong side of the technological divide this morning.  Transparencies, it is.  I was asked by the agency, actually some time ago, to do an evaluation from a statistical perspective of the dose‑content‑uniformity criteria that were in the draft guidances. 
What I will be presenting this morning is sort of the state of what that evaluation is.
[Slide.]
The usual disclaimer applies here.  I am speaking that the work is supported by the FDA through a contract to Jefferson but the opinions I will be expressing are solely those of myself and should not be construed to represent the agency's opinion.
[Slide.]
I will do this, kind of first comment from the statistical perspective on the content‑uniformity standard that Guirag just presented to you and then outlining alternatively how a statistician might go about doing this.
[Slide.]
So this is the within‑batch between‑canister dose‑content‑uniformity standard that Guirag just presented to you.  There are the two tiers and the variety of the requirements.  Largely, in terms of what I will be talking about, I will actually be focussing primarily on this part of the requirement in terms of the 80 to 120 piece.
This part, the 75/125, I tend to think of as a safety net and it really needs to be thought of separately.  	The first thing that is important when looking at this criterion, or really very similar criteria from the USP or the CPNP European guidance, is what is the unit, what is being looked at as the unit of analysis here.  So the dose‑content uniformity within batch that Guirag was talking about is one dose per container.
So we are talking about ten or thirty containers or canisters from a single batch.  This is important because one of the things you sometimes hear is why doesn't the FDA adopt the USP requirement.  The first thing you have to see is that the unit, really, is very different than the USP because the USP is doing one or three canisters with up to ten doses per canister.
So the first statistical comment is to realize that the FDA is intending to draw inference or trying to say something about the batch that the USP requirement is not.
[Slide.]
So this is what I am just mentioning here.  That is really the first thing when you look at the different requirements.  So the USP is actually different than any of the other criteria that have been proposed by both the FDA, the JP, the CPMP and PhRMA.
The next thing, when I look at this, what I am saying is a statistical‑hypothesis test.  It is not being labeled as such, but there is a standard to be set, there is a decision to be made, data collected, somebody to evaluate the data, the data meets a certain criterion and you say you pass.  If it doesn't, you say, no pass.
While it has to form the statistical‑hypothesis test, there is something very crucial missing; that is, what are the hypotheses.  So I think, really, the primary takeaway message that I would like to leave you is that maybe the focus should be not on whether it is one tier and ten canisters but on what the hypothesis should be that the dose‑content uniformity is intending to address.
While I am focussing today on the FDA's criterion because that is the topic of the day, I guess I would like to be clear that most of what I am going to say really applies to all the other criteria as well, so this is not singling out the FDA in that regard.
The other issue that is sort of statistically evident from the structure, again, at both the FDA, USP and CPN‑‑actually all these proposals‑‑is that two tiers has looked at what, in the clinical‑trials literature, we call an interim analysis.  You collect some data.  If the data is good enough, you say pass and you are happy.  If the data is not good enough, you go on and do some more.
That is relevant statistically because that is two opportunities to make the decision and that needs to be thought about in the statistical methodology.
[Slide.]
To put this in a statistical perspective or an alternative statistical approach, I now need to be kind of sure we are in agreement on some of the language, so I am going to ask you to use my language a little bit for the next few minutes.  We need to have two different error rates, or error probabilities, that we talk about in this field.
A more general term would be a false‑positive rate first.  This is sometimes referred to as the consumer risk.  In standard statistics books you usually see this referred to as alpha or the type‑1 error rate.  What we are referring to there, in false positives, it means a batch that, in some sense, is unacceptable.  I put that in quotes because the unacceptable part of it is really not my part of it.  That is more Guirag's field.
Some batch that is unacceptable, then, is passed by this criterion putting the consumer at risk.  The flip side of that is the false‑negative, most typically referred to in statistics books as a type‑2 error or beta and sometimes referred to as producer risk.  This is the chance that a batch is absolutely fine.
In a clinical‑trial context, what we typically would have set up is the false‑positive rate would be set usually at 0.05, sometimes less, and the sponsor of the study determines what producer risk, or false‑positive rate, they are willing to accept.
In the context of dose‑content uniformity, you need to have two more things I need to talk about.  One is the target interval.  That target interval corresponds to the FDA's criterion, the 80, 120 percent, the idea that most of the batch had fallen in some interval.  In that sense, it is a target.  And then a target‑coverage probability; how much of the batch had fallen in that interval.
Again, as soon as we start talking statistics, 100Êpercent is not going to be the number there.  It is going to be some target probability that we would like to achieve.
[Slide.]
I put this in here to help you convert back and forth between the notion of a target interval and target‑coverage probability to mean and standard deviation in the batch.  I had trouble trying to come up with versions that print well in black and white, so the main thing in the handout for the committee is the lowest curve is the narrowest so it is the 85/115.  They just go monotonically up to 65/135, the top one.  The widest is 65/135.
The idea in this graph is that, in the batch, the batch has some average, here expressed as a percent of labeled content, some standard deviation.  All combinations of mean and standard deviation that are on or below the curve satisfy 90 percent coverage‑‑that is the target probability‑‑on that target interval.
So, again, we have the target coverage, the target interval.  So everything on or below this curve corresponds to at least 90 percent of the batch falling within 85 and 115 percent of labeled content.
That is to help you translate back and forth between the two.
[Slide.]
If I were starting this from the beginning, and once you say it is a hypothesis test and recognize that it is really in the form of hypothesis test, how might we go about doing it?  I would say the first thing is the regulatory agency should, in fact, specify the hypothesis, what is the claim that they are seeking to have demonstrated.
In this context, what that might look like is a statement in the form here.  The numbers in here are ones that I put in here just to be specific.  Again, I am not trying to advocate particular numbers, just a notion or concept of how to go about it.  But the agency could say, "demonstrates an alpha of no more than 5 percent, a consumer risk of no more than 5 percent, that at least 90 percent of the batch falls within 120 percent of labeled claim."
What they would not do is specify number of tiers.  They would not specify the number of canisters per tier.  That would fall to the sponsor.  Both of those issues are producer‑risk issues and if you are going with this sort of approach, the sponsor should choose what producer risk is acceptable to them.
That would leave the sponsor to say, do they want to do thirty or forty or fifty, whatever risk they would like to take or accept for themselves.
The last comment here is just sort of the statistical side of things, to remember that if you go with more than one tier, and there is certainly nothing special about two, either, that, when thinking about what the false‑positive rate is for the process, that all those tiers have to be taken into account.
[Slide.]
There are two types of language I think that actually comes out of a paper that was published by the Japanese Pharmacopeia.  You have tests for attributes and tests for values.  The FDA, the USP and the CPMP are tests for attributes.  They only look at whether or not the particular sample falls within the target interval and they are not using the actual value.
So a test that is right at the limit of the target interval and a test that is labeled claim are treated identically the same in this sort of approach.
Item 2 is, again, more in my language because when I look at what the CPMP and USP and FDA are doing, I say, "Well, I recognize this.  I do this every day in my job.  This is the standard test to use in designing phase II oncology trials."  It is a very standard test and there is a good literature for it.
[Slide.]
So I am able to go to what is pretty much the standard reference.  I have given it here, a paper by Richard Simon in 1989.  It tells you how to design these sorts of trial.
Let me take you through this so you can see the sort of approach that this would be.  This table is for designs where the agency has had 5 percent consumer risk and the sponsor is choosing to design at 10 percent producer risk.
The first column is the target.  The agency has specified this.  I am giving you a kind of a range of numbers here.  The second column is what the sponsor thinks their batch would actually satisfy.  In the first row, the agency has said to demonstrate that at least 80 percent of the batch falls in the target interval.  The sponsor says, "Well, you know, I have got a really good manufacturing process.  I think 95 percent of my batch falls in the interval, so I can do this study by doing the first tier of 23.  If no more than one are outside the target interval, I am okay.  I stop.  Otherwise, I add another 28 for a total of 51.  If no more than four are outside, I pass."
That would be a two‑tier test for attributes that has the specified statistical properties, the 5 percent alpha and 10 percent user risk.
I have given you a couple of other examples here.  The greater than 110 is really just a software limitation, the particular version of Simon's program I am using doesn't go higher than that.  I sort of figured anything higher than that, you weren't really interested in anyway.  That is high.
The last row is an attempt to reverse‑engineer the current FDA criterion‑‑that is, to go back and say, if we take the two tiers with 10 and 20, what would the hypothesis be in order for that rule to correspond to a 5 percent alpha and a 10 percent producer risk.
To get the alpha down to 5 percent, I have to specify the target‑coverage probability as 60.  So, in effect, the current FDA, or the proposed FDA, content uniformity criterion is really just seeking to show that at least 60 percent of the batch falls in the target interval.
Then, to get the producer risk down to 10, you would have to be saying that the sponsor would have to saying, the sponsor would have to be saying, that at 91Êpercent of the batch is actually in the interval.  You can see, I can't quite match up exactly, but I get similar properties with 22 instead of 30.
[Slide.]
That is the test by attributes.  I mentioned, there is an alternative which is test by value, to actually use the values of the results, not just the dichotomy of whether they fall in the target interval.  The proposals for doing that‑‑there has actually been some work on this by the JP and PhRMA working group. 
These are what are called tolerance intervals.  Tolerance intervals‑‑first of all, they are not confidence intervals, so don't confuse the two.  They are the intervals that you calculate the data and for which you can make a statement like the following; they have some level of confidence, and I picked a number, 95; that the interval‑‑that is, the interval calculated from the data‑‑covers at least some proportion of the batch, and here I picked 90.
The way you could work this in the content‑uniformity situation is, again, we have some numbers here so that the regulatory agency could say, "95; yes," and 90, and specify target interval.  When you calculate the tolerance interval, if that tolerance interval falls in the target interval, you pass.  If it doesn't, you don't pass, and you have got a kind of very simple‑‑it would be very analogous to what is done for oral products for bioequivalence except using tolerance intervals.
[Slide.]
It happens that tolerance intervals come in parametric and non‑parametric forms.  The non‑parametric one, actually, looks a lot like the FDA criterion.  Then, for the parametric, you assume a normal distribution which is, certainly, a testable assumption but probably reasonable here.  I have given you a reference for one of the standard papers on the topic.
I am mentioning this today because, although I can't give you the numbers on it yet, and that is why I said it is a work in progress, it would seem that, by making the parametric approach and going to the tolerance intervals rather than test on attributes, we would be making better use of the data and that should translate into smaller sample sizes for a given level of producer risk.  That is certainly a desirable end.
[Slide.]
So, in summary, a statistical perspective on this would say that the agency should start by specifying their criterion and not the acceptance rules.  The advantages to this seems, to me at least, that the agency would be concentrating on really working on what is an acceptable batch of product.  It, in turn, would give the company more control over the design of their studies and, explicitly, then, over their producer risk.
It is fair to say there is a price to be paid for this which is that it certainly appears that the current standards are sufficiently loose that going this approach would tend to lead to larger sample sizes.  That does seem to be the bane of the statistician, always giving you larger sample sizes than you want.
As I said, until we finish the work on the parametric tolerance intervals, we can't really tell you exactly how much larger that might be, if at all.
Thank you.
DR. LEE:  Thank you very much.
Now, we have heard the background for the discussion.  We do have about thirty minutes for discussion.  I am not sure whether or not the audience is aware that there are two subsets of participants around this table.  There are four members of the subcommittee.  These are Dr. Szefler, Dr. James Li, Dr. Gloria Anderson and myself.
What I would like to do is to invite the members to express their opinion and then we will opinion the discussion around the table.  Perhaps, I think it would be appropriate to devote about fifteen minutes each to the two questions.  
I will read both questions to you.  The first question, concerning content uniformity, is, "Should there be a single content‑uniformity standard for orally inhaled and nasal drug products?"  The second question is, "Should the FDA continue development of the proposed statistical approach to evaluating content uniformity?"
I would like to open the discussion of the first question, should there be a single content‑uniformity standard for OINDPs?
Subcommittee Discussion 
DR. SZEFLER:  Very nice presentations in terms of organization but I wondered if some of the speakers could reflect on the present status of the available products in terms of are these goals that were identified as achievable with our present products and do they lead to, then, kind of mass changes in the products that we have available.
DR. POOCHIKIAN:  I would say that most of the products which have been approved in the last decade have been approved under those criteria that I presented.
DR. LEE:  Walter, anything to add?
DR. HAUCK:  No.
DR. LEE:  Other questions from the subcommittee?
DR. GORE:  Can I ask more of a procedural question?  We will hear more information this afternoon in the 1:30 to 3:30 slot.  Is it our intent to hold this discussion at this time or come back to it later today?
DR. LEE:  I think at various times, we will come back to it but this is only the time for this section of the meeting.
DR. LI:  I would like to ask a question having to do with the comparison of the dose‑uniformity inhaled products compared with orally available products.  If we just shift for a moment to orally delivered medications, what is the range of dose uniformity typically that is expected in that area?
DR. POOCHIKIAN:  For solid oral‑dosage forms, for example, we apply the USP specifications.  If I remember correctly, the concept is the same.  However, the ranges are slightly different in the sense that the first tier has plus‑or‑minus 15 percent if I am not mistaken.  Somebody can correct me.  The second tier allows plus‑or‑minus 25Êpercent.
DR. LI:  It is a little tighter but, really, not all that much tighter.
DR. POOCHIKIAN:  And there is a standard deviation, again, going from memory, 6.something the first tier and 7.8, I think, is the second tier.  There is a standard‑deviation criteria added which we don't have it here.
DR. GORE:  I had a question for Dr. Hauck.  At the end, you indicated when you complete the project you started.  Would you comment a bit more on what you think needs to be done to further this process of developing an approach to drug‑uniformity classification?
DR. HAUCK:  I think a simple answer would be that I need to be able to show you a table for a parametric approach that corresponds to the table I showed you today using the Simon approach.  So right now, today, I can't tell you what a sample size would be.  That is sort of the bottom‑line number, I think, for a lot of people is what size studies would we be talking about for whatever level criterion and levels of risk acceptable.
So I can't give you that today, and that is something that I think you should have before making a final decision on it.
DR. GORE:  Would it be helpful to actually bring forward some data that would reflect the performance of products that have in the market today?  Would it be helpful to look at the model in the context of data from, let's say, currently marketed products?
As we look forward here, we have an array of products on the marketplace today.  But shouldn't we also look forward to the future and a whole new generation of new products, new dosage forms, new delivery systems?
DR. HAUCK:  I think your question actually sort of falls right here on the table.  In some sense, yes; I would like to see‑‑data is very helpful.  There is one question about this.  And it would serve two purposes here.  One is to go forward on the parametric side, there will be some assumptions that are not currently there and we need to be comfortable that that is reasonable.
The second part of it is to get better experience with how the procedure would behave with real products and real data and that would be desirable as well.  The reason i said it sort of falls between Guirag and me is that that also, then, feeds back into what is a sensible target interval.  A sensible target interval is not my problem and I am not qualified to give you what that interval should be, just to help you do the statistics on it.
MR. PAREKH:  The first question I have is with regard to the intra‑container document on uniformity.  We have specifications in the first year which states that the means for each of the beginning, middle and end should be within 85 to 115 percent.
I think this question may be posed both to the statistician we have and also to Dr. Poochikian.  I am not very clear.  When you start with the inter‑batch criteria where you have ten samples that you start out with and you have an overall mean of 85 to 115 percent, it somewhat makes sense to me.
But when you start going and creating a mean for beginning, middle and end, now you drop your N to only 3.  That mean just doesn't make sense to me.  I think, in my opinion, the mean in the case of intra should be only restricted to the overall which is after you complete tierÊ2, not at tier 1, because the N is just too small to make any meaningful data and related to the quality of the product.
So that is one question, or the comment I have.  If you would like to comment on that one.
The other thing I would like to comment on overall with respect to what is the position of the OTC industry, which your non‑prescription portion of the organization is in the pharmaceutical arena, and this relates also to the question which has been posed here.  One of the questions that has been posed here is that should the FDA consider the development of a statistical approach.
I think it makes sense in some cases but, from the consumer side of the business, these products are in the making for many, many years, in some instances, maybe even thirty years or more.  To apply the criteria now, of course, forces the industry to go and look at the product but, ultimately, shouldn't the product quality also be looked at from the safety perspective?
Is there a need for restricting the industry to a place where industry ceases to do the business?  It is ultimately affecting the consumer.
So those are the two comments.
DR. LEE:  Are you expecting a response?
DR. POOCHIKIAN:  With regard to the first question concerning the mean beginning, middle and end, there is a good reason for that.  You can always increase the sample size.  I don't think the agency will object to that.  So I have to make that one point clear.
Second, the reason we did that is because we want to avoid sigmoidal curves of units which starts, for example, very high and, by the time of the 200 actuation, it looses 40 percent.  I am talking here, on the average.  The average of 30 doses, for example, loses 40 percent.  So if you take the individual dose, I don't know what we are measuring.
So, in order to avoid that approach, we wanted to establish that the patient's individual needs and that the patient is getting the prescribed dose rather than 80Êpercent lower than what is the LCEs on an individual basis, because this is the mean of 30 doses I am talking about.  It lost 37 percent.
So we want to avoid those situations like that.  As to the second comment, with regard to the oral products, as I said earlier, most of the NDAs which have been approved in the last decade fall into the category that I just presented.
There might be a couple of those in the case of CFC products which will be phased out anyway within the next several years.  So it is not an issue.  It is possible, also, that the old one can be grandfathered as long as they are on the market.  That can be handled at the agency level with a policy.
But we are talking about where we proceed scientific from now on.
DR. DALBY:  Let me apologize for my lack of statistical prowess.  Neither one of your approaches seems to pay any attention to the size of a batch, so if a large batch is approved compared to a small batch, does the producer or the consumer incur any extra risk if there is any, in both approaches?
DR. HAUCK:  No.  Really, both approaches are thinking of the batch as being just much, much larger than the sample size.  Once you are out there, whether it is a thousand times larger or ten‑thousand times larger, really, doesn't enter into it.
If you were doing batches of size 40 and sampling 10 to 30, then it would be an issue for the producer, clearly.  The small end of it would change things but not the large end of it.
DR. DALBY:  What about from your perspective?
DR. POOCHIKIAN:  No.  We do not, at least as it is being practiced now, take into consideration the batch size and the sample size relationship.  But that is a very valid point because the batch sizes for some of these products varies significantly from product to product.
DR. DALBY:  I guess I have a follow‑up question for Walter which is, is there any disadvantage to the patient of the producer deciding that they will accept an enormous risk?
DR. HAUCK:  As long as you set it up so the agency is specifying, this is what is deemed an acceptable batch, and then the agency specifies the allowable consumer risk; that is, the chance that something that doesn't actually satisfy those criteria gets out the door.
Then, what the producer takes as their risk doesn't really affect the consumer in the safety sense.  It might be affecting them in some cost‑of‑product sense if not enough batches are getting out the door.  Producer risk has that impact but, in terms of safety, it shouldn't.
DR. DALBY:  So there would really be no need, in this approach, for the agency to set guidelines.
DR. HAUCK:  Just think‑‑I keep coming back to the clinical‑trial context which, in a sense‑‑not that it is directly applicable but the sort of structure applies, which is companies designing a pivotal trial, they decide the power they want.  If they don't pass, they don't file.  It is their problem in that sense.
DR. DALBY:  Thank you.
DR. LEE:  Any questions from this side of the table?
DR. HARRISON:  I also agree that the FDA should continue to develop a statistical approach.  It makes a lot of sense to me, to let the producer determine its own risk, pick the numbers.  I like that so I would certainly like to see that developed.
I would like to some real datasets evaluated, though, to really look at the specificity and get a better feeling for what can be done.
DR. LEE:  Anyone else?
DR. LAGANIERE:  This is a question for Dr. Hauck.  You are suggesting an acceptance range from 80 to 120 percent.
DR. HAUCK:  No; I am not suggesting any particular number, there.
DR. LAGANIERE:  I wonder if you can expand a little bit more about this number.  In the context of oral‑drug administration, concentration is kind of related to effect.  What does it mean in the context of in vivo?
DR. HAUCK:  The best answer that I can give is that it is probably not a question for me.  As to what is an acceptable target interval, I can probably help you design a study to think about that, maybe.  But we are talking about data that may or may not currently exist in the literature, I guess.  So we are dealing with surrogate endpoints, and all of this is a surrogate in some sense for eventual clinical application.
So you and Guirag would need to sit down and have a dialogue as to what that interval should be so as to properly protect the patient without causing undue burden on the company.
I tried to be clear in the presentation that I was putting numbers in there to be specific.  But I do not choose to defend any particular number as either too large or too small.  I can't do that.  That is the best I can do, I think.  Does that answer the question?
DR. AHRENS:  Richard Ahrens, University of Iowa.  Dr. Hauck, Iwanted to clarify I think what I heard a bit ago and expand a bit.  I think your approach assumes normality of the distribution of content uniformity, does it not?
DR. HAUCK:  The parametric‑tolerance interval approach, what I mentioned at the very end, would assume normality in the canister‑to‑canister values.  The approach that I gave you, the table, is based on the method assigned and makes not assumption of normality.  It is just, they are in the interval, they are outside the interval.
So there are the two choices there.  If normality turns out to be an untenable assumption in this context, then you can fall back to the test by attributes.
DR. AHRENS:  Is there any evidence as to whether uniformity tends to be normally distributed or not?
DR. HAUCK:  I haven't seen enough‑‑that is one of the things I would like to see or suggest more data.  I would like to see more data to address that.  From a theoretical perspective, it strikes me as exactly a situation where normality ought to be sensible.  It is a thing called the central‑limit theorem that says that if your error is built up of lots of little components, what you end up at the end is something that is going to look approximately normal.
So, from that perspective, that seems to fit a manufacturing situation that would make sense and normality would be plausible.  What I hear from the agency is, when I looked at the data, it seems plausible.  I think the committee can ask to see some results on that at some point if you want.
DR. LEE:  To show you how flexible the schedule is, I am advised we should take a break at this point because the sound system does not appear to be functioning very well.  Let us do that.  We will convene here about 10Êo'clock.
[Break.]
DR. LEE:  We are going to start with Dr. Yi Tsong's presentation and then we will come back to discussion.  If there is a statistical question, we will catch him before he runs off to the airport.
In Vitro BA and BE Testing
DR. TSONG:  Good morning.
[Slide.]
I want to apologize because I have an engagement in Lubbock, Texas this afternoon so after this presentation, I have to run and I cannot stay here for the discussion.  But, Dr. Walter Hauck can probably help the committee out on the questions.
[Slide.]
What I will try to discuss today is the comparative measures for the in vitro profile comparison of the profile measurements.  The goal of this talk is that I will try to present the work‑in‑progress for equivalence approach for the profile measure from the in vitro studies and apply them as the work group came out to some simulated data.
Essentially, the approach we are looking into is pretty straightforward in the concept of how to measure the difference between two profiles.  But it is also difficult in the sense that there is no statistical distribution available to be able to, for example, use a t‑test, normal distribution, existing distributions to apply to this problem.
That leads to the problem as to, also, how we determine the cutoff point for the equivalence limit.  That is why we have to do a lot of simulation to see where we make the cutoff point to make us feel comfortable with where they are supposed to be.
At this stage, the work is still in progress and we would like to have the committee take a look and think about it and give us feedback to see how we can modify it or whether this is something you think we should keep on working on.
[Slide.]
First, I want to describe the profile.  The profile we are talking about here is the particle‑size distribution in cascade‑impactor equipment.  As this equipment measure, actually we separate what the distribution is in this cascade‑impactor equipment.  We have unit dose and we have distribution of the unit dose among the different states of this cascade impactor.
The unit dose, we put this in as the non‑profile comparison.  The distribution part, we put in as the profile comparison.  So, in this approach here, in the profile‑comparison approach, we don't worry about whether the unit dose is the same or not.  Everything is standardized to 100Êpercent as a total.  And then we just see how they distribute, whether the attached product and the reference product is the same.  Unit dose is a different test to be satisfied.
Equivalence means that we needed to have the profile to be the similar one and also we needed to take into consideration there, because we have variability in this kind of data, versus the variability between the different life stages.  Also, we have variability within the lot which is between the canister of the same lot.  Then we have variability between lots.
At the current stage, we put all this variability together.  We didn't specifically separate that.  There is some difficulty separating them because of the limitation of sample size we have.  So, at the current stage, we put all this variability all together.
Number three is that we want to consider the profile measurement in the comparative sense.  That means we wanted to look at the test‑to‑reference profile distance compared to the reference‑to‑reference profile distance.  Sometimes people call that reference‑reference variation.  Here, it is also coming from the distance measurement of the two profiles.
[Slide.]
I think I have pretty much described this part, some of the non‑profile observations and the profile observations.  But there are some characteristics of these two types of observation.
Non‑profile observation means that each canister gives only one observation which is, in this case, a unit dose.  Profile observation means each canister gives one observation of particle‑size distribution through the various stages of the cascading factor.
It turns out to be that the non‑profile becomes only a univariate but a profile distribution, if you have ten stages, that means you have ten category variables, ten numbers to deal with.  This becomes a multivariate situation.  Then we have a non‑profile situation which is the univariate, it is easy to talk about mean and variability.  In the profile situation, the mean and the variability is difficult to obtain explicitly.
So we are going to talk about average profile or standard‑deviation profile, individual profile to the mean profile; we have some difficulty with that issue.
[Slide.]
Also we can see the approach in the sense of aggregated criteria.  That means, we put the profile difference and the variability into one simple criteria.  We don't want to separate them.  We have to do an additional test, in that sense.
Profile distance at canister level, that is what we look at first.  Suppose I have one canister from the test and maybe one canister from the reference or maybe two canisters from the reference.  We look at how we look the distance between the canister profile at this level.  Then we look at the ratio of the profile distance at the canister level.  That means, we measure the distance between the test reference to the ratio between the reference at each canister.
Then we are talking about now we have reality.  We have more than one canister.  Then we look at how we came from the individual canisters to be able to put together into mean of those ratios and how to calculate the confidence interval of that mean ratio.
From there, we try to set criteria to set how to satisfy the requirement.
[Slide.]
The ratio of profile distance at canister level is really, as I describe, the test and the reference canister distance to the profile distance between two reference canisters.  The mean is just the expected value of those ratios of many of those canisters, in that sense.
Here, we don't really have to stick with this notation, but it is probably just easier to see later because we are going to refer to those notations.  First, at each stage, we have the proportion of the particle distributed in that stage.  So we have the proportion which is pT, standing for the test product.  pR stands for the reference product; s means this stage, and the ds of the tested reference and the ds of the reference‑reference.  This means the difference between the test and the reference at this particular stage.  We are looking to the distance between the reference‑reference at each of the stages.
Then we have cds.  I use this here for the test‑reference and reference‑reference.  That means the cumulative of the distance between the test and the reference up to that s stage.  So it is just the difference at each of the stages together up to the s stage.  Then I use the notation cds, which means cumultative.
es, for tested reference, is simply just the average of the two test and the reference at this particular stage of those distribution proportions.
[Slide.]
If we just imagine that the cascade distribution comes out with the test stage, we have test and reference product and each one of has a proportion at each of the stages, then, how do we measure the distance between the two profiles?
First, intuitively, you can see that the first thing we want to look at is the difference between the test and the reference at each of the stages.  That will give us what I describe as the d of the test reference at the s stage.  Then we try to put all of them together to indicate that this is the measurement for the profile.
There are many different ways we can put them together.  One is just to take the difference, take the absolute value, add them together, average them.  That is what we call the mean absolute distance of the two profiles. And we divide by the total number of stages.
The other one is just to add up all these distances together into one.  If we want to emphasize‑‑if there is a large difference, I don't want to just add up.  I want to enhance the difference.  So we make the difference between the two at this particular stage and square it, and a large difference becomes larger, enlarged into a composite index of this distance.
So that is when we come out with the mean squared distance.  That means to take the distance and square it together.  Then you add them together, divided by how many stages.  That will give us the mean square of the distance.
As you are familiar with dissolution tests, there is the f2 factor.  The f2 factor is really a transformation of the mean score of the distance into a particular formula so we have standardized the f2 between 0 and 100.  I think, in dissolution, you know that we use 50 as the cutoff point for f2 factors passing or failure.
Chi square is really taking us a weighted mean‑square distance because, as I mentioned, you take the difference between the test and reference at each individual stage, square it.  Instead of just squaring it, we also divide it by the average of the test and the reference, so this is a weight which is 1 over this average of the two.
What this indicates is that, for those stages which have a large proportion deposit to, a small difference probably isn't going to be of much importance.  But those which are not supposed to have a large proportion, if there is a large difference, you want to enhance it by using the weight.
So this comes out as the chi‑square distance.  We can also put additional weight to it if I am more interested in a particular stage than some other stage that you are less interested in.  At this stage, we didn't put any particular weight in that sense.
So, in a sense, chi square is a weighted mean of the distance, but statistically it has‑‑but probably, even though it doesn't necessarily apply, those probably do not necessarily apply to our data.
Also, we have been thinking about, in the beginning, an intuitive way is just to, at each stage, you do a t‑test.  Why don't we just do that.  Then we have to do ten different tests and to study all ten tests to satisfy the equivalence.
The second problem is that, because the proportion‑‑supposedly, if the proportion supposedly adds up to 100 percent, so there is a count trend of each one of them to be a total that adds up to 100 percent.  An individual comparison doesn't have this feature and also we have this individual stage value as also correlated to each other.  It is not totally independent.
[Slide.]
Here I give you an example of how we calculate this.  Here is the example of supposing I have only one canister on the test and I have two reference canisters there.  Those are the proportions distributed into all the different stages including the standard and the throat.  I graded future stages because I think later we will jump to the future.
So, actually, when we do the work, I show the example is really up to stage No. 7.  It really doesn't matter that much.
[Slide.]
Graphically, what I want to show is that this is the difference between the test and the reference.  As you see, the first bar is the test canister and the next two are the two reference.  I think one of the questions we have been asking about is should we separate standard and throat from the other stages because maybe there is not‑‑we are interested in that, too, but we don't have a solution to that as yet.
[Slide.]
Here is an example of the calculation.  As you see, the first is the table you have seen already.  The second block is that we first took the two references to average them together.  I have averaged the proportion of all the stages.  Then I have the distance between the test and the reference at individual stages which is really the difference between the test and the average of the two references.
Then I have the E which is the mean of the two references, and then we calculate the difference square divided by the average which comes out for each individual stage that we have these components.  Chi square is just adding up all these components into one value.  It becomes 7.25.  This is the chi square which extends between the test and the reference.
As I mentioned, this one we don't put a particular weight except the weight by using the reverse average of the two references.  So we can do the same for the two reference products and then we have a chi square for the distance of the two references.
Then we calculate the ratio, which 18.83, for the chi‑square ratio.  As you can see, the dominator of these two products are very much the same, but the numerator can go to as small as zero.  So we know that the chi‑square ratio should be the smaller the better, in that sense.  But it could be as large as possible when they are very different.
[Slide.]
I also give an example to calculate the mean absolute difference and f2 and those ratios.  Here, the same calculation but instead of using‑‑oh; here, we are using the mean absolute difference, just those differences added together so we get 7.288.  If I use f2, I use a transformation formula to get to this f2 value for the test and the reference distance.
The same way, I can recalculate those for the reference and f2 for the reference‑reference.  Then we come out with the MAD ratio which is 6.605.  We have and f2 ratio of 0.590.  
MAD, which we know is just adding up all the differences.  So, the smaller the better.  For this one, we know there is a maximum amount which cannot be more than 100 because the maximum difference, theoretically, is 100.  f2, 50 percent is no more applicable to this ratio because it is not f2.  It is the ratio of the 2f2.  
So, here, we know that f2, itself, the larger the better.  We know that.  So the f2 ratio, we still need to come out the larger the better.  The value can be between 0 and it could be as big as infinity.  But, mostly, it is going to be between 0 and 1.
[Slide.]
What I have shown you is just a one‑canister situation.  Now, suppose I have a bunch of canisters of test and reference.  To be able to calculate those differences at the canister stage, what we need to do is try to match them together into a triplet of one test and two references.
So we have to do a random matching to be able to create those and calculate those ratios.  So let's take a look at this.  Supposed we look at three lots with ten canisters per lot.  Why do we come up with 30?  I think we had some discussion and it seems this is something workable.  It doesn't have to be necessarily the final answer.
So that is why you probably Dr. Walter Hauck uses 30.  I use 30.  We are consistent, at least in that sense.  If we look at this triplet combination of the test and reference‑reference, here we need two references to be different.  Otherwise, we may have the denominator to be zero.
In that sense, we have a combination of 33 N‑‑that is the total number of canisters of the test‑‑factor times the combination of the three chosen out of the 3N, all this combination, distinct triplets, combination.  This number could be very large.
We don't necessarily need to have all of them.  So, if the triplet is very large, we take a random sample of this continuous triplet to calculate those ratios.  If it is very large, the total combination number is very large, we just took a random sample without replacement.
If it is small, we probably want to take it with replacement to be able to do this work.
[Slide.]
So we have this sample of the combinations.  For each of those samples, at the current stage, we are sort of satisfied to use 30 because the original sample size is 30 triplets to make one random‑sample sample.
Then, after we have each one of them, each canister, we can calculate the ratio of the chi square or the ratio of the f2 or the ratio of the MAD.  Then we calculate the mean out of the 30 triplets we sampled from and have the average of those which is the sample mean of the 30 triplets.
So we have a sample mean.  To be able to have the confidence interval, we know we don't have distribution.  We don't have parametric assumption and we don't take the asymptotic distribution.  So what we should do is going to repeat the steps in this 30 triplet sampling for N times and come out with the distribution of those means, and take the lower‑upper 95 percent or 5 percent to be the confidence limit of those means.  That is when we come out with the confidence interval of the mean in that sense because, totally, we don't use any distribution to deal with the parameters here.
So, what comparison we need to do for bioequivalence is that we look at the upper limit.  For chi square, we know that the smaller, the better.  So we want to control against the largest mean difference you may have.  So we use the upper limit of those confidence limits compared to whatever we prespecify the limit.
If it is smaller than the limit, we are going to have equivalence.  Now, here, I am puting that the limit is 7.66.
[Slide.]
When we come to the next one, we tried to determine the limit based on the simulation.  Here we have one try and then we try different combinations, and 7.66 comes out to be a reasonable cutoff point.  Here, I want to show you how we do that.
First, we wanted to simulate 1000 per product, we come out with ten lots at 100 canisters per lot.  Then we do the simulation using the real mean and the percentage of CV defined in the simulation.  
Here is an example of this.  For the no‑variation one, the CV is 20 percent and 10 percent as low in stageÊ1, up to 20 percent.  Actually, I think I should have reversed these two.  The upper one is high variability.  The lower one here is the low variability.
So we simulated this out and used this one to figure out where the cutoff point maybe looks like.
[Slide.]
So, in order to be comparable with what we proposed, we randomly select three lots from the ten lots and we simulate it out and randomly select ten canisters per lot from the 100 canisters we propose.  So we have those combinations of a distinct triplet.  And then we repeated this.  We sampled 3/30, as we mentioned before, and we repeated this 100 times.
So we calculate the sample mean as we did before and we will be able to calculate the confidence interval by the percentile from this one.  That is what we have on the next page, we have the simulation.
[Slide.]
The first one is that we have the tested reference to have the simulated‑‑and we have no difference.  But with the test product has high variation between and within, low variation, versus the reference has high variation and low variation.  We also did a simulation with 10 percent difference, the simulation between the test and reference and we have a test product high variation, low variation, versus the reference product, high and low variation.
It comes out that we look at the 90th percentile, which comes out with the value 6.66.  As you see, the simulation comes out here.  This happens when the test product has large variation and the reference product has small variation.  Also, we have a difference given at each of the stages. 
So this is the point.  Otherwise, those two ratios are quite similar.  So we have this one can kind of stand out from the rest.  That is sort of satisfactory to us.
[Slide.]
We also tried that with a similar one with the f2 ratio which comes out in the case here.  We had difficulty to separate them one from the other.  So this comes out as smaller, but there is really not that much difference, to tell the difference, in the f2 ratio.
[Slide.]
Then we also simulated for the mean absolute difference which comes out similar.  This one has a little bit larger number but still not as clearly as we show in the chi square.  There is a large distinction between the large variation versus the small variation of the reference with the mean difference.
So that is what we propose, tentatively propose, to use that point value as the equivalence limit.
[Slide.]
So I can summarize the points that I presented here.  We have briefly summarized an equivalence criterion proposed for in vitro profile measures and we propose a criterion for paired test and reference canisters.  We propose criterion which considers distribution variations as well as distribution differences.
We propose criterion which penalize increased distribution variability and rewards reduced variability.  This work is still in progress with some further considerations we have taken.
I think I can stop here.
DR. LEE:  Thank you very much.
DR. TSONG:  If there are any questions, you can leave them to the FDA members.  They certainly will ask me and you can ask Dr. Walter Hauck.  He works closely with me.  Lots of questions he will be probably able to answer.
Thank you very much.
DR. LEE:  Okay; you can go now.
I understand that the audience was not able to hear what was said in the beginning.  Can you hear better now?  Good.  But we don't have time to go back to the beginning.
I would like to go back to the agenda. 
Subcommittee Discussion (Continued)
DR. LEE:  We were addressing a very important question on content uniformity.  I would like to pose the questions to everyone around the table and your feeling about whether this should be a single content‑uniformity standard for all OINDPs.
I would like to go in order.  Let's start with the  highly distinguished colleague on my side here.
DR. MacGREGOR:  As far as the single content‑uniformity, I have seen a lot of simulations and a lot of statistical descriptions.  However, I haven't seen any data.  What has been promised is that there is a possibility to gain some data for this panel to evaluate.
So I would prefer, rather than answer this question now, to see the data.  It is my understanding, from looking at all the documents we have been given‑‑we have got a pile about a foot high, here‑‑that there will be data forthcoming both today and over the next couple of months.
So, in my evaluation, I do not see how we can answer this question today.  I think we need to see data because a single content uniformity for all the products that are out there sounds like a very idealistic point of view.
Now, if all the data comes in and it does point to a single content‑uniformity guideline, then that would be the greatest thing in the world.  We would all be on the same page.  However, it is my gut feeling, having worked on many of these projects, that every drug is different.  Otherwise, we would all be selling the same drug for the same indication.
So I would prefer to table this question until the end of the summer when everyone says that they will have data.  I realize that we are under a deadline to try to meet an advisory committee that is in early fall but I would suggest that we reconvene sometime in the future when we have data rather than‑‑
DR. ANDERSON:  This is my first meeting and I certainly am not prepared to make any decision one way or the other.  I made some notes as I listened to the discussion this morning and, as a teacher who is not an elementary‑school teacher, these questions are probably very elementary.
These are things that I need to understand in order to make an intelligent decision about this question.  I have here, one, presumably, and this is in answer to the question should there be a single content‑uniformity standard, my statement here is presumably there is not one now.  That sounds like one of the answers to my organic‑chemistry questions.
Two, and why I ask this question, what is the consequence of not having a single content standard.  I would like some information that would help me answer these questions or at least get more information on them.  Having not attended the previous meetings, I would rather wait until I have information in these areas.
DR. BAASKE:  The committee is dealing with two distinctly different types of devices.  We are talking about a metered‑dose inhaler or a dry‑powder inhaler and a nasal spray.  Without understanding the capability of the device manufacturers, it is hard to draw, across different devices, one standard.
So I would agree that we need to see data before you could make that decision.
DR. AHRENS:  I would agree with the comments that were just made and probably not have a lot to add to that.  It is very difficult to answer the question in absence of essentially data as to what is out there with currently existing products.
DR. LANGANIERE:  I would like to see more data to put in the proper perspective of variability associated with certain products versus other types of products.
DR. DALBY:  I am certainly willing to look at the data but I do think that ultimately what matters is a consistent dose to the patient and it doesn't intuitively make a lot of sense to me to tightly control that at a device level if there are other enormous sources of variability.
So unless the data speaks very consistently to one standardized set of criteria, I am more inclined to say that it should be looked at on a product‑by‑product and drug‑by‑drug basis.
DR. GORE:  I am very much in agreement with the recommendation that we look at more data.  I think we also need to look more at the consequences of how the drug‑content uniformity actually plays out.  For example, one consideration that we didn't have to discuss and factor into our consideration is that, in development and also in manufacturing, batches are routinely placed on stability for up to two years.
Just a quick back of the envelope says that the minimum number of canisters is somewhere around 400. Depending on how many go into stage 2, you could get up to well over a thousand canisters for those batches.  So I think it is a more complicated picture and we just to need some more time to really understand it.
MR. PAREKH:  I would pretty much echo what Dr. Gore just mentioned in terms of I think we need to look at the practical implication of these things.  Without data in front, I can't seem to be able to comment on, especially because these devices are so different from each other, and how they are therapeutically used.
So I would like to see more data but, also, strong consideration for what is going to be its practical implication in the end and how industry deals with it, in general.
DR. HAUCK:  I guess from a conceptual perspective, the notion, as it is sometimes called, of "one size fits all," doesn't make any sense to me.  It hasn't in a lot of these things.  So that takes you down a path towards having different criteria for different products.
The problem there, there is a downside to that which is you don't want an absolutely different criterion for every single product either.  Some of the stuff, even what Xi Tsong was presenting, was working toward a notion that using the properties of the reference product would essentially help determine criterion.  That sort of approach would be one possibility to consider.
DR. HARRISON:  I also agree with Walter that the "one size fits all" concept doesn't seem to really make a lot of sense here.  What you need is a dataset.  It does seem to be an opportunity to have such a dataset available by the end of the summer.  I would also like to see us waiting until that point in time to make a more rational position.
DR. DERENDORF:  I basically agree with what was said before.  I want to make an additional comment, however, and that is that we are looking at a three‑level evaluation here or assessment, content uniformity, the in vitro and then the in vivo assessment.  I think we kind of look at content uniformity in isolation.  It is tied in with the other two and we need to make sure that they all match.  We cannot have more stringent, let's say, in vivo requirements than we have in content uniformity.
So they all interact with each other and need to be put in perspective and we need to identify what is rate limiting.  I agree with what Richard said in that sense.
DR. SZEFLER:  I think everybody is trying to avoid discussing a difficult question, but, as a clinician, we deal with variability in response among patients.  Our assumption always is that the product is acceptable and so we deal with thinking about adherence to the medication and biologic response as the other variables.
So, to delay kind of a movement towards standardization and characterization would be unfair to the clinician and to the public.  I think we have to move in that direction.  Having said that, I would like to know if there is a problem and is there something worth fixing or is it something that we are moving to.
I guess the impression that we are trying to move towards standards in order to characterize a product in order to get some assessment on bioequivalence.  So I would say we need to move in that direction while assembling the data, but making it clear we didn't start out that way by giving examples of problems.
That would force us to move there even more quickly.  If we are dealing with products, and having done a study recently where we had a recall on a product, it doesn't settle well when you are investing money into doing studies and then you find out that the product doesn't meet standards for some reason.
So I would say we need to move there.  We need to see some examples where there are problems so that we could set the goal posts because I think the problem is not trying to set the standard, it is trying to set the goal posts in terms of what is acceptable and what is not.
DR. BEHL:  First of all, I also agree with my other colleagues that the better off we are in drawing conclusions and making a better guidance.  But as a sort of more fundamental issue here, the question was should there be a single content‑uniformity standard for all nasal and inhaled products.
If you go back to Dr. Poochikian's slide No. 5, he defines them as eight different kinds of products.  Then, when we go back to his eighth slide, we only have values or the specifications given for NDI and DPI.  
Dr. Poochikian, are you saying that these also apply to all of the six products, the specifications on your eighth slide?
DR. POOCHIKIAN:  That was only an example of NDI and DPI, but, currently, those are being applied also to nasal preparations.
DR. BEHL:  The same specs apply then?  The same specs will apply to all of them?
DR. POOCHIKIAN:  Correct.  Yes‑‑where are on the market, for example.  Some of those still are not on the market, as you know.
DR. BEHL:  If you do that, then the question comes up, in multidose container versus a container that contains less than three doses, your beginning, middle and the end estimate of the dose delivered, the question comes how would we address the issue of a DCU for a bidose or a unidose nasal drug product.
We do have a nasal drug product on the market, a unidose.  Is the guidance excluding those special cases at this time, unidose, bidose, nasal drug products?
DR. POOCHIKIAN:  That was not the intent of the guidance.
DR. BEHL:  So if somebody is developing a bidose or a unidose nasal solution or suspension drug product, then that company is not bound by this guidance?
DR. POOCHIKIAN:  In that case, through the container lot, that applies only to reservoir‑approach drug products.  If you have a single dose, you have a single dose; that's it.
DR. LEE:  May I request that you focus your questions to the central question.
DR. BEHL:  The central question was about the number of units in the beginning, middle and the end.  The next line of comment is that are we mixing things there in terms of validation versus the Q/C test required to release a batch.
Some of these issues, doing first tier, second tier, on the actual dose delivered, either as a whole, or beginning, middle and end, I believe these things are normally done as part of a product process where we have to do a second validation package to show that the dose delivered, or the actual device used, is, in fact, valid and can be used in an efficacious manner precisely each time you use it.
That, to me, sounds like a validation issue.  Beyond that, we shouldn't have very few Q/C tests type of testing procedures.  There is no sense in doing a validation type of evaluation on each batch that has to be released for commerce.
DR. LEE:  So what is your position about the central question?
DR. BEHL:  The central question is that these are too restrictive evaluation tests for each batch released because they are more or less of a validation issue than a Q/C release issue.
DR. LI:  I want to just concentrate my remarks on the question of whether there should be a single standard for all drug products with the emphasis on the single.  I can see that, theoretically and, even in some respects, practically, there may be some advantages to moving in that direction in so far as there would be a single standard or single set of parameters that would apply to all these products.
Whether that is, in fact, achievable or appropriate, I think we have yet to determine.  I think that, as some of these models and some of the data is accumulated, it will become more clear whether or not the single standard is an achievable goal.
For example, if we assume a statistical approach, does it make sense to have a false‑positive rate of 5Êpercent for one product, 6 percent for another and 10Êpercent for yet another.  As we get more information and look at new products and existing products and how they fit into these standards, I think it will become clear.
Perhaps we could answer those questions product‑by‑product.  So there may be some advantages to having a single standard, but I think as we move toward actually getting practical information, it may turn out that there are some severe limitations to that.
If this is the case, we will limit the down side to having multiple standards based on different classes of products, whether it is nasal products, orally inhaled products, different drug moieties like corticosteroids as one group and another drug product as another, maybe turn out to be the best approach.
I guess I would say a single standard is a reasonable goal and, practically speaking, I think it will become clear whether or not that is achievable, and multiple standards for multiple products adds some complexity.  But I think the downside is limited there.
DR. SHUM:  Without the data in front, and without the chance to review all the materials, looking at the two questions, to me, it will be difficult for me to make a decision today to answer question No. 1.  We obviously need to review what is out there.
And, I am leaning more to saying yes to question No. 2, which is saying that we should look at other approaches.  I also want to remind my colleagues here that, as we are looking into the database, we also can see that there might be other approaches that we should consider.  Obviously, we had a presentation from Walter about his statistical approach but I also recall that there was a statistical approach presented by my distinct colleague, Dr. Mike Rebe, in the June workshop last year.
So there are other approaches.  Of course, there are also other guidelines, ICH Q6A, ICA Q4, all these approaches that we should also consider before we come to a position.
DR. LEE:  So the consensus around the table appears to be that we need more data.  I just want to know whether or not anybody knows what kind of data they are looking for.  It sounds like a question that I pose to my students; we need more data.
DR. AHRENS:  It seems to me that the data needs to address two questions, one probably easier to get at than the other.  One is, what is history.  That is the products that are currently out there, what kind of variation in content uniformity is there.  That would clearly be a floor to what a new product would have to match.
You would clearly not want something that was worse than is already out there.  But as I think Dr. Poochikian mentioned earlier, essentially the past is prologue.  That doesn't mean, with current technology, it isn't possible to do better than that.
The second question which I think is going to be much harder to get data to answer is what is reasonably achievable in terms of improving on what is already out there.  I don't know quite how to address that other than from companies who have tried and what kind of success they have had in improving on content‑uniformity variability over history.
DR. GORE:  I think probably most of us have not had a chance to speed‑read the entire package, but there is a statement and a proposal in the package from the ITFG/IPAC collaboration on Page 8 which actually makes a proposal for data and data collection.
DR. BEHL:  I believe if you look at the QC failure rates of different batches of different kinds of products, one could learn from there as to what the problem is and a resolution or a better method for the future.
Second, the justification for asking for all of these tests for each batch is produced; that, to me, is a central question because is that really necessary?  Is the cost justified?  In regard to the first point, has there been a Q/C program from various batches tested so far?
DR. LEE:  I think that we have heard the opinions around the table about the virtue of a single content‑uniformity standard.  There is consensus that we need more data and I think we have a vague idea of what the data is.
I would also like to acknowledge comments made by Dr. Jim Li about the need for some kind of guidepost and variations thereof.
At this point, I would like to move quickly to the second question which has been covered kind of in tandem with the first one; "Should the FDA continue development of the proposed statistical approach?"
Very quickly, Walter?
DR. HAUCK:  I would say yes and get to say "Off with his head" later, if you don't like it. [Laughter.]
DR. HARRISON:  I would also agree.  We should definitely continue and I would like to see some datasets.
DR. DERENDORF:  I agree.
DR. BEHL:  I also agree but, again, one more time, I would like to repeat that we should look at the justification that we asked in question‑‑because if that justification is not there, then the question can become semi‑moot.
DR. LI:  I like the statistical approach in part because it forces the agency or committees or even clinicians to concentrate on what the important parameters are.  Rather than number of canisters being tested, to concentrate on the guidelines for parameters, for uniformity and variability.  To me, that has more clinical significance in terms of protecting patients from out‑of‑spec products.
DR. LEE:  In the interest of time, let me ask the rest of the table, is there any difference of opinion?
DR. SHUM:  Mr. Chairman, I just want to refer to that question.  To me, that question is a broad question.  It is not only applied to what Dr. Hauck has presented this morning.  I, again, want to urge this committee, when we look at statistical approaches, we should consider there might be other approaches out there that we should consider.
DR. DALBY:  I would say, I think it is quite important to also make sure we educate people about what the statistical approach means because, to me, nine out of ten passing units sounds much better than a 60 percent probability that the units fall within an acceptable spec.  They are both based on the same data so I think it is important not to frighten people with that information.
MR. PAREKH:  The only comment I would like to make is that I think the statistical approach makes sense when you are developing the products.  To the extent that level of testing that is required to comply with that kind of process controlling the quality of the product, it is very impractical.
So I agree with the statistical approach.  How far we can take it, I am not sure at this stage.
DR. LEE:  Anybody else?  Guirag, do you have enough information to work on?
DR. POOCHIKIAN:  Unless there are specific questions that I can enlighten about.
DR. LEE:  Any questions?  If not, I think that closes the first session of this meeting.  We are not done yet, because I would like to move on to the next session.  The next session is on bioavailability and bioequivalence.  	Dr. Adams, are you ready?
Bioavailability (BA) and Bioequivalence (BE)
Current FDA BA/BE Background and Issues 
DR. ADAMS:  Good morning, ladies and gentlemen.
[Slide.]
My topic this morning is orally inhaled and nasal drug products for local action, current FDA BA/Be background and issues.
Before starting, I would like to thank the members of the subcommittee and invited guests for participating in this meeting and also to recognize the amount of work that has been done by Nancy Chamberlin and the advisors and consultant staff and also from David Morely and Jim Corey in OPS.  This is represented a lot of work in putting this program together.
The talk on BA/BE background and issues, the issues have already been delineated by Dr. Eric Sheinin in the BA/BE questions which he has gone over earlier this morning, so I will talk about background here.  
[Slide.]
I would like to start with showing you the Technology Committee, the OINDP Technology Committee, that has been involved in developing two guidances, primarily the nasal BA/BE guidance which has been on the FDA's Internet site since June of last year, and indicate that there are seven working groups that have been involved in that.
Many of the individuals listed there are in the room today.
[Slide.]
The two guidances at issue are both product‑quality guidances.  One is the BA/BE Studies for Nasal Aerosols and Nasal Sprays for Local Action.  The second one, which is in preparation, is a BA/BE Studies for Orally Inhaled MDIs and DPIs and Inhalation Solutions, also for Local Action.
[Slide.]
These draft guidances cover BA and BE.  But, on the BA/BE side, they cover only product quality BA which refers to release of drug from the drug product, but, rather, it does not cover additional bioavailability studies which are required by the divisions; that is pharmacokinetic and bio studies in addition to those studies indicated in these guidances.
Of course, bioequivalence is a product‑quality issue only.  Furthermore, these guidances are strictly limited to locally acting drug products.
[Slide.]
We know that, according to the CFT, the approaches to measure BA and establish BE are pharmacokinetic, pharmacodynamic and clinical, in that order, preferably pharmacokinetic.  If that is not appropriate, then pharmacodynamic studies.  If they are not appropriate, then clinical studies.
In addition, BA and BE may be established based upon in vitro or in vitro plus in vivo studies.
[Slide.]
The challenge for locally acting drug products is that these products do not require systemic distribution in order to reach sites of action.  Consequently, pharmacokinetic studies, in general, are not appropriate for documentation of BA and BE.
[Slide.]
When we talk about the locally acting drug products we have, then, to concern ourselves with both local delivery, which relates to efficacy, and, because these drugs are absorbed into the systemic circulation although, generally, it is not wanted, we have to concern ourselves also with systemic exposure.
[Slide.]
The recommendations for bioequivalence that appear in our nasal BA/BE guidance pertain to formulation equivalence, recommendations that the inactive ingredients be qualitatively the same as those in the reference‑listed drug, and that at excipients be quantitatively the same; that is, within plus‑or‑minus 5 percent of the concentration in the reference‑listed drug.
Furthermore, that the devices be functionally comparable.  That is because these drugs are, as we all know, combinations of formulation and the device.
[Slide.]
Regardless of whether in vivo studies are needed, we always ask for in vitro data for BA and BE whether it be a metered‑dose inhaler or a dry‑powder inhaler or nasal sprays.  We are considering confidence intervals for comparative data for selected of the in vitro bioequivalence measures.
As has been indicated by Dr. Tsong this morning, those statistics are under development.
[Slide.]
For the metered‑dose inhalers and nasal sprays, the draft guidance lists six tests that we feel are appropriate for characterizing products; that is, dose or spray‑content uniformity through container life, droplet‑size distribution, drug particle‑size distribution, spray pattern and plume geometry, priming and repriming and tail off. 
Those six tests are to be provided in the BA and BE portions of the submissions in addition to information in the CMC jackets.
[Slide.]
On the in vitro BE side, statistical comparisons under development are the profile comparisons for the cascade impactor data.  Dr. Tsong has talked about the f2 and the chi‑square approach.  The nasal BA/BE guidance refers only to the chi‑square statistic.
But we recognize that there are other possible approaches and we are going to be hearing from Dr. Andy Clark in the next presentation concerning a different approach to profile comparison.
Then, for the non‑profile comparisons, we have recommended those for dose content uniformity for container life and certain other in vitro tests as indicated in table No. 1 of our draft guidance.  It is based upon a population bioequivalence criterion.
[Slide.]
The proposed bioequivalence criterion for content uniformity requests that the mean performance of the test and the reference products be determined, the variability of the reference products and the variability of the test product, within and between batches be determined.  The criterion is based upon differences between test and reference means, differences between test and reference variances, and then scaling of the bioequivalence boundaries to the referenced listed drug variance.
It uses the one‑sided, 95 percent upper confidence bound with an alpha of 0.05.
[Slide.]
This is the equation.  This is the proposed equation for population bioequivalence.  We simply put in equation form the information from the prior slide showing shown differences in means, differences in variance and then, in the denominator, scaling to the reference product variance.
[Slide.]
Turning from in vitro to in vivo BA/BE, there are concerns about local delivery based upon a clinical study, systemic exposure based upon pharmacokinetic study or systemic absorption based upon PD or clinical study.  
Bullets No. 2 and 3 are simply a definitional issue where we are saying systemic exposure is defined as pharmacokinetics and systemic absorption is defined as either PD or clinical.
For nasal‑solution formulations, we are requesting, for product quality, BA/BE in vitro data only. 
[Slide.]
For nasal sprays, our draft guidance proposes three different types of clinical studies to establish efficacy.  It proposes only one of those three studies would be needed, however, not all three.  And they are the traditional two‑week treatment study, a days‑in‑the‑park study, or an environmental exposure‑unit study.
I should indicate that these slides were prepared by my colleague, Dr. Gur Jai Pal Singh with a presentation he gave recently.
[Slide.]
Now, BE studies for nasal sprays; in addition to the efficacy side of things, there is also the systemic‑exposure side of things.  For that, we recommend, if possible, that a pharmacokinetic study be used to establish bioequivalence.  We recognize that, for some drugs, the systemic exposure may be so low that it may not be possible to measure the drug in the plasma.  If that is the case, then we are recommending a pharmacodynamic study.
[Slide.]
Turning from nasal products to inhalation aerosols and, specifically, albuterol MDI, pharmacodynamic endpoints; our present thinking is that pharmacodynamics based either upon bronchodilitation or bronchoprovocation maybe used to document bioequivalence and, in fact, the Office of Generic Drugs has approved generic albuterol MDIs based upon both of those endpoints.
[Slide.]
Our current recommendations for the randomized crossover design for the pharmacodynamic study for albuterol MDI are that, in addition to baseline data, that one puff and two puffs of the test product, one puff and two puffs of the reference product be included in the study design as a minimum although, in order to better define the dose‑response curve, one, two and three puffs of test and one, two and three puffs of reference would be preferred.
[Slide.]
In addition to the efficacy type of study, there are the concerns about systemic exposure of inhalation aerosol products and, for albuterol MDI, we recommend a randomized, two‑way crossover study.  This is conducted generally in healthy volunteers and the study could be a PK study.  We would prefer that, although the current products which we have approved have been based upon comparative pharmacodynamic endpoints for albuterol MDIs.
[Slide.]
And then data analysis for the clinical bioequivalence studies; that data analysis is study‑design dependent.  For rhinitis studies, those are categorical endpoints and, consequently, the appropriate statistics must be used for those.  For pharmacodynamic studies, we have adopted a dose‑scale analysis which I won't take the time to go into at this time.  For systemic‑exposure studies, we use the pharmacokinetics.  We use the conventional two one‑sided tests procedure.
Thank you.
DR. LEE:  Andy Clark is going to talk to us about an alternative view profile analysis.
Profile Analysis of Cascade Impactor Data:
an Alternative View
DR. CLARK:  Good morning.
[Slide.]
First of all, I would like to thank the committee for inviting me to come on this morning and talk and, particularly, to Dr. Adams for giving me the job of giving an alternative view on how we should look at profile analysis on the cascade impactor.
[Slide.]
I guess where I would like to start is a little explanation about background.  There are three main reasons I can think of that you would want to compare impactor distributions and make some sort of measure of similarity or dissimilarity.
The top two, I guess, are the two we were talking about this morning, releasing batches or bioequivalence between a new product and an innovator.  The bottom one is up here mainly because this is where this piece of work and, I guess, along with a lot of other work we have had this morning, this piece of work is still a work in progress.
But this is really where it started, an interest in trying to figure out how good a radiolabel has to be on the product to be able to match the product well enough to tell you what it is doing in the clinic if you measure deposition profiles. I think that the idea behind this one applies as well.  
I guess the question I want to ask is whether simple statistical distance or a measure with some physical significance is actually really needed when comparing these impactor distributions.  To be honest, having heard Dr. Tsong this morning, I am not sure simple statistical difference is the right terminology.
[Slide.]
I guess the question is we all know we have got to measure size distributions because we all believe they are physically significant in terms of determining the dose of aerosol that gets to the site of action within the airways.
[Slide.]
What this chart is trying to point out is the approach, so far, appears to be to use this simple distance measure.  So this is the distance between a reference distribution and a test distribution.  In this particular case, I have chosen log normal distributions, tests 3Êmicrons with a GSD of 3, reference is 3, et cetera.
But the object of the exercise is to measure these distances and either f2 or chi square is really a function of this distance or some of these distances between the two distributions.
The problem I see in taking that approach is if you look at the significance of these distances, it depends where you are on the size distribution curve whether that particular significance in determining the dose that reaches the lung or not.
For example, the top end, here‑‑this is the top stage of an Anderson cascade impactor‑‑a difference here, in terms of its implication for change in dose reaching the airways, is really pretty small.  I apologize for getting this the wrong way around.  The 1.2 should be up here and 9 should be here.
It is pretty pivotal.  But, if you take this distance, 12 percent difference in the distribution, that 12Êpercent difference has to be normalized as to how it affects the deposition that reaches the lung.  In this particular case, at this particular size, that difference is really pretty small, zero, if you are looking at alveolar deposition.
It doesn't matter what happens up here.  All the aerosol is deposited in the upper airways.  If you want to go down to a smaller size, around 1 micron, 0.9 or 0.8, in terms of fraction, would be deposited in the airways so, a change here could bring about a major change in lung dose.
So I guess what I am arguing for here is that you have to understand the physical significance of where the change is taking place in the size distribution, not just sum the statistical differences regardless of where it is in an analysis.
[Slide.]
So what I said about doing this is to try to take a look at f2 and chi square and see if they measure the differences in distribution in any way that is relevant to how a product might perform.  
Rather than trying to use real data, the model was pretty simple; log‑normal distribution for the reference aerosols, log‑normal distribution for the test aerosols and the two variables here are either a change in MMAD, which is the blue line here, parallel so the GSD is the same but the MMAD is smaller, or a change in GSD, which is the width of the distribution‑‑i.e., same MMAD, different angle on here, meaning a different width in terms of distribution.
[Slide.]
If you take a look at f2, which Dr. Tsong defined earlier, and see how that responds to those changes in size distribution, what you get is a nice inverted, almost triangular, function.  As you go from the test aerosol, which in this case was 2, move away, either to a courser MMAD or a finer MMAD, f2 decreases.
Typically, for the dissolution‑type testing, you take an f2 equals 50.  So, for this particular aerosol with a GSD of 2, you can get anywhere between 1.2 and about 2.7Êmicrons, which would be judged by an f2‑50 criteria as being similar.
You could also move the GSD axis.  Remember, the test aerosol here was 2 MMAD, 2 GSD.  You get the same sort of function in response to the change in the width of the size distribution and, again, an f2 of 50 will give you as GDS of anywhere between about 1.5 and in excess of 3.0, in this particular case, as being similar.
Of course, that is varying two variables independently.  You can, obviously, put them all together and build a response surface like this to change in size distribution.
[Slide.]
What we have got here; this is for a test aerosol, again, of 2 microns, MMAD, 2 in terms of GSD.  This is varying the GSD.  This is varying the MMAD.  This is how f2‑50 responds.  So what you get if you look at a set of log‑normal distributions relative to a reference and you slide this three‑dimensional picture here at f2 equals 50, is you get an ellipse.
Any size distribution that is inside this ellipse would be judged by an f2‑50 criteria as being similar.  So the easier way to look at that is actually just project it down onto the bottom axis of GSD and MMAD.
[Slide.]
So what I have tried to do here is put together five different reference aerosols.  So we have got 1, 2, 4, 6 and 8 microns MMADs as the references.  They all have a GSD of 2 in this particular plot.  And then we see how they respond to changes in either in MMAD or the GSD.
You can see what you get is an ellipse.  Anything inside this ellipse, according to this f2‑50 criterion, would be judged as similar.  One of the problems you get is, for a 1‑micron reference distribution, the distance from here to here if we don't vary the GSD, is about 0.7 of a micron.  So, an f2‑50 criterion would allow you to take an 1‑micron aerosol and somewhere around 0.7 microns MMAD would be judged as similar to somewhere around 1.3 microns.
You will see, in a minute, that doesn't make a lot of difference in terms of deposition in the dose that the would reach a patient's lungs. However, if you go up to the courser aerosols, the situation starts to become a little different.  4 microns, if it was in the middle here, would mean that you could get up to somewhere around 1.3 times 4, so somewhere around 6 or 7 microns at the top end and somewhere around 3 microns at the bottom end.
Now, a 3‑micron to a 7‑micron difference in terms of the aerosol that is deposited in the lungs makes a big difference in dose, as you will see.
Those of you who are confused as to why the ends are flat here, what you are seeing is a limit in the resolution of the Anderson cascade impactor.  This was run as a simulation on an Anderson.  If the aerosol gets too big, the f2‑50 flattens at the top because all the aerosol is on the top stage.
If the aerosol gets too small, it flattens at the bottom because all the aerosol is in the bottom stage.  And then, of course, the f2‑50 does not respond because you are looking at no change in sort of seven or eight of the stages and only a big number on one of the them.
[Slide.]
You can plot the same thing for an MLI, which is the other instrument that I have done here.  Again, you get the flat ends.  They are slightly different because of the way the cascade impactor‑‑the range of sizes that it analyzed.  But the difference here is still pretty much the same in terms of what an f2‑50 would allow as a pass in terms of a similar aerosol.
Again, at a small size, this difference is not too big in terms of the difference it makes in terms of lung dose.  At a larger size, up at around 4 microns, maybe 6Êmicrons, this difference would be substantial in terms of the dose that would actually reach the lungs.
[Slide.]
So that is a rough idea of how f2 responds to size‑distribution changes.  This is chi square, which is the other alternative that Dr. Tsong talked about this morning.  You will notice the shape is different.  It is not an inverted cone.  It is much more of a sort of flat mushroom hat.  
But, in essence, the ellipses are pretty much the same in terms of if you set a particular value of chi square here to either pass or fail, you would have an ellipse when projected down onto this MMAD GSD axis, which says anything inside the ellipse would pass.
The question is are those response surfaces for those particular statisticals at all similar or relatable to a response surface in terms of how you change the dose that actually gets into a patient's lungs.
[Slide.]
The answer is they are not, but we will go through this chart first.  The reason they are not is because it actually matters whether the aerosol is a course aerosol how much change you can allow for a specific change in dose into the lungs or whether it is a fine aerosol.
Typically, what I have tried to do here is choose the 1 micron that we got off the previous slides.  f2‑50 would say we can go from about 0.8 microns here to about 1.3.  The change in dose, and I accept this is a lung‑deposition model.  I don't believe it is directly applicable in terms of absolute number, but I certainly believe you get doses that are proportional to these sorts of numbers.
But the change in deposition is really pretty small.  It is on the order of 4 percent change down at 1Êmicron.  If you take the same f2 criteria and apply it to an aerosol up at 4 microns, you could end up with a change in lung deposition of somewhere around 150 percent depending on whether you are up at this 6.5‑micron end or down at this 3‑micron end.
So f2 and chi square, actually neither of them respond in a way that is relevant to the physical situation of what goes on with those aerosols when they are inhaled and deposited in the lungs of a patient.
[Slide.]
Just to try and fill you in again with a three‑dimensional plot, this is for a 2 micron, 2 GSD aerosol.  All the changes in lung deposition here are actually plotted as negative.  In reality, what happens, of course, is the aerosol goes this way, the change is positive.  But it is just easier to look at this surface.
So out here, GSD of 2.8, 2.9, MMAD of about 1.2.  There is a 28 percent difference compared to the deposition we would get in the lung from this 2/2 micron reference.  So the shape really here is sort of a saddle shape.
You will notice the shape for the f2 response surface and for the chi square is much more of a cone or an upside‑down mushroom.  So they don't match very well.  
[Slide.]
If you do the projection down onto this GSD/MMAD axis again, this is the typical f2‑50 plot for a 4‑micron and an 8‑micron aerosol in this case.  This is a 10 percent change in lung deposition.  The inside one is an 8‑micron aerosol.  The outside one is for a 4‑micron aerosol.  If I was to do a 1‑micron aerosol on here, the line would probably be here and up here somewhere.
So, not only do they not have the same response surfaces, but if you try to measure a change here, bounded by an f2 number‑‑and this is just particularly f2‑50‑‑you get a channel here where you get significant changes in lung dose that gets to the patient, but you get areas outside by this f2 criterion where you would have a substantial change in lung deposition but the f2‑50 would say you have got the same aerosol.
One of these major problems is that it doesn't know whether you are dealing with a fine aerosol or whether you are dealing with a course aerosol.  It is merely just the sum of statistical differences.
[Slide.]
The way I propose, and this bit is a real work in progress‑‑the only way that I could think of, having got through that primary analysis to try and correct that situation, I use the term "weighted" very, very differently from Dr. Tsong's weight in his chi square a little earlier on‑‑was to actually try and weight the importance of the amount of material in each stage. 
So, for example, this is a column of deposition weights.  It is merely calculated from a lung‑deposition model and you will see, in a minute, this is one of the limitations.  I think it would probably take us another five years to agree on these weighting factors, but the throat and stage 1, of course, have a very low weighting factor because they contribute very, very little to that part of the distribution that is important in getting into the lungs and affecting an efficacious dose.
Stages at the bottom of the impactor have a much higher weight because there the size fractions stand a high probability of getting in through the mouth and the upper airways and depositing in the lung and, hence, constituting part of an efficacious dose.
Really, all I have done here is taken the median sizes off the stages for an Anderson, calculated some weighting factors based on a pretty simple lung‑deposition model, taken the weights‑‑this was for a log‑normal distribution on the Anderson plates‑‑and then just multiplied the two together to get a weighted distribution.
I think, at this point, there is a variety of things that you could do with this.  You could maybe normalize and go back to one of the f2 or the chi square type statistics because you have weighted the distribution appropriately to the important end.
Or, the approach that I actually took, certainly from a radiolabel perspective, was just to do a sum of all of the weighted fractions to get what I have called a theoretical deposition factor.  I call it this simply because these weights are based on what might get into the lung.  This is the amount of material in each fraction and the sum gives you some measure.
I admit it is not a perfect measure.  It absolutely won't marry what happens in a patient but it is some measure of quality in terms of that fraction of the distribution that gets into the airways.
[Slide.]
Can you apply this sort of approach to real data.  I have to thank Bo Olsson and Mike Rebe for allowing me to use the data that the European group generated on MDIs a few years ago.  What I have done here is calculated this theoretical deposition fraction, looked at the SDs, both for a set of MLI data across five different labs and across the Anderson across five different labs.
Basically, they all come out reasonable similar.  I have not completed the statistics on here but within these standard deviations, these are all the same.  There was one odd one.  It is interesting that the consortium, or the group, themselves, noted that this dataset was odd.  
You will notice the theoretical deposition factor is low.  The reason is, when you look on the raw data, there is missing material down on this bottom stage that is on this top stage.  I only put that in to try and demonstrate that this number appears to be sensitive to what is happened to the distribution.
[Slide.]
I am going to skip through this one pretty quickly.  This is where, I guess, this investigation started, trying to look at matches between radiolabel and drug.  I would emphasize, however, that the process is just as valid to look at the match between two size distributions.
Finally, where does that leave you?  It is an alternative approach.  The advantage I see is you have got flexibility.  That is also the major disadvantage.  In order to apply weightings to the distributions, rather than going back and doing an f2 or a chi square or the distance statistic, everybody has to agree on what weighting factors would be appropriate.
I think that would probably be a tough job.  If you got a group of people in this room together and try to agree on these, it would probably take some while.  But it is flexible and you can actually apply to it to different products.  For example, systemic; you would want to look at what was deposited maybe in the peripheral lung.  Local drugs; maybe the whole lung.  Or maybe a combination of the two.
You can apply some simple statistics to the number afterwards.  You don't have to go through a huge sum of the squares of the distances between the distribution.  I believe it has physical relevance in that it notices the difference between an 8‑micron and a 1‑micron aerosol which the f2 and the chi square statistics, as proposed, don't.
Disadvantages; choosing the weighting factors.  Quite how you deal with distribution pattern, as proposed. If you only use one set of weighting numbers, you can either get some number that is proportional to lung or some that is proportional to alveolar, but you don't get both.
The other disadvantage, which I guess Dr. Ahrens and I have been talking about, and it is actually not a primary measure.  You are taking a raw dataset and you are imposing some weighting factors on top of it. 
I guess, work in progress.  I don't quite know where to go from here in terms of whether this theoretical deposition fraction is a good approach or whether, having weighted the distribution, you could then go back and take a look at the standard sort of distance‑type statistics and see if you could apply those a little bit better with a bit more sensitivity to what is important about the change in size distribution from one end to the other.
That's it.  Thanks.
DR. LEE:  If you could stay for a minute.  I wonder what is your bottom line about the proposed chi square approach.
DR. CLARK:  If what you are trying to do is compare experimental variability, I think it is a very valid approach.  If what you are trying to do is look at a innovator product versus a generic, it doesn't have the sensitivities in the right places to judge the differences between those two parts appropriately.
I am sure Dr. Tsong and I will argue about it for a while.
DR. LEE:  Thank you.
Before I invite the next speaker up to the platform, I would like to remind the committee members that there are a number of questions that we need to address.  There are quite a few of them and we need to move quite quickly.
So, to continue with this British theme, I would like to have David Ganderton to come and talk to us about his views about DPIs, in vitro test performance and comparability.
DPIs: In Vitro Tests for Performance and Comparability
DR. GANDERTON:  Thank you very much, Mr. Chairman, for the introduction.  I hope, by now, the audience is getting used to the accent.  I would have a particular note of thanks to Wally Adams.  I think we have argued long and hard over a number of years, very constructively, and, of course, it was his agency which brought me here.
[Slide.]
I think, in arguing the case for the in vitro evaluation of dry‑powder inhalers‑‑I am going to ask your indulgence.  If you really go quite back to the beginning, of course, in Europe, and I am speaking first, personally, and second, with a European perspective.  You will have to distill out of this what is relevant to the system, as you see it.
But, in Europe, I think in all submissions, we would see a big pharmacodynamic component and a big in vitro deposition component.  Some submissions would have pharmacokinetics and some may have in vivo deposition, that is particularly using gamma scintigraphy.
It is important, I think, to remember that all these contributions are flawed in one way or another.  I think the pharmacodynamics element, of course, is accurate but it is very imprecise and, of course, the reverse is true of in vitro dependence.
Pharmacokinetics is often not applicable and, if we are going to do gamma scintigraphy, of course, we have got to modify the formula.  So, I want to certainly make the case that, in any overall assessment, you have really got to synthesize contributions perhaps from all these elements.  Of course, the way in which that synthesis is carried out will depend upon the relative strengths of each one.
Of course, this will vary very much from case to case.  I am going to argue strongly for the value of in vitro deposition because‑‑well, he would, wouldn't he?  It is an area which I have been attempting to promote for a number of years.
[Slide.]
In an absolutely super review, Pauwels said that, "The quantity of a drug deposited in the airways is the primarily determinant of the local airways response to the drug."  I would like to extend that a little bit and say, "The quantity of drug deposited," and I would add, "depends upon the concentration of the drug in the cloud, the particle size distribution in the cloud and the actual inspiratory maneuver by the patient."
I think all these things can be properly modeled and I think, if we do this carefully and well, we will get a very useful in vitro surrogate.
[Slide.]
It is going to be based upon inertial behavior because this will accommodate the way in which the capture mechanisms work in the lung.  So we shall have our modeling also based on inertia methods and we won't admit any other technique, although, of course, you can validate them and use them in aspects of product quality judgment.
But, as I say, we are basically going to be rooted very, very strongly in the areas of impactors.  
[Slide.]
Our particular problem, if we look at the next slide, is really to model the way in which the patient's inspiration through the device is going to separate the particles, because they are normally aggregating devices, produced to produce the respirable cloud.
In this respect, the powder aerosol, the DPI, is going to be very different from the MDI.
[Slide.]
So if we look at this next slide, we can see here‑‑oh; we can't see here.  Oh, dear; oh, dear.  Well, never mind.  I shall have to do some translations here.  What I am doing here is I am basically contrasting the performance of a PMDI and the DPI as a function of the flow rate.
Oh; the wonders of technology.  The slide, when I made it, was really quite legible and now it is even better.  What we have got here is some of our gamma scintigraphic work where we are looking at the deposition of cromolyn sodium as a function of rate of inspiration.
You can see that at 30 liters per minute, we are getting 11.8 percent of the dose into the lungs.  But, at the faster inspiration, that value is dropping quite significant.  Quite the reverse is true with the dry‑powder inhaler because here, at 60 liters a minute, we are getting‑‑and this is the gamma scintigraphy work‑‑we are getting 0.5Êpercent of the drug into the lung.  But at the faster inspiration of 120 liters per minute, we are getting 13.1.
One thing I think to point out on this slide is the enormous variability from patient to patient, always an aspect, I think, that we keep in mind as we compare methods.
[Slide.]
Now, that effect of inspiration rate on cromolyn sodium is, in fact, mirrored in some earlier work, years ago, by Auty and his colleagues at Phizens, when it was Phizens.  You can see, here, across this peak inspiration flow rate, you can see this enormous dominating influence of the inspiration rate on defining the respirability of the cloud compared with this question of the depth of the penetration.
It is quite clear that, in assessing dry‑power inhalers, this effective inspiration rate in generating dose completely overshadows the fact that the dose now generated at a relatively high speed will have a lower respirability.
So, all this, of course, leads to the model.
[Slide.]
If we go on to this next slide, we can basically pursue some of these arguments by looking at a comparison now in which we are looking at a Turbuhaler, a dry‑powder inhaler, compared with a pressurized metered‑dose inhaler.  This is a lung‑function response to the administration of either 250 micrograms or 500 micrograms given by these two methods.
This is work published by AstraZeneca.  You can see that, if we compare the 0.25 250 micrograms from the Bricanyl PMDI, we can see we get this response.  This is very much significantly doubled, or very significantly increased, if we increase the dose to 0.5 milligrams, 500Êmicrograms.
If we compare the data for the dry‑powder inhaler, we don't, in fact, see this differentiation between dose for reasons which I am sure Richard Ahrens would give us an explanation in terms of where we are on the dose‑response curve.
So that is, if you look, a PD, a lung‑function evaluation.  Let's go on to the next slide and see how this is reflected in a lung‑deposition study.
[Slide.]
This is not gamma scintigraphy, now.  This is basically being done by the charcoal‑block method.  But we can see here that, if we look at lung deposition‑‑that is in vivo deposition studies‑‑we can see a difference between the 2.5 milligrams given by the Turbuhaler or by the PMDI is equally reflected, the relative efficiency is equally reflected, in a lung‑deposition study.
So the lung deposition, now, reflects the greater deposition which we did not see when we were looking at a pharmacodynamic response.
I asked Lars Borgstrom, who basically published this data, if he would look back in his archives and see exactly how this would be reflected in an in vitro evaluation.  He kindly carried this out.  These were the results that we got.
[Slide.]
This is, in fact, some batches of the PMDI where we have got here a test done with an Anderson of 28.3Êliters a minute, and we are characterizing the amount of material in the cloud which was less than 4.7 micrometers.  We can see here we have got an average figure of something like 20Êpercent efficiency lung deposition predicted from this value, from the in vitro test.
If we carry out exactly the same procedure‑‑in fact, we are now using a five‑stage liquid impinger because we have to be able to vary the flow rates for reasons which we shall discuss in a moment, we can see that this ratio of 2 to 1, the Turbuhaler being twice as efficient as the pressured metered inhaler, nicely sustained.
So, again, we are building across these contributions.  In this case, we have got a beta agonist.  We have got an imperfect PD.  We have got a very interesting lung in vivo deposition which is nicely reflected in an in vitro model.
[Slide.]
What we have got here, and this is also not easy to read.  But let me take you through this.  We have got here some work that we did at Bath on measuring the peak inspiratory flow rates through a number of devices.  
This was the control.  We were using both volunteers and asthmatics.  This was the Rotohaler.  This was the Spinhaler.  This is the Turbuhaler.  This is a Boehringer device with the inhalator.  And this is a Pulvinal, which is a device which is marketed by an Italian company called Chiesi.
Obviously, these devices vary very, very greatly in their resistance.  This is reflected in the actual peak inspiratory flow that patients and volunteers can, in fact, inspire through them.  It is quite clear that in my model and in my techniques, I have got to, basically, try to control that characteristic if we are going to use any sort of sensible comparison.
So we look at the performance of these different devices which we have got to accommodate.  If we basically say what we will do is we will impose a pressure drop of 4Êkilopascals, which reflects, if you like, the minimum inspiratory pressure drop that a patient can impose across devices and then we will operate it at the flow rate which derives from that.
We see that these are the four kilopascal figures which would allow us to make a comparison between one device and another.
[Slide.]
What I want to do now is, again, to turn a little bit back to the power of these correlations.  Again, I am indebted to Bo Olsson.  This data is made available by Bo Olsson and his colleagues at AstraZeneca.
Essentially, we are looking at the fine particle dose in the sort of modeling that I have been describing for dry‑powder inhalers against lung deposition established by an in vivo technology which is essentially based on their  charcoal‑block technology. 
You can see that what we have got here is some sort of correlation.  The in vivo deposition, which you remember, going back to Pauwels' original statement relating to availability, to real availability of the dose, is broadly correlated to the fine particle dose as established by these in vitro inertial techniques.
The point that this makes, where we have got here the Turbuhaler, a PMDI, the Cyclohaler and the Rotohaler, you have to give much attention to the way the model is made.  If you notice here, we have got here a bold throat which is an old configuration originally introduced into this sort of evaluation.
[Slide.]
This is a USP throat where we have now got a right angle, much more sensitively and properly reflecting the oropharyngeal capture in our in vitro model.  We come onto that correlation again in the next slide.
[Slide.]
We can see how it has improved.  In other words, good modeling, now.  I think this is an excellent relationship between fine‑particle dose and in vivo lung deposition established by a validated method.
[Slide.]
Now let's look at this issue of flow rate and the way it affects particle sizes and fine‑particle doses.  This, again, is data for the Turbuhaler and you can see that, as the flow rate moves from 35 liters a minute to up to 80 liters a minute, which is probably the broad spectrum of flow rate you might expect to have through this device, you can see how this is reflected in the efficiency of the device.
This would allow you to say that, from the study of the variability of flow rate through that device in patients, it would allow you to say that, very much, most of the patients would get a dose which was adequate.  But you might have to do something strange at the lower end if you had patients which were highly compromised and you might, perhaps, have to restrict it in some aspects of pediatric use.
But the in vitro model, really, is very, very useful in assessing that effect for an individual device.  It is really quite hard to see how you could get some useful information other than by very, very complex and expensive experimentation over wide ranges of patients.
[Slide.]
If we come to the comparison, it is a bit more difficult because here is the Turbuhaler data, and I have got some data here from this Pulvinal device, which is a much higher resistance device and so, consequently, patients are inspiring through this at much lower rates.
In fact, the average flow rate that a patient would get through the PulvinalÊdevice is about 35 to 38Êliters per minute.  For the Turbuhaler, the average value is 55, 56‑‑he wants me to use 60.  So we will use 60.  The value there would be 60 liters per minute.
For the Rotohaler, which is a low‑resistance device, patients are, in fact, drawing through the device at a much higher rate.  So, using the fine particle dose comparison is much more difficult.  I suppose you would probably have to say that if you wanted to do a comparison between one device and another, you would probably be looking at the device which broadly had the same sort of slope of generation of fine‑particle dose against inspirational flow rate and probably which had the same overall resistance.
Of course, this means a very, very close relationship between the design of the formula and the design of the device.  So I think our technology is going to be much less adequate and much less powerful in working in a comparative way.  Its power, as I am saying, will be in the basic characterization of the efficiency of the device and I really believe that it can make a major contribution to this overall assessment.
We shouldn't have our in vitro and our in vivo and our PDs and our PKs in different compartments.  They are part of the story.
[Slide.]
Let's just finish with a word on methodology because, in this slide, these were configurations, experimental configurations, in this case to vary the flow rate through the device according to the principle which I have described.  This was elaborated in a very positive series of meetings that we had between the European Pharmacopeia and its working party on what, because of their classical upbringing, the Europeans call "Inhalanda," which is Latin, compared with the Americans pressurized and powdered inhaler dosage forms.
But the importance of this was that there was good harmony between the two groups and what is being suggested really is very similar between what is done in Europe and what is done here.
[Slide.]
Basically, now, what we are doing; we have exactly the same configuration except we now are leading from our device through an induction port, sensibly designed, into a multistage impactor where we shall have some elaboration of the fine‑particle distribution and the derivation of the fine‑particle dose.
That is really where we are.  The methodology, as I say, is being widely used in Europe with the harmonization process.  I think I would implore the American scientific community, the FDA, the USP, and so on, basically to embrace this technology.  It is not perfect but, on the other hand, it is state of the art and we are beginning to derive a lot of very, very useful data on in vitro characterizations.
It is about time we also derived some useful data on actual clinical and PD determinations because some of these studies out there are very, very seriously flawed.  We are in a position, I think, to collect data using this type of technology and comparing it with properly designed clinical studies.
That is where we are.  Where we are going‑‑of course, this isn't a stationary situation.  At the moment, there is a European‑American cooperation in designing a new impactor.  You must remember that we are employing impactors which were designed from completely different purposes and we have shoe‑horned them into what we need. 
Perhaps, for the first time, we are going to have an impactor which is, in fact, designed specifically for aerosols for delivery to the lung concentrating on 1, 2, 3Êmicrometers as the important dimensions.  We are also not necessarily finished at this point of the activity.  I know that there are some very, very interesting academic industrial cooperation going on in attempting to study different casts of a oropharynx to see whether this is, in fact, the best design.
So the situation is far from standing still.  To me, it is a lively, powerful contribution to the assessments of products and one which I would hope the regulators, both here and in Europe, will take fully into account in making these very important assessments.
Thank you very much.
DR. LEE:  Thank you.  Before you leave, I wonder if the members of the committee have any questions.  
DR. LI:  I have a question.  I would like to ask Dr. Ganderton whether there has been any progress in developing anatomical models for nasal inhalation where the particle size distribution importance is different plus that right angle would not necessarily apply.
DR. GANDERTON:  First of all, you raise a very important issue that the geometries are different.  I think that the modeling is far less advanced.  This has attracted a lot of attention, but it is capable, of course, of being a model.  I know of no important elements that are contributing to that assessment at the moment that are very specific to nasal deposition.
I think there is one aspect of where the modeling‑‑we are certainly, in extending the pulmonary delivery, looking at inspirational profiles rather than the simple square wave where you apply a pressure drop across the device perhaps building it up more in the way that the patient would, in fact, breathe in. 
DR. LI:  In your model, what is the relative importance of larger particles, say greater than 5Êmicrons, in lung deposition or deposition in airways?
DR. GAMBERTON:  I think, in that model, they are discounted.  They are actually completely discounted.  At the moment, we are in a relatively crude statement in defining a fine‑particle dose, which is that part of the cloud which is less than 5 micrometers and discounting that which is above.
We are seeing some interesting developments in the two preceding speakers where they are beginning to tease apart and compare and contrast these distributions.  So I think there are some intelligent steps to be taken in dividing the inspired cloud into fractions, possibly relating to deposition depths, and then, perhaps, carrying out an analysis on that basis, hopefully a lot simpler than the ones that were disclosed this morning.
DR. LEE:  Thank you very much.
DR. GANDERTON:  Thank you.  
DR. LEE:  Thank you for getting us back on schedule. 
Subcommittee Discussion
DR. LEE:  We now come to the part of the agency where the subcommittee has to deliberate.  There are a number of questions prepared by the agency to get your opinion.
The first lot of questions concerns profile analysis.  The first one asks, "Should all stages in the cascade impactor be considered in a comparison of test and reference products?"
Anyone?
DR. DALBY:  I have a few concerns about some of these methods.  Maybe Andy or Guirag could talk about them a little bit.  My question is, in all of these techniques that essentially use a point‑by‑point comparison, it would seem to me that there is a real danger that, if you had a very tight particle‑size distribution, or two that were very similar means but very close together, that happened to correspond to the difference between two stages, you could end up, by the particle size distribution being very close for two products, test and reference, and yet the amounts deposited on two adjacent stages could vary dramatically and all of these tests would falsely interpret those products as being vastly different.
That is kind of one concern that I have.  The other one is that I am sort of intrigued by Dr. Ganderton's last diagram.  I don't know, really, what it implies but has that cascade impactor connected to a variable‑flow control valve been used to imply that it is possible deconvolute the particle deposition in the impactor when it is operated at different flow rates or has it been used to suggest that the particles have been separated aerodynamically but we won't actually know what size particle impacts on each stage and, therefore, it is comparative between products but its absolute meaning is no longer interpretive.
So I would be interested in hearing what is the thrust of that kind of approach.
DR. GANDERTON:  An important part, I think, of the development of inertial techniques to accommodate different flow rates is that we now have got to have calibration methods apply to each stage.  Consequently, we do have real particle‑size data from this analysis.
The only complication, of course, is that these particles are necessarily going faster.  Consequently, we have always got to bear in mind, although we are properly characterizing the particle size as the materials move through the device and making appropriate inertial characteristics, there are implications, I think, for the actual basic respirability which might change with very, very fast inspirations.
Is that an answer?  No; he is not happy.  You can tell by his face.
DR. LEE:  Any other opinions about these questions on the floor?  
DR. HARRISON:  I have an opinion.  I think that all stages should be measured, but this is a bioequivalence comparison and it is really hard to know what value each stage has versus safety or efficacy.  What we did talk about is weighting, and we can debate that.  It may be that you should weight stages more than others, but I have not heard any rationale why we shouldn't have at least all the stages in the equation.
Again, this is a bioequivalence analysis.
DR. CLARK:  I will try and answer Richard's first question.  The difference between f2 and chi square is f2 actually works on a cumulative distribution.  If you look at it as a cumulative distribution, you don't actually get too much into the problem of it being only a stage 6 or stage 5, because, cumulatively, it will smooth itself out.
To answer the second question that David tried to answer, what he is proposing is using his impactor as a model of the lung not as a sizing instrument anymore.  If you vary the flow, it becomes a model lung.  It doesn't become a sizing instrument.
DR. BEHL:  I support, also, the measurement of all stages mainly because the clear effects of particle size versus the efficacy has not been precisely studied.  Therefore, cutoff at a certain particle size may not give the right results.
DR. DERENDORF:  I would like to come back to the f2.  We have to differentiate.  There were two different proposals.  One is the f2 factor and the other one is the f2 ratio which includes the variability of the reference.
I think you can come to very different conclusions depending on the variability of the reference including particle size, areas, ranges that may not be relevant that will have an effect on the variability and, therefore, an effect on the conclusion depending on where the goalposts are.  So that needs to be considered.
The number 50 is not a god‑given number for f2.  So that may be debated if that is reasonable or not.
DR. LEE:  Hearing no other response or comments, I assume that the committee shares the view of Lester Harrison and Charan that all stages ought to be considered.
The second question we have kind of drifted into is, "Should a statistical approach rather than a qualitative comparison be used for profile comparison?  If yes, does the chi‑square comparative profile approach seem appropriate?"
Walter, you are the expert.
DR. HAUCK:  So what do you think I am going to answer to the first part of this question?  Let's take that as given; yes.  I guess the bottom line on the second part is I don't think we are ready to answer that question.  I actually did want to congratulate Yi Tsong and Dr. Clark for their presentations.  I think it is a very difficult problem that they are tackling and it is good work that is helping to move us forward on this.
The kind of key issue that we are working around, both with the first question and then that Dr. Clark got to at the end of it, is how do you combine across the stages.  That is kind of the goal of both lines of work.  It seems to be a desirable goal to not have to look at each stage separately, partly to just make sense of all that and part of sort of the issue that Richard was just raising, which would really be exaggerating.
I know that the chi square is geared towards downweighting stages that seem unimportant in terms of how much is there, so it is doing a type of weighting.  If it was possible to get away from kind of an empirical weighting to a if you want to call it a clinical weighting if the weighting is actually based on some clinical notion of which of those stages are relevant, that would seem to be highly desirable.
So I am going a long‑winded way saying this is a good problem.  I think there are some good work being done, but I don't think we have an answer to the second part of that question.
DR. LEE:  Are you ready to offer some guidance as to which way they should be going?
DR. HAUCK:  No.  I don't think so.  Again, I think Dr. Clark summarized it pretty well, that there are these kind of two tracks.  If you go the sort of clinical weighting approach, we kind of get to sit back and kind of watch you guys try to make sense of all that.
It has been clear previously that the notion that there should be sort of one criterion that applies to everything doesn't make a lot of sense to me but to say that, for every single product, that you have to sit down and form a committee and agree on weighting, that would just sort of bring the whole process to a halt, too.
[Slide.]
So there has got to be something in between there.  I don't know quite what it would be.  So that is where I kind of said, no, I don't really‑‑I would like to see both of them proceed on what they are doing.  It probably takes us back to an earlier discussion.  We need to seem some real data with both of them applied to and get a better feel for what they do and what they don't do.
I think that the notion of using f2‑50, there is no particular value to that, as was just commented.  So part of the discrepancy that we are seeing between the two things Dr. Clark presented were that the 10 percent and the 50Êpercent don't have to be comparable.  So the fact that they were not comparable doesn't tell us anything.
There is just so much to do there.  My guidance is to encourage them to continue doing it.
DR. LEE:  Thank you.
Anyone else?
DR. SZEFLER:  The way I see it in terms of the complicated issues, there are drug, there is delivery device, and there are accessory devices.  When you put all these three together, I think you wind up with four categories.  I keep getting confused between where the questions are trying to sort out, how do you start putting these various questions into categories.
The way I see the categories is, first there is characterization of those three.  Then there is categorization, in terms of trying to find pigeon holes where these things fit into.  Once you have got that, then you get into the issues of equivalence and where equivalence plays a role is how much testing gets done beyond a certain product.
Then there is the standardization process.  So I see where we are asking the question now is in terms of standardization in the question because all the other ones have to be done before you kind of get to the simple question of simple testing.
So, for us to kind of move to that level, I think the agency and the pharmaceutical firms have to get together how are they going to characterize these systems, categorize them and then start going after questions of equivalence.  Then you can start sorting out what are the simple and critical areas.
Once you have kind of put things into pigeon holes, then you can kind of narrow down the things you do for standardization.  I think the different delivery devices point to those issues.  It may be that, once you kind of get these things pigeonholed, then, for kind of the simple standardization tests, you can pick those critical variables and then kind of narrow it down to certain tests.
But, until all these things kind of get pigeonholed, it is going to be very hard to kind of put everything into that one category and say one simple test is going to do it for all.
I am not answering the question in terms of that specific, but I think before you get to that area, you have got to kind of start pigeonholing things so that it is easier to communicate, and then to simplify the level of testing.
DR. LEE:  Thank you.
DR. BEHL:  I am a little confused as to why we have to do this as opposed to a quality test or a quality comparison.  It would seem to me that the stat approach should be done in conjunction with a quality comparison.  They both should be equivalent and should be done because the implication could be that they are qualitatively different and yet I am going to show the bioequivalence by showing a statistical confirmation of the product.
So maybe the question should be worded that should the stat approach be combined with a quality comparison of the product, because I don't see how you can do the stat comparison without a quality comparison of the products.
DR. LEE:  Are you expecting a response?
DR. BEHL:  Just a suggestion that the question be looked at in a different light.  Both are important.
DR. LEE:  Thank you.
DR. ADAMS:  I think, Charan, the question concerning the qualitative versus the quantitative comparison of the cascade‑impactor data comes in that if we are looking at these data and it is not quantitative, then it is going to be a subjective evaluation as to whether the numbers are, in fact, the same or whether they are different.
Different people would look at those numbers and might decide that they are the same.  So it is very helpful to have a quantitative measure of equivalence.
But I would like to, if I could, just make a comment concerning Andy's slide‑‑it is his last slide‑‑in which he talks about choosing weighting factors.  The approach that we have been using with the chi square or the f2 which doesn't use specific weighting factors as opposed to the approach that Andy talked about using weighting factors based upon the physiologic deposition from a Rudolph model, that is an interesting approach.
He gets to the issue about choosing weighting factors based upon receptor distribution.  I think that would be another approach.  For instance, we may look at comparison of cascade‑impactor data and put a lot of emphasis on stages 3, 4 and 5 where the drug is deposited.  But, in fact, are those the right stages to be looking at when we are talking about a specific drug‑‑for instance, and anticholinergic which is deposited principally in the central region of the airways or a steroid which may be more broadly distributed.
I think that these are issues that haven't been sorted out in terms of how to look at weighting factors.  I was very interested in Andy's approach, but I think that it requires some additional considerations, or at least we have to be cognizant of that issue.
If members of the subcommittee or people sitting around the table have some thoughts about that issue, I would certainly be interested in them.
DR. LEE:  You heard the question.  Any comments?
DR. MacGREGOR:  I do think a statistical approach is necessary.  I do like the idea of trying to tie it to a clinical effect.  It is probably not a requirement, but it is something that you do when you write a new drug application is you talk about where the receptors are.
This is the only way to be able to correlate clinical pharmacology from a molecular level all the way up to the clinical situation.  So it is always a nice lead‑in.  That data is out there.  It is always in the literature.  We know where the receptors are.  We know that cholinergic receptors are in one region, the beta sympathomimetics need to reach a different region.
When you make combinations of these two drugs, you actually have to justify your particle sizes and that.  So I think that it is not on a drug‑by‑drug basis, as Dr. Hauck's fear is.  It is more on a class system.
I think most of us know what the classes are and where the drugs should be deposited based upon the receptors where they are working.  So I like the approach of trying to tie it more to a clinical stage of the impactor.  Therefore, I think it becomes more that if you are going more for bioequivalence and you are slightly off in one of your stages, then you would go and justify why that stage is not really relevant to equivalence.
You have all the other parameters to go with it to back up your case.  So I like the clinical approach.
DR. LEE:  Thank you very much.  It seems to me that the statistical approach is preferred because it allows quantitation.  I think a very interesting twist to this is to tie it in with the receptor distribution.
Also, I heard that we are not quite ready to comment on the chi‑square comparison as yet.  I think further work needs to be done.  Is that all right?
MR. PAREKH:  This is actually a continuation of the question, sort of a question on top of a question.  When you talk about the bioequivalence and bioavailability, you just are referring more from if you are innovating a new product.  What happens is, in the course of this life cycle of the product, the product changes.  The components change.  The formulation changes. 
Is there an expectation that we use the statistical approach or a quality approach to compare as the product changes in the life cycle?  
DR. ADAMS:  If you talk about a formulation change during the life cycle of the product, that that may not be a bioequivalence issue.  It may or may not be a bioequivalence issue, all depending upon what the objectives are.  I don't have any response other than that.
DR. LEE:  I think we have to move on to the next set of questions.  
DR. HARRISON:  May I make a closing comment?  I am concerned that we can't really comment on the chi‑square analysis.  The draft guidance is out there.  Chi square is part of the guidance and is probably being used by the FDA right now as the deciding factor.
Do you have any comment, Wally, on how you see the appropriateness of that guidance right now in terms of using that for doing bioequivalence?
DR. ADAMS:  On the appropriateness of the chi‑square analysis, Lester?
DR. HARRISON:  Yes; based on what you heard.
DR. ADAMS:  I think that we have to consider what we have heard and discuss it within our technical committee.  No; I don't have any comments other than that.
DR. LEE:  I think the person to handle that question is not in this room.
We will move on to the final two questions concerning DPIs.  This is a long question; "What design features of the device and formulation, and what parameters, should be considered in determining pharmaceutical equivalence?"  
Did you hear the question?
DR. HARRISON:  The answer is the hard part.
DR. DALBY:  I will take a stab at it.  The thing that bothers me a little bit about this question is that I think it misses the important thing which is how do people actually inhale.  How the device responds to that and how the formulation responds, to me, is a very secondary concern.
It should be important to define how people are likely to use the device and how the device responds when it is used in that way.  But, to try and look at devices a priori and try and make determinations about what is important about the device and the formulations seems to me to be putting the cart before the horse.
So I think it is a very odd question.
DR. LEE:  Would the framer of the question shed some light about what is being sought here?
DR. ADAMS:  The genesis of the question has to do with approaches that firm would use for a second‑entry dry‑powder inhaler and how to best go about designing that product to assure the in vivo bioequivalence.
Our approach for metered‑dose inhalers has been to try and recommend formulation equivalence and device equivalence as a way of providing additional assurance for a firm going into a study that, in fact, the product will be bioequivalent in the end.
So the intention of this question is along those same lines.  As Dave Ganderton has talked about, product DPIs which have high flow resistance, low flow resistance, does that matter when a second‑entry firm is designing its product?  Maybe it doesn't matter?  That is the intention, to try and determine what factors in vitro matter.
Does the fact that one DPI may be a drug‑only, but another DPI could be a drug‑plus‑lactose, for instance?  Does that matter?  What are the criteria that matter before going into the clinical study?
DR. DALBY:  I never thought I would find myself saying this, but that seems to me a very academic question because most companies don't have the option of changing from one device to another device.  So that, essentially, is to preclude entry into the market unless you essentially have a device that is functionally very similar to the innovative product.
I just don't see how you will ever see a bioequivalence issue even come to you to review.
DR. ADAMS:  We haven't yet. 
DR. DALBY:  I don't think you will.
DR. LEE:  So the answer to this question is yes?
DR. LI:  I just want to make a brief comment.  I would think that the operating characteristics of the equivalent devices ought to be as similar as possible.  If one considers how these product will be used, essentially equivalent products will be considered interchangeable with the parent or the reference product so that a particular individual patient may get one product or another.  It may actually be different at different times depending on what pharmacy they use.
So the operating characteristics are different, if  the inspiratory flow requirements are different; for example, one requiring a fast inhalation for maximum delivery and another product requiring a slow inhalation for maximal delivery.  There really would not be an effective way to communicate that to the patient.
The instructions that providers give would differ depending on which equivalent product a patient was using.  There are other operating characteristics having to do with the device, the timing and so on.  
So just in general, I would think that equivalent devices really should have equivalent operating characteristics.
DR. ADAMS:  Dr. Li, does that mean, then, that you are recommending that products have similar operating characteristics in terms of their flow resistance, for instance?
DR. LI:  I would probably emphasize the instructions and the operating activities of the user rather than specifically identifying flow resistance.  So, if the maneuver is similar, if the resistance is somewhat different and the delivery from in vitro studies is equivalent, then the resistance, as a particular parameter, would be, I think, probably secondary.
The are issues involving sameness of labeling which a generic firm has to meet.
DR. LI:  First of all, help me with the terminology here.  That does indicate something that is intended to be marketed as a generic equivalent, is it not?
DR. ADAMS:  That is what I meant, it was generic.
DR. LI:  I just wanted to confirm that.  I guess I would disagree.  It seems to me that, if that is the intent, what we very clearly know from things that were presented this morning and elsewhere, that flow‑rate dependence of dose and dose delivered and particle‑size distribution can certainly affect response.
Therefore, I think it needs to match with the two devices.  It is hard for me to see how that would happen without having the resistance be similar and the flow rate generated, therefore, by the patient being similar as well.  So I think do have to not only match resistance.  You have to match flow‑rate dependence of drug delivery which includes that resistance as a function.
I guess, taking Richard's point here a step further, that what happens in the hands of the patient is what is really important.  There still could be some ergonomic differences in the device that, even though in vitro they could look very similar, and this may be stepping into the next question, but it seems to me that what kind of flow rates patients actually generate with those devices in typical clinical circumstances would be a step that would be nice to add to that.
DR. LEE:  Thank you.
Wally, do you have the input you needed?
DR. ADAMS:  Did we get the input that we needed? 
DR. LEE:  The question seems to be ahead of its time.
DR. ADAMS:  Just to elaborate a little further, the basis for the question is that we are in the process of drafting a orally inhaled BA/BE guidance.  The Office of Generic Drugs has not approved any dry‑powder inhalers at this time.  It is an issue that, hopefully, we will be dealing with over time and so, therefore, bioequivalence issues will become something that we have to deal with.
So that was the purpose of these questions, was to look at what aspects of DPIs should be considered on a comparative basis.
DR. LEE:  Apparently, no one around the table would like to take this on with specific answers.
DR. ADAMS:  I think that Dr. Ahrens' comments were very helpful in terms of comparability.
DR. LEE:  The final question for this morning is what comparative in vitro tests should be conducted to help support bioequivalence?  I think this is a follow‑up to David Ganderton's plea.  Any suggestions?
MR. PAREKH:  Is the question intended, in addition to the test that you have already requested in the draft guidance?  Are you validating whether those tests that are requested‑‑are you asking are those valid tests?
DR. ADAMS:  Basically, the intention of the question is to ask what additional tests may be needed such as drug delivery as a function of flow rate.
DR. DALBY:  It does seem to me that the last system that Dr. Ganderton described is very powerful in terms of comparing DPI products for two reasons.  One is that it addresses not only what is the peak flow rate that a patient would achieve at a particular pressure drop, the rate of rise of that flow, how it is generated, which, I believe, does have a strong effect.
The other thing is that I am generally a proponent of using gamma scintigraphy as a way of looking at product performance.  But DPIs are the most difficult product with which to utilize that technology and, therefore, I think that this is a good secondary method.  So I think that, while I know it is dangerous to recommend an additional test, I think the value of looking at the rate of rise of flow rather than just the flow rate is important.
DR. HARRISON:  I think it is also valuable to look at multiple flow rates of the variability of the devices as well as the regular tests, keep the Q1, Q2 about the same, collect the size distribution.
DR. LEE:  So what we have heard is that some incorporation of flow rates' influence on the subject at hand would be appropriate.
DR. DERENDORF:  I think the question is really only half a question because it asks what tests should be done but it really doesn't ask what should be the criteria after you do the test, if it is equivalent or not.  So I think there it is very important that we focus on why we are doing this in the first place and that is to predict both local and systemic exposure.
I think that should be a criteria that we need to show that whatever we set as a goalpost has clinical meaning and this is relevant for either systemic or local exposure.
DR. LEE:  So the word is clinically relevant in vitro tests.  
DR. HARRISON:  Absolutely.
DR. LEE:  Thank you.
On that note, are there any other comments from around the table?  If not, before we go into recess, I would turn it over to Nancy to say a few words.
MS. CHAMBERLIN:  We will have to start at exactly 1:30 for the public session.  We have eleven speakers during that time.
DR. LEE:  I would like to remind the public speakers that you only have about five minutes to make your case.
MS. CHAMBERLIN:  Four.
DR. LEE:  Four minutes; I'm sorry.  
[Whereupon, at 12:35 p.m., the proceedings were recessed to be resumed at 1:30 p.m.]

A F T E R N O O N  P R O C E E D I N G S
[1:30 p.m.]
MS. CHAMBERLIN:  We thank you for your patience.  We have new mikes.  You just need to be ten inches away from them.
Open Public Hearing
DR. LEE:  I would like to invite these speakers to come to the platform.  Instead of for me reading the title of your presentation, would you please do that for us.
Data Related to BE Testing of Nasal Sprays, and Comments
on the BE Studies of Nasal Sprays for Systemic Action
DR. ZAHIR:  Yes.  I will just be presenting some bioequivalence testing that we do according to the draft guidance.  I will just give some examples of the CMC sections, also.
[Slide.]
My main title, if I give it a title, will be "Do We Really Need These Numbers of Tests?"  If you look at some of the tests I have given in this transparency, and if you see that, according to the bioequivalence guidance, we will be doing a minimum of 8100 laser‑diffraction tests, because of three different distances, three different delay times, three life stages, then the precision number and 10 is the minimum size, and then we have three lots and then we have two products.
[Slide.]
Similarly, for spray pattern, we will be doing three different distances.  For priming and repriming, we have to report the early pre‑prime actuations also.  If you read the draft guidance, you will see that the justification they give is that in order to see how the prime is developed.
Do we really need these numbers of tests, if I take just the simplest form solution nasal spray?  I am sure you know my answer, that we don't.  Why don't we?  I have only four minutes.  I have to rush.
If we do a method, once we do our product development, we do method development, we do method validations and we select the most optimum, the best distance, the optimum size, the optimum delay time, the optimum obscuration values.  All these tests are done on the reference product so our goal post is the reference product. 	So, for an ANDA nasal‑spray solution, I don't think we need even three lots.  I will show you why we don't need three lots.  The lot‑to‑lot variability should never, ever be an issue of bioequivalence testing because this is well‑controlled by the CMC sections.
[Slide.]
If you look at the CMC guidance, and please read it.  I don't have the time to read this, but if you read section IIIF(1), IIIG and IV, you will find that all these tests are done.  According to the CMC guidance, these tests are done in order to make sure that the design reproducibility and performance characteristics of incoming lots of the components is maintained.
So the lot variability is an issue of the CMC section, not the bioequivalence section.  If we have good method developments, good validation studies and then we have the CMC control, a very strong control component, I don't see how we need even three lots and we need all these tests.
[Slide.]
If we look at even the statistics part of it, and I am not going into statistics because the whole day, today, we were into it.  But if you look at just two points, and all these questions were well‑discussed before‑‑if you look at the last two points, if you see if the reference variance is larger than the scaling variance, we are using reference scaling.
The idea is that if there is any variability among the reference three lots that we used in our BE testing, this will widen our limits and it won't punish us.  But if the reference variance is less, then it is not shortening the limit.  Rather, it is telling us to use a constant scaling.
So I don't think that if I buy three lots of reference product which are on the market, we all know that in most cases, all these components will be coming from the same lot of the components.  Nobody wants to do these tests on a daily basis.  If I am the manufacturer, I will buy all the components, the same lot, for five years.
So, in most cases, all these three lots of reference are anyhow from the same lot.  So we are not looking at true variability of the reference, actually.  Secondly, if I make a good product, I don't need the favor of this reference widening the limits.
I say I will compare just against one lot of brand and this is the manufacturer risk.  I think the consumer will benefit from it a lot.  Why?  Because I am now assuming that the reference lots have the least variability without doing the test. 
I will just summarize it.  Even this is a summary, but I will further summarize it.  I will say that if we have a strong CMC control, we have good method validation, method development, if I am using a constant‑scale criteria for the statistic.
Please, let's not say, "Oh; how are we going to determine variability if we don't have three lots versus three lots?"  I think we all know that statistics is used to define an objective.  We don't develop our objective around statistics.  We develop our objective first and then we develop statistics.
So those things I am sure Dr. Hauck and the good statisticians that we have can take care of the statistical problem.
Now, I will just jump.  In this case, if we have all these controls, and I am just touching them, but, please, look at them in detail, these two, the CMC guidance and the bioequivalence guidance should be read in conjunction with each other.  They are not separate.  They have separate objectives and development objectives should never be a part of the BE testing.
The second thing, and just finalizing it now, is that suppose, if I have a solution‑dosage form, then, according to the CFR, we all know that solution‑dosage forms don't have any problem with bioequivalence.  So if there is no problem with the variability, then the solution‑dosage form for systemic‑solution nasal spray for systemic delivery, the only problem or concern we have is data of the components, the container‑closure system, for which we do in vitro testing.
So I think maybe we missed it or what, but I think this nasal‑solution spray for systemic action should also be added to this guidance.  
[Slide.]
Lastly, I will say that what if I, and there is no mention of this in the guidance, but what if I have the same container‑closure system as the reference product has.  
It is up to me, or it is up to the agency, to give me a list of what do I need to prove that these are coming from the same lot of product?  What if the manufacturer of the component is me, myself and I am making it for myself.
So, if I have a solution nasal spray for systemic or local, when I am using the same components, is the brand product, the reference, should I get away for doing bioequivalence testing?
So, please, look into this also and add a statement to the guidance.
Thank you.
DR. LEE:  Thank you very much.  I think you made a very clear case.
DR. ZAHIR:  Thank you.
DR. LEE:  Dr. Dugger to address the issue of sublingual sprays.	
MS. CHAMBERLIN:  For those of you who did not get copies of the slides, they will be on the website.  The way you get to it is on the handout.
Uniqueness of Lingual Spray Delivery 
DR. DUGGER:  Thank you.
[Slide.]
My name is Harry Dugger.  I represent Flemington Pharmaceutical Corporation this morning.  Our company specializes in the development of lingual or buckle sprays to be delivered into the mouth for systemic effect.  These sprays are intended to reach a therapeutic blood level within three to ten minutes and usually at a much lower dose than the standard oral tablet or capsule and often, in many cases, the nasal spray.
[Slide.]
It is our feeling that this guidance should also include these products along with nasal sprays and inhalation products.  There are many similarities between oral solutions and nasal solutions, and there are many places where this testing can be made more simple.
The present guidance tends to divide products along the lines of products that are solutions and products that have a phase difference within the product.  Phase‑different products, multiphase products with these things like suspensions, emulsions, micellular solutions which are really not true solutions but have a phase transition within the solution,.
Lingual sprays would fall into the area of solutions as would nasal solutions and inhalation solutions.
[Slide.]
True solutions are a much simpler system than these others that we have been hearing about all morning.  By their nature being homogenous, they can be approved with reduced testing.  There are, really, basically three kinds of solutions.  This is something we all learn in chemistryÊ101.  There are unsaturated, saturated and supersaturated solutions.
Because the nature of the solution being homogenous, you don't have to worry about which you have.  You can test this very easily by just adding a seed crystal, for those of you who are into your basic chemistry.
[Slide.]
There are some examples here.  I don't have much time to go into all the differences and all the similarities between nasal solutions and buckle solutions, but here are some of them right here.
For instance, in a spray, for content uniformity, number one, there is no mouthpiece involved.  It is sprayed directly into the mouth.  Since they are homogenous, these products should be tested, in our opinion, by using the weight variation method of the USP.  Every single unit of a solution has exactly the same content as every other unit of the same weight.
So, if you can deliver a certain weight, once you know the average amount of drug and that weight of solution, every time you deliver that weight, you are delivering that amount of drug.
The weight‑variation method in the USP for tablets and capsules makes the assumption that the drug product or the drug substance is uniformly distributed in the table or capsule matrix.  In this case, we don't even have to make this assumption.  It is a known fact that it will be homogenous.
SCU through a container life; again, solutions do not change.  The only thing that would change in the case where container life would be a factor would be if ever there is leakage from the container or there was degradation of the product in the container.  Otherwise, again, every weight of solution will deliver the same among of drug product.
[Slide.]
Temperature cycling, for instance; with solutions, this is not such a critical matter because what you are really looking at here, I would believe, is a chance for a phase separation.  Many suspensions may aggregate and not resuspend on freezing and thawing, but for a solution, the contrasting event is crystallization of one of the components or, in the case where you have a liquid and a liquid, there may be a phase separation.
This could be very easily seen by just cooling it in an ice bath or a constant temperature bath.  If you think you have a supersaturated solution and you have a tendency towards crystallization, again, adding a seed crystal is all you need to find out whether that is the case. 
If the crystal dissolves, you are unsaturated.  If the crystal just sits there and looks at you, you are saturated.  If crystallization is initiated, then you were supersaturated.  It is as easy as that.
[Slide.]
Other examples are leachables.  Lingual sprays often use very high boiling solvents.  As a result, to use the formulation as an extraction medium for leachables is really not practical.  The boiling point of some of these solvents is close to 200 degrees celsius and, in many cases, it requires vacuum distillation to remove the solvent.
Vacuum distillation, of course, impinges upon the integrity of the leachables test because they may also vacuum distil, along with everything else.  I would propose, in a case like this, that the leachables be profiled using the USP tests A and B, which are water and isopropanol and that, in these cases, the actual solution to use for the formulation would not be used.
Orientation and resting for these solutions is also not a problem.  Solutions do not settle out.  Orientation doesn't play a role as long as the dip tube is underneath the solvent in the container.  If the dip tube is underneath the solvent, then the same weight of solvent will be delivered, the same weight of drug will be delivered.
If it is above the solvent, no drug will be delivered and if it is partially emerged, you can expect to get a partial dose.  Things of this sort can be best addressed in the labeling of the product informing the patient that they have to keep the container in an upright position and that the dose has to be delivered into the mouth in a way so that the dip tube is under the solvent.
For stability testing, we would propose that only one set of conditions be used for solutions.  Again, we are not so worried about settling out in the solution as we are probably worried about what happens when the solution comes in contact with the gasket.
We believe that the weight of test, the maximum test for this effect, is to have the solution upside‑down or inverted so that you get the maximum contact between the solution and the gasket and valve components.  If it will pass this test, it probably passes the other two, also.
"Solutions do not change on resting."  There are many places where solutions‑‑and this would be both buckle and nasal and probably respiratory, also, have a difference between that and the suspensions or phase‑separated products.
I would like to see the guidance, if at all possible, delineate between these two sets of formulations and make it very clear what testing is required for which one because our fear is that we come to the FDA and we are going to be expected to meet all the requirements for a nasal solution or a respiratory solution and they don't really apply to a buckle solution.
Thank you very much.
DR. LEE:  Thank you.
Next comes a series of presentations by two groups that have been very active in this area.  Dr. Cummings?
AAPS Inhalation Technology Focus Group (ITFG)/
International Pharmaceutical Aerosol Consortium (IPAC)
Collaboration Technology Teams
Overview of the ITFG/IPAC Collaboration
DR. CUMMINGS:  Good afternoon.  Thank you for the opportunity to speak today.  
[Slide.]
My name is Harris Cummings.  I am with Magellan Laboratories and Research at Triangle Park, North Carolina.  In the next few minutes, four minutes, I believe, I would like to provide a brief overview of the collaboration between the Inhalation Technology Focus Group and the International Pharmaceutical Aerosol Consortium in addressing the recent draft guidances from the FDA and to express the extent of interest and commitment on the part of industry to support the further development of these guidelines for inhaled products.
[Slide.]
Two groups are involved in this collaboration, the Inhalation Technology Focus Group which is the focus group of the AAPS is comprised of pharmaceutical scientists concerned with inhalation products.
[Slide.]
Also represented is the International Pharmaceutical Aerosol Consortium which is an association of manufacturers of aerosol products.
[Slide.]
Shown here are the three draft guidances which I think we are all pretty familiar with by now.
[Slide.]
As far as perspective of the two groups, both the ITFG and IPAC are in full agreement as to the value of the new guidance documents and welcome their issuance.  In addition, we agree with the BA/BE and statistical issues including the questions surrounding dose content uniformity presented by the subcommittee today.
We do, however, believe that, in addition to these important questions, there are many significant CMC issues particularly related to testing and specifications that still need to be addressed.  In addition, we believe that these difference can and need to be resolved through a data‑driven and science‑based approach to achieve the best guidances possible, a process which IPAC and ITFG have started and are prepared to continue to support.
[Slide.]
The ITFG/IPAC collaboration was proposed in the IPAC statement at the June '99 workshop as a part of a consensus‑building process involving collaboration with the ITFG.  The collaborative work between the two groups began in September of 1999.
[Slide.]
The structure of the organization is as shown on the slide and it consists of the steering committee and five technical teams.  The steering committee provides general oversight and review for the five technical teams which are shown in the slide and the technical teams are formed based on the general technical subjects found in the three guidances.
As you can see, CMC issues are the primary concern of the documents and of the technical teams.
[Slide.]
The significance of the concern and commitment on the part of industry is also reflected in the number of companies involved in this collaboration.  Individuals for more than twenty companies representing a broad spectrum of  industry, including manufacturers, contract organizations and component suppliers participate in this collaboration.
In addition to the approximately 85 individuals who participate directly in the steering committee and technical teams are many times that number of scientists at member companies who work on collection and evaluation of data.
[Slide.]
In the presentations that follow mine, a representative of each of these technical teams will present the current activities of the team and future work which the team plans and the commitments that each team is willing to make to further the work of the subcommittee.  This includes generation of data, technical papers and recommendations and even a willingness to meet with the subcommittee, if desired.
[Slide.]
Finally, the pharmaceutical industry, as represented by the IPAC/ITFG collaboration, is committed to a science‑based and data‑driven process of establishing best practices for the FDA guidances.  Large amounts of work have already been completed in this process and even more has been committed to by the member companies of this collaboration.
Thank you very much for your consideration.
DR. LEE:  Thank you very much.
The next presentation is on BA/BE by Steve Farr.
Presentation on the Work of the BA/BE Team
DR. FARR:  Thank you, Dr. Lee.  Good afternoon, ladies and gentlemen>
[Slide.]
I am Steven Farr.  I am actually from Aradigm Corporation in Hayward, California.  I am grateful for this opportunity to present to you today on behalf of the BA/BE in vitro and in vivo Test Team.  Over the course of a number of meetings, the team is about through collection and evaluation of relevant information, a series of data‑driven position statements that I wish to share with you today.
While the team used the current draft BA/BE guidance document pertaining to aerosol products for nasal application, it believes the findings are generally applicable to in vitro and in vivo testing of products that are both orally inhaled as well as nasal products.
[Slide.]
In the slide that you have in front of you, it really describes the team's work that has lead to the following propositions.  And these were agreed to at the last meeting.  With respect to in vitro testing, we strongly agree that it is essential for pharmaceutical product equivalence to have these tests and they should be included as part of the BA/BE guidance for oral nasal and oral inhalation products.
But it is not currently sufficient for BE approval without establishing in vivo BE.  In other words, in vitro testing is not sufficient to establish bioequivalence in the absence of in vivo testing.
Turning to in vivo tests for BE approval, in other words to establish product quality through the measurement of bioequivalence, the guidance documents for nasal and oral inhalation drug products should require the use of validated human models for testing for local and systemic exposure efficacy and safety.
[Slide.]
These working propositions are associated with certain assumptions that define their applicability.  The team recognizes that its BA/BE recommendations apply to locally acting drugs only as per the current draft guidance for nasal aerosols and sprays.  However, the team's comments apply to both orally inhaled and nasal drug products, but it is recommend that these dosage forms should be treated in separate guidances.
It is further recognized that the scientific and clinical bases for developing BA/BE guidance are evolving so the working propositions created by the team only reflect the state‑of‑the art knowledge.
[Slide.]
Based on currently available information, the team has reached the following conclusions.  Current in vitro tests, namely dose‑content uniformity and particle‑size distribution, may be used to estimate lung deposition but their predictability with respect to bioequivalence has not yet been shown.
Furthermore, the in vitro tests described in the current draft guidance are not necessarily more relevant or discriminating than clinical studies for the measurement of bioequivalence.  Systemic PK/PD studies will estimate local exposure which will contribute to safety but may not estimate local delivery which will contribute to efficacy and local tolerance.
In turn, efficacy studies alone of a locally‑acting agent cannot establish bioequivalence since they will not assure comparable safety through systemic exposure.  So, bearing in mind these preceding conclusions, the team believes that in vitro alone are not sufficient to assess product quality for bioequivalence.
Indeed, the guidance should not distinguish between testing requirements for nasal suspensions and solutions for in vivo BE.
[Slide.]
In closing, I just would like to inform the subcommittee that the team is committed to prepare a technical paper by the end of June this year to support the conclusions described today.  The purpose of the paper will be to highlight areas where there is sufficient data to draw conclusions and where there is not enough data at present, and also to review technical documentation related to BA/BE issues addressed by the team.
In addition, the team will be prepared to address the BA/BE questions which have been posed during today's meeting.
Thank you.
DR. LEE:  Thank you.
The next up is Dr. Bo Olsson addressing the specifications.
Presentation of the Work of the Specifications Team 
(Dose Content Uniformity/Particle Size Distribution)
DR. OLSSON:  Good morning.  My name is Bo Olsson, AstraZeneca.  I am grateful for this opportunity to present the statement of the CMC Specifications Technical Team.
[Slide.]
Our focus has been on dose‑content uniformity and particle‑size distribution as the key attributes.  For the industry, internationally harmonized guidelines is the key component for timely and cost‑effect development of safe and efficacious drug products.  A tremendous amount of work has gone into establishing a range of harmonized guidelines between the United States, Europe and Japan through the ICH process.
The Technical Team on CMC specifications believes that orally inhaled nasal drug products are amenable to the principles set forth by ICH.  Particularly, the ICH Guideline Q6A on specifications provides a process for establishing specifications and the extended application to inhaled dosage forms is being encouraged by the document.
[Slide.]
The ICH Q6A recommends a data‑driven process for specification setting.  Based on pharmacopeial standards, results from development and from pivotal batches and a reasonable range of analytical and manufacturing variability.  We concur with Q6A that it is important to consider all of this information an we don't believe it is justified to apply a single standard specification to the wide range of different products that are on the market and in development.
[Slide.]
Based on the collective experience, the Specifications Team has posed the hypothesis that the current state of OINDP technology may not allow general compliance with the DCU specifications in the draft guidances.
To address this question, to date more than twelve companies have initiated the process to collect a worldwide blinded database of more than 45 products to examine actual DCU capability of these products.  Our target is to have an initial assessment of the database by the end of July.
It is our position that the format of specifications should be based on sound statistical practices such that they can be translated into quality requirements.  We propose to work with the subcommittee and the agency to investigate using this database, alternate DCU specifications which may better serve this purpose.
This includes those approaches presented by Dr. Walter Hauck this morning.
[Slide.]
Also, for particle‑size distribution data, we have initiated a process to collect a database.  The target date for initial assessment is, again, by the end of July.  The purpose of this survey is primarily to examine the relevancy of the mass balance criterion as a product specification versus a system‑suitability requirement.  But it may also be used for looking into profile comparison techniques as well.
[Slide.]
In summary, we believe that the achievements of ICH should be taken advantage of in the FDA guidances and we are collecting a wide database which we hope can provide useful information for the subcommittee and the agency.
Thank you for your attention.
DR. LEE:  Thank you.
The next subject is tests and methods. Carole Evans?
ITFG/IPAC Technology Team: CMC Tests and Methods
DR. EVANS:  Good afternoon.
[Slide.]
My name is Carole Evans from Magellan Laboratories.  My role in this series of presentations is to give an overview of the work and approach of the Test and Methods Team.  The team has reviewed the draft CMC guidances and has identified areas where the FDA approach differs from that which we in industry feel is meaningful and scientific justified.
[Slide.]
As a result of this review, we have identified four general concerns.  Firstly, while recognizing there are certain key tests which are required for all dosage forms, we feel that the requirement for certain other tests should be driven by a critical review of the data and that the guidance should, therefore, distinguish between these two categories of tests.
In some instances, the language used in the guidance was ambiguous.  For example, we are uncertain of the intent behind the requirement for a stability‑indicating method of dose delivery of MDIs.  We would recommend a change in wording to, for example, a validated method free from bias.
We feel that the guidances should be further edited to clarify the requirements for each dosage form possibly separating each dosage form into individual guidances.  Finally, the team would like to strongly recommend further harmonization of requirements with other pharmacopeial and international standards; for example, the control of synthetic impurities should be aligned with ICH.
[Slide.]
The team has started its work by reviewing the diagram for metered‑dose inhalers and has identified several areas for comment.  These are shown here.  The scope of the comments vary from simply requests for clarification of wording and calls for harmonization to suggestions for alternate approaches to testing.
For example, in some cases such as the requirement for moisture testing, the guidance should indicate that the need for this test should be driven largely by the development data.  There are other tests such as plume geometry or spray pattern which did not offer meaningful performance characterization or redundant component controls.  These, therefore, should not be required.
[Slide.]
Our approach has been to develop position statements on each of these areas and the outline of those is provided in our written statement.  We plan to collect data with regard to most of these position statements.  In cases where the request is simply for rewording or for further harmonization, we will not be collecting data.
[Slide.]
We are currently in the process of collecting the data.  This data will allow us to evaluate and, where necessarily, refine our position statements.  To date, we have only addressed the guidance with respect to metered‑dose inhalers.  It is our intent to repeat the process for other dosage forms.
[Slide.]
After we have completed this process, we would like the opportunity to share our recommendations with the subcommittee and the agency.  We believe that data‑driven recommendations will be helpful to the subcommittee and, ultimately the agency, in creating stronger guidances.  We hope we can continue this discussion on critical CMC issues by providing these documents and welcome an opportunity for further dialogue.
Thank you.
DR. LEE:  Thank you very much.
Next up is leachables and extractables.  Dr. Dave?
Presentation on the Work 
of the Leachables And Extractions Team
DR. DAVE:  Thank you, Vincent.  My name is Kaushik Dave.  Actually work for Schering Plough.  However, this afternoon, I represent the Extractable and the Leachable Team.  What I will present is the opinion of the team based on reviewing the draft guidances.
[Slide.]
The team recognizes the importance of control of extractables and leachables from the point of view of patient safety and quality of these inhalation products.  The team is committed to providing information in this area.
[Slide.]
Just to give you some background with regard to definitions, extractables is what one observes when one uses solvents.  Leachables is what appears in the product.  Just to put it in some other words here, I hope that you can extract as much as you can from this presentation and, from my perspective, I hope a lot of this leaches in.
[Slide.]
Just to share with you; the team has identified four particular areas of focus which are listed up there.  The general approach which the team is taking is collecting data from several companies and what we plan to propose to do is analyze the data and make some recommendations in these four areas.
I will, over the course of the next couple of minutes, just go over these four areas briefly. 
[Slide.]
The first area of interest is what we have defined as analytical characterization of extractables.  We feel that the guidelines are not particularly clear and, perhaps, it may be advantageous to propose slightly different language and clarification.  For example, we feel that there is a need for clear definition of what a critical component is from an extractable point of view.
[Slide.]
The second area of interest is what we have defined as analytical characterization of leachables.  The real question here is do we really need to be extractables and leachables testing commercially since we are looking at pretty much the same phenomenon.
The draft guidelines have identified this and has alluded to the fact that if a correlation can be established between the leachables and extractables, perhaps, there could be some reprieve from leachable testing.  But, then, the question becomes what is a correlation here.  The guidelines are not very forthcoming.
Keeping in mind that we are looking at trace analysis here, firstly.  Secondly, we are trying to compare extractables, which is a solvent‑based phenomenon to leachables which is formulation‑dependent.  Can we really come up with a correlation and what kind of correlation should that be?
What the team proposes to do is, after reviewing data, come up with a working definition of a correlation.
[Slide.]
The third and most important area of discussion in the team is safety qualifications of leachables.  We feel that this is an extremely important area where there is a need for discussion and understanding as to what are the requirements.  Simple questions like, "What is the criterion for qualification?  How do we determine the levels?  Does ICH apply here?  If it does, do we compare it to the active ingredient.  They are not chemically related; does that make sense?"
Again, the team has formed a working group composed predominantly of toxicologists from the industry  they will be reviewing this closely and making some recommendations.
[Slide.]
The fourth and final area of discussions in the team is is this the right way of approaching control of components, testing them at the end.  Shouldn't we building quality into components instead of looking for quality at the end?  Again, there are a lot of systems out there, quality systems, which would insure that quality components are produced and also those quality systems will include change control and audit.
Actually, we have a technical team, the Supplier QC, which is looking into this.
[Slide.]
Finally, the team is committed to offer databased technical reports and recommendations to the agency and the subcommittee over the course of the next three to four months.  Also, secondly, the team is available to evaluate any extractables or leachables issue which the subcommittee or the agency would like us to. 
Thank you very much.
DR. LEE:  Thank you.
The next issue concerns supplier quality control.  Mr. Hansen?
Presentation on the Work
of the Supplier Quality Control Team
MR. HANSEN:  Thank you and good afternoon.
[Slide.]
My name is Gordon Hansen from Boehringer Ingleheim Pharmaceuticals.  I would like to take the next few minutes to present an overview of the work of the ITFG/IPAC Supplier Quality Control Supplier Qualification Team.  This collaboration has presented a unique opportunity for representatives from the pharma industry and component suppliers to collaborate on a review of the key issues in the draft CMC guidances which relate to the testing and qualification of inhalation‑device components and excipients.
[Slide.]
The draft CMC guidances focus extensively on testing of components as well as excipients.  A core theme of the CMC guidances with respect to these components is that tight standards and extensive testing by the pharma manufacturer are required in order to assure batch‑to‑batch quality of components and excipients.
[Slide.]
The team, in reviewing these guidances, has drafted a thesis or vision statement which may be described as follows.  The qualification and control of critical components in the area of performance‑related physical testing, extractables and leachables and excipients should be achieved by a combination of appropriate scientific practices, cGMP controls and supplier qualification systems.
[Slide.]
The first step for the team was to collect data on current GMP practices.  A survey of suppliers was conducted to evaluate quality and compliance practices at all stages of component, excipient, raw‑material and active‑substance manufacture.  Information was obtained from fifty‑three suppliers from raw materials through finished component manufacture.
[Slide.]
The results of the survey are shown on this slide.  One is that the level of cGMP awareness and compliance in the component and raw‑material supply chain is improving but improvement needs to continue.  Secondly, there are specific cGMP program elements which remain to be generally accepted and implemented especially early in the supply chain.
[Slide.]
Some general observations were also made from the survey in that there are no generally accepted cGMP guidelines for the component supply chain but guidelines do exist for the control of bulk excipient manufacturers which have been drafted by IPEC, which is the International Pharmaceutical Excipients Council.
[Slide.]
The team proposes the following: the team endorses the IPEC guideline for the control and cGMP compliance of excipients and it encourages its broader acceptance.  The team also proposes that an industry‑wide initiative be established to develop a cGMP guideline for component suppliers.  This collaboration would be a unique, perhaps unprecedented, partnership between suppliers, the pharma industry and the agency in designing a system which assures product quality by building it in rather than by extensive testing by the end user.
[Slide.]
The team also requests that the agency partner with the pharma industry and component suppliers by first formally recognizing the value of the cGMP guideline for component suppliers by acknowledging in the guidance documents that if sufficient supplier mechanisms are in place, appropriate reductions in testing will be considered.
We also ask that the agency help establish key elements and expectations for the cGMP guideline for components and participates in reviewing and commenting on draft guidelines.
Thank you for your time.
DR. LEE:  Thank you.
Now comes the concluding presentation by this group, Cynthia Flynn.
Concluding Presentation on ITFG/IPAC Collaboration
DR. FLYNN:  Good afternoon.
[Slide.]
My name is Cynthia Flynn.  I work for Aventis Pharmaceuticals.  I would like to take this opportunity to provide you the concluding remarks concerning the ITFG/IPAC collaboration.
[Slide.]
I trust that during the last six presentations, we were able to demonstrate the very high level of commitment and the massive amount of work that has been completed by more than 85 pharmaceutical scientists working in the foreground of this effort as well as the hundreds supporting them in the background which represent more than twenty companies to address key concerns in draft CMC and BA/BE guidance documents.
ITFG and IPAC is committed to collecting and assessing all relevant data which becomes available to this collaboration.  More importantly, we are committed to sharing those findings in a very timely fashion with this subcommittee and the agency.
ITFG/IPAC anticipates that this information will be useful to the subcommittee in its deliberations and also to the agency in the preparation of the final CMC and BA/BE guidances.  In addition, we believe that this information will assist in the creation of a very high‑quality document which the industry and agency can use in designing the dosage forms of the future.
[Slide.]
I just would like to take the time, then, to review very briefly the deliverables which the technical teams are committed to providing and the time frames associated with those deliverables.  Firstly, the BA/BE team is committed to preparing a technical paper on BA/BE that have been highlighted in the previous presentation.  This will be completed by the end of June.
In addition, that team will attempt to address as many questions as possible as have been raised during this meeting.
The Specifications Team is committed to completing, by the end of July, an initial statistical assessment of the actual DCU and particle‑size database which is collected by this collaboration.  We would very much like to share this initial assessment with you and with Dr. Hauck in order to help your endeavors.
The Test and Methods Team is committed to completing, within the next three to four months, the technology paper outlining the key MDI tests.  In addition, in the future, we also plan to do similar work for other dosage forms, as was alluded to by Carole in the previous presentation.
The Leachables and Extractables Team is committed to also completing a technical report within the next three to four months as well as to making recommendations within the next three to four months concerning leachables and extractables.
Lastly, the Supplier Quality‑Control Technical Team is volunteering to ask as a co‑leader with the agency in developing a cGMP guideline for component manufacturers.
[Slide.]
I would like to point out to the committee that it should be noted that the work of the collaboration deals with not only BA/BE issues, which have received substantial emphasis today, but also places a significant amount of emphasis on four critical CMC issues, not just the DUC issue.
[Slide.]
The collaboration of ITFG/IPAC is very convinced of the need for a science‑based interactive dialogue and is requesting that the agency continue the subcommittee process.  We are also requesting that the collaboration be given the opportunity to provide the deliverables that I just described in the next three to four months for the use of the subcommittee and agency in order to assist in the resolution of the various CMC, BA/BE issues.
[Slide.]
I would like, then, to conclude my remarks by acknowledging several groups.  First of all, we would like to express our deep gratitude to the agency for holding this meeting and allowing us to present the work that has been completed to date of the ITFG/IPAC collaboration.
We would also like to thank the members of the subcommittee for considering our comments and proposals and we look forward to working with them in the future.  I would like last to acknowledge the very hard work of all of those people I was talking about, the 85 in the foreground and the hundreds in the background, for the commitment, constructive collaboration, that they have given to the ITFG/IPAC collaboration.
Thank you for your attention.
DR. LEE:  Cynthia, may I ask you one quick question?  What is the size of the team, how many members?
DR. FLYNN:  The entire team?  Or a specific technical team?
DR. LEE:  A specific technical team.
DR. FLYNN:  They vary, depending on the technical team.  So you would have to tell me exactly which one.  We have a total of 85 members when you add up all the steering committee and all the technical‑team members.  There are five technical teams.
DR. LEE:  So divide by five.  Ten or fifteen?  Can someone be one several teams?
DR. FLYNN:  In some cases, there are, but not in all cases; no.
DR. LEE:  And then the position paper that you will develop or deliver will be a consensus document?
DR. FLYNN:  Correct.
DR. LEE:  Thank you.
That concludes the presentations by those two groups.  Now we have two more to go.  Next up is on CMC issues by Dr. Neugebauer.
CMC Issues
DR. NEUGEBAUER:  My name is Ken Neugebauer.  I am the Director of Marketing for Solvay Fluorides responsible for the NAFTA region.  I am speaking on behalf of and presenting the comments of Ms. Anja Pischtiak, Product Manager of Pharmaceutical Aerosols for Solvay Fluor based in Hanover, Germany.
[Slide.]
Solvay Fluor is a manufacturer of the propellants HFA227 and HVA134a used in inhalation drug products, marketed by Solvay under the trade name of Solkane, would like to make two comments on the major excipients and MDIs, the noncompendial propellants 227 and 134a.  The comments relate to the draft guidance for industry, metered‑dose inhaler and dry‑powder inhaler drug products chemistry, manufacturing and controls documentation.
[Slide.]
The first point.  Lines 288 to 295 identify a requirement for a toxicological qualification of the novel excipients 134a and 227 but do not give directives of what comprises a toxicological qualification.  The consortia IPACT I and II have submitted to the FDA extensive safety data on 134a and 227 intended for inhalation which may sufficiently demonstrate the toxicological suitability of the novel excipients 134a and 227 for use in medical products including MDIs.
Solvay believes that the uncertainty of the requirements for a toxicological qualification of the pure excipients strongly inhibits the pharmaceutical industry from reformulating its CFC‑containing products to HFAs.  Therefore, we propose that a definition for the toxicological qualification of the noncompendial propellants HFA134a and HFA227 be added to the draft.
The second point we want to make, lines 381 to 405 show impurity acceptance‑criteria limits for 134a impurity by impurity, which are given in such detail, strictly process related.  Solvay, for example, uses for the manufacturer of 134a pharma a process starting from trichlorethylene which is not mentioned in the FDA specification.
However, it is present in trace, but detectable, amounts in our product and, therefore, is specified by Solvay.  While Solvay has four additional impurities not shown in the specification quoted by the FDA, sixteen other impurities that are listed in the draft specifications are not contained in Solkane 124a as manufactured by Solvay.
Therefore, Solvay proposes to replace detailed impurity‑by‑impurity limits with acceptance criteria based on toxicological tests performed both for HFA134a and for HFA227.
[Slide.]
I submit, with these comments, Solvay's specification‑‑that is impossible to read; I apologize.  I will get a clearer copy for publication.  Basically, this is our specification for 134a with detailed description of all of the impurities listed and comparison for what Solvay manufactures in the draft guidance.
[Slide.]
This slide is the specification for Solkane 227 pharma as filed currently with the FDA to be added to the draft guidance in case the 134a specification remains.  The 227 specification is currently omitted.  
Finally, I have included with my submission that  we agree in principle with comments previously submitted by IPACT as published in the August 1999 Gold Sheet.  Again, I am submitting them with the key points highlighted for the committee.
Thank you very much.
DR. LEE:  Thank you very much.
The final speaker of this session is on growth effects of nasal steroids by Dr. Schenkel.
Growth Effects of Nasal Steroids in Children
and Differences among the Steroid Preparations
DR. SCHENKEL:  Good afternoon.  I want to thank the committee for allowing me to speak about this issue.
[Slide.]
I am a practicing allergist.  I am Director of Valley Clinical Research Center in Easton, Pennsylvania.  I have been involved in a number of clinical trials looking at differences among the various nasal corticosteroids.  What I am going to be talking about in the next few minutes is exactly that, the differences among the steroids in a clinical setting.
You have heard a lot today about trying to look at in vitro models and how to tell differences among the steroids.  I am going to point out to you the fact that there are differences, not just in bioequivalence but in what I have called bioactivity, particularly in the pediatric population and particularly the effects on growth.
I would urge the subcommittee to look at this very carefully.  It has already been looked at by the FDA in terms of acknowledging a new pediatric labeling for nasal corticosteroids.
It is well known that oral corticosteroids can cause growth suppression in children.  What was a surprise to the medical community was information that I was involved in that looked at the effect of certain nasal corticosteroids on growth in children.  This prompted a joint meeting between the Endocrine Metabolism Group and the Allergy Pulmonary Group in July of 1998 to review proposed class labeling for both oral and intranasal corticosteroids regarding growth suppression.
In fact, in November, the FDA did announce new pediatric labeling along those lines.
[Slide.]
The FDA reviewed the literature at that time on both oral inhaled corticosteroids and intranasal corticosteroids.  Two well‑designed studies were reviewed regarding the nasal corticosteroids, one of which showed a growth‑suppressive effect which I will show in just a minute.  I am not going to deal with the issue of oral inhaled corticosteroids because that is not the issue we are talking about right now.
However, it was reviewed and four of the five studies did show a growth‑suppression effect.  It is interesting because the growth‑suppression effect found in intranasal beclomethasone study, which I will review now, was separate from any effect on the HPI axis.  As you all are aware, the HPI axis is sort of the gold standard of some systemic activity of both nasal and oral inhaled corticosteroids.
But, as I will show in just a second, the growth‑suppressive effect in this particular study, which is in a handout, also, involving intranasal beclomethasone, did not have any effect on the HPI axis.  This study, which was a well‑deigned, double‑blind, placebo‑controlled study which occurred over the course of a year in 100 prepubescent children, looked at growth effects of intranasal beclomethasone diproprionate, 168 micrograms BID in half the patients and placebo in the other half of the patients.
The patients all had allergic rhinitis and all were between the ages of 6 and 9‑and‑a‑half.
At the end of the year, what surprised everyone was that there was a small but significant growth effect.  The placebo group grew about 5.9 centimeters per year where as the BDP group grew only 5 centimeters per year.  The conclusion was that the overall rate of growth was lower for the BDP group compared to the placebo group, about a centimeter over the course of a year.
This has been published now in Pediatrics on Line.  What I did not show in this slide but is in the handout is that the HP axis was not affected in any of the groups.  The conclusion was that a small, but statistically significant effect, of BDP on growth was observed separate from its effect on the HPI axis.
Again, we have talked earlier this morning‑‑I have heard a lot of really exciting talks on statistics and in vitro models‑‑looking at ways in which you can compare, on an in vitro basis, certain nasal corticosteroids or corticosteroids in general.  What I am going to show you, though, in an in vivo setting, the difference between beclomethasone and mometasone furoate nasal spray.
This study that I am going to talk about was not available to the FDA at their meeting and has recently been published in Pediatrics, the On Line version, in February of 2000.  This was a study in which I was lead author and looked at, in the same fashion, the effect of mometasone furoate on the same group of children, ages 3 to 9, on the effects of growth and also HPI axis.
[Slide.]
The study design was essentially the same as the previous study, about 100 children, half receiving placebo or half receiving mometasone furoate.  Standing height was assessed very carefully using stadiometric techniques.
With mometasone furoate, at the end of the year, children who received active drug did not have any suppression of growth in the group studied ages 3 to 9.  The group that received mometasone furoate did not have any significant side effects and, very importantly for the committee to understand, there was on effect on HPI axis.
So I think that this clearly shows that there are differences between these particular nasal corticosteroids.  Can we translate this into other nasal corticosteroids?  I believe we can based on bioavailability data.  I think that if the committee is to consider other types of nasal corticosteroids, that they should all go through the rigorous growth studies as the currently available models have been done.	
Thank you.
DR. LEE:  Thank you very much.
I would like to thank all the speakers in the open public speaking session for being on time and informative. 
Now we are going back to the form agenda which is a discussion on the in vivo BA/BE. 
The first speaker in this session is Dr. Roman on clinical studies for local of nasal aerosols and sprays.
In Vivo BA and BE
Clinical Studies for Local Delivery
of Nasal Aerosols and Sprays
DR. ROMAN:  Good afternoon.  However, it feels more like "good evening" to me.  My name is Izabela Roman and I am Medical Director and Founder of a contracting organization specializing in nasal study.  I was involved in developing new drugs and studying generic products in nasal allergy now for over twenty years, close to twenty years.
I would like to thank you very much for inviting me to help you with selection of a proper model of nasal study for the advisory board of FDA.  I hope I will not disappoint you, that I will not present to you a novel, revolutionary model which will answer all the questions.  We, as researchers of nasal allergy, are still struggling with the selection of the proper efficacy endpoints since we are still relying mostly on patients' reported symptoms and signs of nasal allergy which are very a variable and not very well standardized endpoint.
So, instead of presenting a completely new model, I will review the three proposed models in the draft guidance vis‑a‑vis their strengths, weaknesses and potential for bioequivalence studies.
[Slide.]
So, as you are all familiar, there are three well‑studied models in nasal allergy; the so‑called "park" study, the environment unit and traditional clinical study of seasonal allergic rhinitis.  Each of them has their weaknesses and strengths and I will not go over, first of all, the detailed description of the basic principles that they can all be done double‑blind, placebo‑controlled, most of them parallel.  That is all well known.
I also will not repeat the presentation of Dr. Mary Fanning who did this overview in the June presentation to you in 1999.  Again, I would like to present my opinion on the strengths and weaknesses.
[Slide.]
So the park study, so‑called, which usually involves one or two days.  It is a short duration of study which, of course, implies less weather variability and potentially better control evaluation of symptomatology and severity of symptoms over two days.  However, of course, it does not allow us to study drugs with longer duration of action and drugs which will require, for a steady state, longer treatment than one or two doses.
It allows cohort enrollment, again potentially dealing with less environment variability and patient‑to‑patient variability since they are all exposed to the same concentration of allergens.  Nonetheless, I believe this is not an easy way to deal when you talk about bioequivalence.  It is too short a study.
Of course, it offers more control compliance.  The  drug is delivered by the medical staff, mostly by nurses or research associates, so we know how the patient took the drug, how it was delivered to the nose.  It offers better compliance.  It has a great potential for, of course, obtaining a greater number of time points for subjective and objective data, subjective, again, evaluation of symptoms of patients' objective, potentially waiving the nasal tissues, collecting nasal washings, et cetera.
[Slide.]
However, it has a whole list of weaknesses.  Again, it is restricted to seasons.  Therefore, there are only three opportunities of conducting such trials in this country, at least; spring season, fall season and so‑called cedar season in Texas.
I get mixed up a little bit, not looking at my slides.  That is actually a weakness and I presented it previously as a strength that the drug does not reach effect.  There is a weather risk.  Frequently, it takes a long preparation to set up the studies, selection of patients and so on and so forth, and then rainy weather or stagnant weather does not permit you to conduct these trials.
There is a lack of site and population diversity.  Again, it is done usually by one site‑‑the other ones were done by two investigators‑‑so it is less representative of geography and other sites in the United States.  It is susceptible to single‑investigator influence.  Obviously systemic error done by one investigator carries through the whole study.
There is lower variability than the traditional study model‑‑I'm sorry; that belongs to the strengths.  However, the next one is the potential for high incidence of sedation.  It is a boring type study and if we study drugs which have a sedation potential, they are reporting in this type of study a lot of sedation.
Then it is not, of course, good for overall safety information.
[Slide.]
The type of study is most frequently used for pilot efficacy of new drugs, for onset of action, for dose‑response or at least the approach of dose‑response studies, and duration of the effect for single dose.
[Slide.]
In my opinion, as far as the bioequivalence potential of this, it is not very high particularly for the drugs which take more than two days to reach maximum effect.  Usually, because of less variability in weather and between subjects, the treatment sizes are smaller than the traditional study, up to 50 to 100 patients per treatment group.  100 is pretty big in this model.  And it is not inexpensive.
[Slide.]
The other proposed model is the environment unit.  The strengths are very similar to the park model.  Again, it is of very short duration so it is easy to conduct.  It controls the environment.  There is no environmental variability.  The concentration of allergen is controlled.  It can be done all year around.  It does not require seasons.  It is also a good model for non‑seasonal allergens such as cat dandruff.
[Slide.]
It is the farthest from reality.  Of course, it is something completely artificial.  It has a very limited number of center available.  There are just a few in this country.  The most well‑known, actually, is Dr. Day in Canada.  The whole duration is one day.  The observations are over eight hours so it is just a single‑dose type model.
The protocol is pretty complex.  It requires priming of the patients for establishing baseline and severity of patients.  Safety information is pretty limited from it.  
[Slide.]
Again, it is more frequently used for onset of action, for pilot efficacy and for single‑dose studies.  However, this particular one offers a potential for the crossover studies.  For short‑acting drugs, which for bioequivalence purposes could be studied in crossover design, this is a model which potentially offers such a possibility for other drugs such as intranasal steroids which would require long‑term treatment for maximum effect.  It has rather low bioequivalence potential for using this model.
Again, the treatment groups are much smaller than traditional, about 30 patients, and the cost is sky high.
[Slide.]
Finally, we are coming to the traditional clinical study.  It is closest to reality.  There are numerous sites around this country available to conduct such studies.  It is well tested and quite well validated.  It offers geographic diversification and, again, offers longer duration of observation versus the other models so we can observe steady‑state efficacy and long‑term safety.
[Slide.]
The weaknesses of this model is that it has high variability across sites, greater variability within a site due to the non‑cohort enrollment.  Some patients are enrolled at the peak season, others at the tail of the season, with different concentrations of pollens around.  There is a lower sensitivity for detecting differences between the doses or vehicle or placebo inactive.
It is very much season‑dependent.  However, there is also a perennial rhinitis which could be potentially studied for bioequivalence.  I don't think it will be a successful approach.  And then, in this particular model, there is almost lack of total control over compliance since these intranasal drugs are very much technique dependent, not to the same extent, of course, as an orally‑inhaled drug, but still technique dependent.
The compliance in the study is in the hands of the patient and, very much, evaluations of efficacy depend on patient diaries and interpretation of the measurement used there which is severity of symptom scores with the best definitions from absent to more severe.  Still it is patient dependent, how they evaluate themselves.
[Slide.]
It is most frequently used for efficacy and safety, for dose response and comparative studies.
[Slide.]
All of this is, of course, relative.  But between the three models, I would suggest that this is the best model of all of the three for bioequivalence type studies.
[Slide.]
The problem with them is that, because of the endpoint insensitivity and variability, it requires large patient population size for treatment.  Nowadays, it is about 130 and over per treatment arm and the cost is also substantial.
[Slide.]
So, in general, problems with in vivo bioequivalence studies, I would sort of summarize as follows; there is limited or lack of dose response.  I do not want to say that there isn't a dose response for nasal steroids or intranasal antihistamines.  I believe that the limited way we can measure efficacy and variability and lack of sensitivity of this method does not allow for clear discrimination between the doses.
We have great difficulty in blinding.  Obviously, all these products are delivered in devices which are patented specifically to the company producing them.  In order to blind them, they have to be covered with something and there are a lot of problems with blinding them.  The best way we can do it sometimes is just to have evaluator‑blinded, not double‑blind.
Vehicle and placebo responses make it quite difficult to distinguish between treatments.  I just would like to bring to your attention that vehicle which is frequently used as a placebo for intranasal studies is a very effective treatment.  In studies which we conducted in our group, we can prove a dose response to vehicle.  Once‑a‑day vehicle is less effective than a twice‑a‑day vehicle.  
So a vehicle is in higher doses, if you wish, or more frequent application, the efficacy is up to 35 percent change from baseline, which we usually use as an endpoint.  Then, again, we are struggling with limited and non‑standardized scales for efficacy measurements.  Even with the best script, the interpretation of these scales by patients that we are dealing with, and, of course, the sophistication of patient and user of such a method very much influences the results of the measurement.
[Slide.]
So, with this in mind, I would say that we have a changing nature of disease.  We have a very variable environmental and mental conditions.  We have subjective efficacy measurements and the spray‑dose form is very much user‑technique dependent, as I stated.  So we have high variability and rather low sensitivity models.
[Slide.]
How I would suggest to improve this traditional study model; Again, as I stated before, the dose response is something which is quite difficult to establish with, particularly, intranasal steroids.  So the requirements of doing two different doses to test the sensitivity of discriminating two doses is pretty hard.  So vehicle control, which I suggested, is really an effective treatment and is, in my opinion, one of the arm of the dose‑response treatments.	
So, maybe just to make this more doable, vehicle control should serve as this noneffective dose, noneffective not in terms of active component but effective in terms of efficacy.
There are frequent designs using run‑in period with vehicle or placebo control sort of run‑in period.  We learned that this really decreases the baseline severity so, without run‑in vehicle, we are increasing baseline severity and ability to discern differences in treatment groups.  However, for a well‑established baseline evaluation of symptomatology, some kind of just a collection of diaries and screening run‑in is recommended.
[Slide.]
The last slide, which I will present, is real data.  We conducted a study for a company with a generic intranasal steroid.  The design of the study was traditional.  What was done was a one‑week run‑in vehicle control, two weeks treatment, two doses of a reference product, two doses of the test drug and collection of the diary.  Patients were evaluating their nasal symptoms scores and non‑nasal symptom scores in a very classical way on a scale of 0 to 4.
We compared the overall results for two weeks to the baseline.  So, in this particular study, the total nasal signs and symptoms expresses a percent mean change from baseline for the two weeks of treatment, for the lowest dose of tested drug, showed 21 percent improvement over baseline.  The reference product showed 22 percent improvement.  The high dose was 33 percent versus almost 31 percent for the reference product.
Now, for any physician looking at this, it will be quite sort of intuitive to say that, obviously, they are exchangeable or substitutable products since the efficacy there is quite close, or very close to each other.  If they would be any closer, I would suspect that the data was cooked up.  So I think that, in real life, that is exactly what we see.
As you see, the differences were not too big.  However, because of variation of the methodology and so on, we have quite a bit standard error.
Now we applied, as requested by the FDA, the standards of bioequivalence for PK studies.  So it was a 90Êpercent confidence interval as determined, and it is supposed to range 80 to 100 of target parameters, our normally distributed data.
[Slide.]
So, even with the therapeutic equivalence, the very close efficacy of this product, when compared, the confidence intervals were nicely distributed around 0, ‑8.3 to 6.2, but the at the 20 percent plus or minus as expressed as the delta 0.2 times reference product, the product did not make exchangeability criteria.
So the decision resulting from such a study‑‑by the way, both of the doses showed statistically significant differences compared to vehicle or placebo.  There was no significant difference between the doses for most of the parameters.  Still, this product would not, in some way, meet the exchangeability criteria.
My last suggestion is that the bioequivalence standards for PK studies should not be straightforwardly applied to in vivo trials and there should be some deliberation on what kind of standards should be developed for the in vivo trials.
Thank you very much for your attention.
DR. LEE:  Thank you very much.  At this point, I would like to announce a change in the program.  Dr. Hartmut Derendorf also has to take an early exit, but I don't think he is going to Lubbock.  He is going to talk about PK and PD studies for systemic exposure of locally acting drugs and, of course, the academic view. 
Hartmut, I would like you to remain for a few moments after your presentation since you probably won't be here to participate in the discussion.
PK and PD Studies for Systemic Exposure
of Locally Acting Drugs
An Academic View 
DR. DERENDORF:  Good afternoon.
[Slide.]
It is a  pleasure for me that I have the opportunity this afternoon to address some methods or some thoughts on how pharmacokinetics and pharmacodynamics may help us to address bioavailability and bioequivalence issues of locally acting drugs.  I want to point out that this presentation is jointly prepared by myself and my colleague, Gunther Hochhaus at the University of Florida.
[Slide.]
I would like, also, because of the limited time, only focus on inhaled corticosteroids and remind you of the scenario that we have.  We administer the drug by inhalation.  Our target site is the lung and this is where we have our local delivery.  This is where we want to drug to go to.  But we are all aware that then, after it has been active, it goes into the systemic circulation where it can cause systemic side effects.  Of course, we have another entry via the GI tract where the drug can enter and we also have to address the issue of first‑pass inactivation during absorption.
[Slide.]
So what we want to achieve in our treatment is a targeted activity with high local pulmonary activity and reduced systemic side effects.  If you translate that into features of inhaled corticosteroid, that means we want to keep the drug in the lung as long as we can.  We want to have low oral bioavailability so the portion that is swallowed shouldn't go in and what gets in should be cleared quickly by high systemic clearance. 
Furthermore, we want to have high plasma protein binding because only the unbound concentration will be systemically active.  So these would be desirable features.
[Slide.]
I want to break down my talk into four parts to show you how we can use both PK and PD to assess both systemic and local exposure because when we talk about bioequivalence, what we want to achieve is equivalent rate and extent of both systemic and local exposure.  That is our goal.
[Slide.]
So let's start out with PK as a measure of systemic exposure.  This is really the easiest part of the talk because we can directly measure the plasma concentrations of our compounds.  We have really benefitted from advances in improved analytical sensitivity recently so that we can, in most cases, measure our drug concentrations directly.  For the assessment of systemic exposure, the route of absorption is irrelevant.  It doesn't matter where it comes in through the lung or the gut.  We are only interested in blood levels to assess safety.
[Slide.]
Just to show you some examples.  This is one of our assays on fluticasone propionate where we now can measure concentrations of 10 picagrams per milliliter in serum.  As you can see, there is still room for improvements, that we can go to even lower concentrations.  And it is possible today for all inhaled available corticosteroids to measure systemic levels.
These are some examples; fluticasone propionate, budesonide, triamcinolone acetonid and flunisolide resulting plasma concentrations after inhalation.  I know that Les Harrison is going to show you some BDP data later on.  So, really, that is pretty straightforward.  We can measure the systemic concentrations directly.
[Slide.]
We can also do that, by the way, after nasal administration where the concentrations are lower.  This is some of our data on fluticasone propionate, two different doses and the resulting concentrations that we observe.
[Slide.]
When we move on to pharmacodynamics as measure of systemic exposure, there are several methods that are used, most frequently cortisol.  Cortisol is a good parameter because it is sort of the common currency of different corticosteroids so it allows us to compare systemic exposure from different steroids.  
We have to watch out for the method that is used.  It is very important that the correct method is 24‑hour serum cortisol at steady state has been proven as the most sensitive parameter whereas the other methods that are around, like 24‑hour urinary cortisol or, particularly, 8:00Êa.m. serum cortisol clearly are inferior in detecting the differences.
ACTH challenge is a different approach.  It really doesn't measure the cortical suppression but it measures the responsiveness of the HPA access which really is not a major issue for the modern inhaled corticosteroids.
Other approaches are blood cells, which I will cover, and growth.  Growth sometimes has been reported as being more sensitive than effects on the HPA axis.  However, I am not convinced of that because it depends, again, on the method that is used for the assessment of the HPA axis.  I believe, if it identify one correctly, then there will also be an effect on cortisol that can be observed in these situations.
[Slide.]
We need to keep in mind that a steroid is not a steroid, but that they have different receptor affinities.  This is a comparison of the commonly used compounds.  They are relative to dexamethasone which is 100.  We can see that they vary quite a bit, keeping in mind that BDP, beclomethasone diproprionate, is an inactive pro‑drug and is converting through the monoproprionate which, then, is a very potent steroid, and fluticasone is about 18‑fold more potent at the receptor site than dexamethasone.
So that needs to be taken into account.
[Slide.]
This is some recent data from our group comparing different treatments of budesonide and fluticasone in different doses.  On the top, you have the single‑dose administration.  On the bottom, you have steady‑state data after five days.  And, in both cases, you have, in red, the placebo cortisol concentrations over 24 hours and, in white, the treatment group.
First of all, you see that, overall, the suppression of serum cortisol is relatively small.  If you use the areas between the curves as a measure, we find, here, the respective percent suppression for the various treatment.  They are statistically significant but, overall, they are relatively minor.
[Slide.]
One can, then, further go ahead and analyze these cortisol suppressions.  We have developed a mathematical approach where we can model the circadian rhythm of cortisol.  This is baseline data of healthy subjects; 1 day, 2 day, 3 day.  Baseline, you see nicely the rhythm.  And you see the line which is drawn by the equation that was modeled and fits quite well.
It can also be used to mathematically express the degree of suppression that we get with this equation and this model, then, allows us to predict how much cortisol suppression we would observe for various treatments.
[Slide.]
This is a study that we have done some time ago on triamcinolone acetonid after intravenous, oral and inhalative administration.  We measured the kinetics and, based on the blood levels, then, model the effect on cortisol and just see, for all three treatments, that it works very nicely.  The dashed line is the baseline pre‑dose line and the other line is the suppressed curve measured and modeled.
[Slide.]
So this allows, us, then, then to make pretty good predictions, basically to translate the pharmacokinetics of the inhaled steroids, the serum concentrations, into the expected degree of cortisol suppression.  Again, the best cumulative parameter to summarize this data is the AUC between baseline and treated group and express it in percent.
[Slide.]
We have developed a spreadsheet, an Excel spreadsheet, that puts all this information together and one location and one can, then, enter the drug, the dose, the time of dose, which is important, and the device.  Based on population average parameters, the program then will calculate an expected cortisol curve over 24 hours, calculate the percent cumulative suppression.
[Slide.]
We have, then, compared these predictions with a number of studies.  All of these dots here represent major clinical studies that were not done by us.  These are studies taken from the literature.  We have shown a nice correlation between the predicted cumulative cortisol suppression based on the model that I have just shown you and the measured and reported data in the literature.
So, really, all we are doing is we are translating the kinetic information into the dynamic information and it is quite consistent.  That makes sense because corticosteroids all work the same way.  They have the same exact mechanism of action.
[Slide.]
Briefly, on other systemic‑dynamic parameters, lymphocytes, the number of lymphocytes go down.  This is from the same study that I had shown you earlier, 200 and 500 micrograms of fluticasone, single dose and multiple dose.  You see, in red, placebo and, in black, the treatment group.  Again, there is a significant decrease but of small magnitude.  
[Slide.]
Only for the higher doses was it significant.  The same was true for flunisolide, here.  
[Slide.]
Similar results can be observed on the granulocytes, where we have the opposite effect.  The number of granulocytes goes up, dose‑dependent.  This is flunisolide data here, 400 and 1000, single dose and multiple dose.  In only this case, in the multiple dose situation, was it significantly different.
So, again, really, these parameters allow you to compare different compounds but they do not really improve the information that we get on systemic exposure if we compare two different formulations of the same compound.
[Slide.]
So, if we summarize this, if the issue is bioequivalence, we want to compare two formulations, we should simply take the plasma concentrations and no other data is really needed.  Whereas, if we want to compare different steroids, that is a different question.  And then 24‑hour serum cortisol at steady state seems to be the parameter of choice.
[Slide.]
Now, let's move on the real hard question, and that is local exposure.  How can we express local exposure.  How can we measure how much drug gets to the site of action in the lung.  There are two different approaches.  One is direct and one is indirect.  Unfortunately, the direct measurements are limited to animal experiments.
Lung microdialysis is a very promising new technique where one can put a probe into the lung and measure directly the unbound concentrations.  We are doing this right now in animal studies and getting very nice data but this is, unfortunately, not applicable to human studies.
Pulmonary‑receptor occupancy; same issue.  One can measure the steroid occupancy with binding assays.  Again, unfortunately, this is only possible ex vivo and in animal studies.
The third direct way is gamma scintigraphy where one can follow the label.  However, that has the downside that one would no longer study the original formulation but only after manipulation and introducing the label.
So we are really limited with indirect measurements but I hope that I can show that they may be of value.  One way to go is to look at the pulmonary absorption profile after application of charcoal and deconvolution.  So let's look at that a little more in detail.
[Slide.]
We have to keep in mind some basic pulmonary delivery concepts.  First of all, before the drug is active in the lung, and the steroid needs to be dissolved and get into the cell, the receptors on the cytosol.  Also, only unbound fraction of the drug is active, so binding is an important factor.
Then, all of the drug that reaches the cytosolic steroid receptor then will be absorbed systemically.  So, of all the components that are available so far, they will not magically disappear but, then, the next step is systemic absorption so one can really not have one without the other.
Thirdly, one should be very careful in looking at the literature not using total tissue concentrations, not using biopsy numbers, because they are hybrid numbers and reflect the sum of the drug in the tissue and are very difficult to interpret quantitatively.
[Slide.]
Furthermore, it is important to keep in mind that the delivery efficiency is a very important factor.  This is a simulation to show that if you increase the percent of the drug that is delivered into the lung that, then, the targeting will be improved.
If you compare two compounds with an oral bioavailability of 10 percent, drug A, and an oral bioavailability of 0, drug B, then, if 10 percent gets into the lung by our device, that means, then, the systemic variability for compound A will be 19 percent, 10 percent coming from the lung and 9 percent is 10 percent of the remaining 90 percent, so that adds to 19 percent, whereas for compound B, it is only the 10 percent that goes to the lung which is a ratio of 1.9.
If you improve, for the same compounds, the delivery to 30 percent and do the same calculations, you end up with 37 percent systemically and 30 percent, and that is a ratio of 1.2.  So the oral bioavailability becomes less of an issue the more you deliver to the lung.  That makes sense.
[Slide.]
So how can we differentiate, when we inhale, how much of the drug goes in through the lung versus the GI tract.  There are three approaches.  One is to simply use a drug that doesn't have any GI absorption.  That is, of course, the easiest way.  Or, if it is to block the GI absorption with charcoal, the third approach in the literature is utilize early time points where the pulmonary absorption is dominant and oral absorption is not yet very large.
[Slide.]
Fluticasone propionate will be an example for the first case where we have oral bioavailability of less than a percent, or around 1 percent, so one can assume that the vast majority of the drug that shows up systemically comes from the lung.
[Slide.]
Charcoal has been used to separate the route of entry.  This is a study on budesonide where two devices were compared, Turbuhaler MD‑MDI, with and without charcoal, and, by calculation, then of the different profiles, one can show that the contribution from the lung and the GI is different. 	[Slide.]
This is the Turbuhaler here and we see that the fraction that is coming through the lung is much, much larger than from the MDI by comparison of the absorption with and without charcoal.  If one does it right, if the charcoal application is optimized, one can almost completely, or completely, block, the absorption.
[Slide.]
This is an example from not a steroid but terbutaline.  Oral bioavailability with and without charcoal where it was possible to block the oral route almost completely.
[Slide.]
So, what one can do now is to really find out how the drug enters the body and also describe the time course of absorption and, thereby, the time course of pulmonary residence, is to use old‑fashioned pharmacokinetics.  What is needed for that is a good characterization of the disposition which one only can get by an intravenous study.
So, if an intravenous study is done and the kinetic parameters are determined, like in this example of fluticasone propionate, and then a clearance of distribution is calculated after IV administration, then an inhalation experiment is made and, again, the kinetic parameters are calculated.  
This is here an example from several studies from our group and others on the resulting profiles.  And then you can use pharmacokinetic concepts such as deconvolution.
[Slide.]
This is one example, the so‑called Loo‑Riegelman method, allows then to calculate an absorption profile which is the rate of entry.  If you block with charcoal, or have a drop that is not already absorbed, it is equal, then, to the pulmonary residence time‑‑
[Slide.]
‑‑you end up with a profile such as this absorption profile.  It is percent absorbed versus time that allows you, then, to characterize and compare, in this case, different compounds but also different formulations of the same compound and would mirror, then, what is going on in the lung.
[Slide.]
One noncompartmental way to express the same data is to use a mean‑residence‑time approach where, again, you need the intravenous data, calculate the mean‑residence time after intravenous administration and after inhalation.  If the inhalation is only through the lung, then the difference between the two will be equal to the mean absorption time or also equal to the mean pulmonary residence time.  You would have a quantitative way of comparing.
[Slide.]
The fourth way, and the one that we have heard in the previous presentation, is to use pharmacodynamics as a measure of local exposure.  I think the data that we have seen so far are very discouraging because, for the small differences that we want to detect, particularly in bioequivalence studies, the variability is very high and, therefore, the ability to discriminate between products is limited.
Another way to go would be to use surrogate endpoints.  There is a lot of work that is being done right now to identify surrogate endpoints for steroid activity and it is the big hope that, one of these days, we will find one that is really reproducible and can, early on, tell us what we can expect after chronic use of the drug.
But, so far, unfortunately, there is none available that is really of that quality.
[Slide.]
So, I believe, right now, pharmacokinetics is our best bet and I believe that it is much more than just a measure of systemic exposure, that if you use kinetics correctly, that it is able to give us information about the local exposure as well so that one approach to tackle bioequivalence problems with inhaled corticosteroids is, first of all, of course, to have in vitro studies‑‑and that was the discussion we had this morning‑‑to come up with good criteria about in vitro equivalence.
After in vitro equivalence is shown, then follow up with in vivo studies where equivalent systemic exposure needs to be shown, and that can be done by just measuring plasma concentration, and equivalent pulmonary‑absorption profiles that can be shown by showing equivalent absorption profiles using deconvolution methods.
Obviously, the details would need to e worked out and, very difficult, the goalposts need to be defined which will be quite a challenge.
[Slide.]
Would like to close by thanking all the people who have contributed to this data and thank you for your attention.
DR. LEE:  Thank you, Harmut.  I just wonder whether or not the subcommittee members have any questions.
I wonder whether or not you have looked at the questions for discussion.  Maybe, in a way, you have answered this question, which is, are there situations where the in vitro data plus systemic PK and PD data can be relied upon to assure local drug delivery for either nasal or inhaled drugs?
DR. DERENDORF:  Yes; I believe that that may be possible, that if you use the data well, that we can make the conclusion that there will be sufficient‑‑unfortunately, we don't have any better ways right now to approach this.  If we would have a surrogate marker, a pharmacodynamic surrogate marker that would be easily quantifiable for the local activity, certainly that would be even better.  But it is not out there.	
So I think what we have right now, this seems to me the best approach.
DR. LEE:  Were all the data you presented human data?
DR. DERENDORF:  Yes.
DR. LEE:  Thank you.
DR. BEHL:  One quick comment.  Even if you have some data that goes to the correlation of PK and PD, it is very hard for me to believe that PK can be used as a means to judge local effect.
DR. DERENDORF:  What we are trying to do here is‑‑we have two different issues here.  If it comes to bioequivalence, the traditional, or the approach that it used with bioequivalence for systemic drugs is if we show equal or equivalent exposure within a certain range with certain confidence, then we imply that the effects will be equivalent.
This is exactly the same approach that I would propose here.  There is no need, in my opinion, for clinical studies.  The systemic equivalency can be done just by traditional comparison of plasma concentrations.  The more difficult part is the indirectly characterization of the local exposure that could be done by absorption profiles.
So, just as with any other bioequivalence, really, there is no need to measure any kind of pharmacodynamics if you follow the same logic that the same exposure profile will result in the same effect.
DR. AHRENS:  You made the statement that systemic exposure, if you are using the same compound that you are inhaling, that you only need pharmacokinetics, which makes great sense to me, perhaps, except for beclomethasone because of the fact that there is more than one active species and the absorption profile, the plasma profile, for BDP may not be same as between products and BMP.
How would you handle that?  Can you do that with kinetics alone?
DR. DERENDORF:  Yes; I fully agree with you.  I think the concept still holds.  It is more complicated to do because you have several players which you need to interpret and measure all of them and then add up the numbers.
Again, this is not new.  There are other drugs for systemic use where you have active metabolites and the same issues exist there in bioequivalence situations.  But, yes; of course, you would need to measure the active metabolites and interpret them correctly.
DR. LEE:  I think that we ought to let him go.  We will come back to this towards the end.  Your taxi is waiting for you out there.
Let me talk about a few logistic changes because of the shift in the program.  We will now take a break and I  would like to come back at 3:25, about ten minute.  When we come back from the break at 3:25, Dr. Richard Ahrens will be presenting his view on clinical studies for local delivery of orally inhaled corticosteroids.  And then the surveillance will go into discussion.  Then we will come back on line with the rest of the schedule.
Thank you.
[Break.]
DR. LEE:  Before I turn the podium over to Dr. Ahrens, I would like to alert the clinician members of the committee to begin thinking about addressing the three questions in the upcoming sessions.
Dr. Ahrens will be talking about clinical studies for local delivery of orally inhaled corticosteroids.
In Vivo BA and BE:
Clinical Studies for Local Delivery 
of Orally Inhaled Corticosteroids 
DR. AHRENS:  Thank you.  
[Slide.]
This is the point where I am, of course, supposed to say I am happy to be here.  It is particularly true this time because the topic I am talking about here is an area that I have been interested in for more than a decade in terms of the general issue of the assessment of bioequivalence of inhaled drugs used to treat asthma.
[Slide.]
The task of these clinical studies that I have been asked to address is, first of all, to address the issue, predominantly, of generic equivalence to determine whether the innovator and generic inhaled corticosteroid deliver bioequivalence quantities of drug to the site of action in the lungs.  In other words, even if there is a difference in quantity delivered to the site of action, that that quantity is not large enough to make an important clinical difference.
The same approach to addressing this certainly could also be applied to reformulations that are not intended to be pharmaceutically equivalent, although, there, it would be determining rather than proving bioequivalence, determining the ratio of doses that are required to deliver equivalent quantities of drug to the site of action. 
For example, if an FHA inhaler delivered two or three times as much drug to the site of action as an innovator CFC preparation, that may well be okay and approvable but, still particularly important, at least in my opinion, that the clinician knows what that ratio is so that he or she can adjust the dosing strategy appropriately.
[Slide.]
So the concept of how to address this issue of bioequivalence.  As we have heard earlier, there is a lot of variability in clinical studies, particularly with inhaled steroids, addressing the issue of bioequivalence.
The typical approach that I think is now reasonably well established in precedence, at least with beta agonists, is to look at formulations along the dose axis rather than the response axis; that is, rather than looking at a comparison or responses, are they equal or not to a given dose level, coming up with a ratio of doses that are likely to produce equal responses.
This is, in essence, using a pharmacodynamic response, in this case a clinical outcome, to bioassay the quantity of drug delivered to site of action.  So, in a sense, I have difficulty separating, in this context, the issue of clinical study from a pharmacodynamic study because they are getting after, in a sense, the same thing; are we delivering, within the realm of not making a bit clinical difference, an equivalent quantity of drug to the site of action.
[Slide.]
This issue of using the dose axis rather than the response axis is something that has a history relating to albuterol, generic albuterol inhalers.  Albuterol, of course, went off patent in 1989.  The initial studies that were done to try to address bioequivalence of inhaled albuterol, in fact, did look along the response axis figuring that if you showed equivalent response to two different inhalers at the same dose that they must be bioequivalent, looking, in this case, at bronchodilitation.
At the prompting of some people, including myself, a control was put in these studies where the innovator product had more than one dose level.  Then, in 1992, when everybody got back together to look at the results of those studies, in horror‑‑Wally, I hope you agree with this history; this is at least my interpretation of the history‑‑to everyone's horror, they discovered that the studies couldn't discriminate between different dose levels of the same product.  And, if they couldn't do that, then how could they be expected to differentiate different doses delivered by different products.	
That led to a search, more advisory committee meetings and a search over the next several years for acceptable and valid methodologies to accomplish this and, ultimately, to the approval of the first generic inhaled albuterol, at least in terms of the in vivo study, a bioassay study, demonstrating bioequivalence.
[Slide.]
So the concept here, in essence, is to perform more than one dose level to at least on preparation, preferably both.  In this case, this is a so‑called two‑by‑two bioassay because there are two doses of each preparation.  And then, instead of looking at a comparison of responses, to look, essentially, at the distance between the dose‑response curves.  
This is the ratio of doses that will produce an equal level of effect.  If it takes twice as much to produce the same effect, that test preparation would, therefore, be half as potent.  
This is an old concept dating back, probably, to the forties.  With well‑established validity criteria, you have to have a significant dose‑response curve, dose‑response relationship, to act as a standard curve.  If you don't, you don't have a valid bioassay.  You need to have responses that are in the same region‑‑that is what this means‑‑and the response curves, of course, need to be parallel.
With two inhalers delivering the same product, at least, hopefully, you would expect that to be the case.
[Slide.]
We applied this approach, in my laboratory, to Baker Norton's product.  This is the product, the first generic albuterol that was approved, using these data as the in vivo demonstration of bioequivalence.
We looked at a response to histamine bronchial provocation, PC20 FEV 1 versus dose, down here.  As you can see, the curves largely coincide.  And then we applied bioassay statistical methodology to come up with a confidence interval.  As you can see here, that was 0.69 1.40.  This met the concurrently established bioequivalence criteria of being between 0.67 and 1.5, essentially between two‑thirds and one‑and‑a‑half times as potent since the entire confidence interval was within that range.
[Slide.]
That was the analysis that we did using so‑called Finney bioassay.  The FDA, I think for very good reasons, chose a somewhat different statistical approach which they called a dose‑scale approach.  We used two doses of each preparation.  They, instead, chose to use the reference as a standard curve and then compare one dose of the test preparation at a time.
The curve‑fitting methodology was a little bit different.  The method for establishing the confidence interval, we used Fieller's theorem as part of Finney bioassay which relies on normal theory whereas they chose to use the somewhat more robust methodology of bootstrap which doesn't require normality.
It was comforting to find that the results by these two methodologies were virtually the same.
[Slide.]
So that is the concept, at least as it was developed in addressing inhaled albuterol.  Now the problem at hand is that this kind of bioassay approach has rarely been applied to inhaled corticosteroids because it is not very easy to do.
Furthermore, when it has been applied, it has really met with very limited success.  On the next slide, I will give you an example of that.  
[Slide.]
This is, in my humble opinion, the best bioassay study, if you will, comparing inhaled corticosteroids.  It was a study done by 3M in the approval process of their HFT BDP product comparing it to CFC BDP.  This was an exceeding rigorous clinical trial, clinical study, which involved hundreds of subjects each of which came to the clinic at least five days a week to perform lung functions in many ways that are beyond that.  It was an extremely rigorous, carefully done study, not a typical study.
They did succeed in estimating a potency, a dose ratio, that each microgram of HFA is equivalent to 2.6Êmicrograms of the CFC product.  However, if you look at the confidence interval, it stretched from 1.1 to over 11.0, essentially, not a very clinically meaningful confidence interval if it is over a ten‑fold range.
The fact that it didn't encompass one indicates that, at a statistically significant level, these two preparations are not equivalent.  It does not, even with the best study that has been done to date, give you a clear answer as to exactly what that potency ratio is, what the bioequivalent dose is.
[Slide.]
This issue has been addressed by a number of people, but, in this recent review by Peter Barnes and Bill Busse and Soren Pedersen, first of all, they noted that this was true that, in spite of dozens of studies that are in the literature, it is very difficult to draw firm conclusions comparing comparative efficacy.
It is hard to say that anything is different from anything else in the way studies have been done and relate that to things like study designs, control over important confounding variables and, last but not least, the lack of a strong dose response relationship for inhaled corticosteroids.
[Slide.]
That leads to the common wisdom that the problem with inhaled corticosteroids is that the dose‑response curve is just so darned flat that you really can't detect any differences.
If this is really true, that the dose‑response curve is that flat then, first of all, the dose delivered really doesn't seem to matter much, clinically.  If you can't tell a difference in dose, that may mean that the dose doesn't really matter.
If that is true, then you don't need to, and, in fact, can't really do a valid bioassay using the concept that I just described simply because you don't have a valid significant dose‑response curve.  Therefore, you don't have a valid bioassay.
[Slide.]
You could, therefore, simply rely on clinical trials going back to the concept of comparing responses. By the way, this "no" doesn't belong there.  My apologies.  Cross that out.  You could use a clinical trial showing that two formulations yield similar responses.
That is in fact what was done.  This is a recent publication which was presenting data that was at least part of the basis on which a Baker Norton HFA product was approved in the U.K.  You can see, they studied morning peak flows here in a group of mild, not so well‑controlled, asthmatics and more severe asthmatics.  These are actually two different studies, hence the different doses of BDP.
You can see that there are no significant differences in response.  Unfortunately, failure to show a significant difference in response is not at all the same as proving the two things are the same.  This has lead to a good deal of consternation, I am told, by Dr. Ganderton and others in the U.K. as well as what I understand is a record number of letters to the editor to respiratory medicine particularly when this study and the fact that the Baker Norton product was approved on a one‑to‑one, a one inhalation equals one inhalation basis, with the next study.
[Slide.]
This will be familiar to you.  This is the 3M study I showed you earlier which shows a difference, a significant difference, in potency.  I have it on good authority, although I could not vouch for it myself, based on what was in those letters to the editor and other discussions that have taken place, that these two inhalers are very close to being pharmaceutically equivalent.
If that is true, that is what has led to the consternation in that how could one be approved on a one‑to‑one basis and this one be approved in the U.K. on a two‑to‑one basis, two inhalations of CFC is equivalent to one inhalation of the HFA.
[Slide.]
That leads me to the following question.  That is all based, by the way, on the fact that the dose‑response curve must be just so darned flat.  That is what the letters to the editor really addressed, that you really can't do what the Baker Norton study did but there isn't, necessarily, a good way to get around this problem.
I might just stop here at this point and say what everybody says, it is just really hard to deal with inhaled steroids so clinical studies may be not that useful.
I would like to now ask the question is it really true that inhaled steroid dose‑response curves are just that flat.
[Slide.] 
What has got me originally thinking about this is what I will label here as the asthma clinician's paradox.  That is, in clinical studies, multiple clinical studies, with hundreds of patients, it has been very difficult to show anything but a very flat‑‑dose‑response curves that are flat to nonexistent.
On the other hand, clinicians think they see inhaled dose‑response relationships in the clinic every day in individual patients.  Both of these cannot be true.  One says there is no dose‑response relationship.  The other says, at least in some patients, there is an important, clinically important, dose‑response relationship that we all see.
[Slide.]
So the question is, which is allusion and which is reality.  I would like to make a modest proposal here, for the remainder of my talk, as to what the answer to this may be.  I will start out with this quote: "Good judgement comes from experience.  Experience comes from bad judgement."  I think we have all experienced that from time to time.
"Furthermore, experience teaches you to recognize a mistake when you have made it again."
[Slide.]
What I would like to propose to you here is the that the typical inhaled corticosteroid study design which has been used over and over again with various modifications but the same basic theme, in dozens of studies, is a mistake that we keep repeating over and over again.
The typical study, as most all of you will recall, is a parallel group study lasting varying lengths of time but at least for a month or two.  The general approach is that you take baseline data in poorly controlled asthmatics and then do something that none of us really do very often as clinicians, is just place those poorly controlled asthmatics on a inhaled steroid and wait for them to get better of subsequent weeks, and then measure their response.
This is marked by very high variability in response, the very shallow dose‑response curves that we have talked about and a tremendous reluctance to do crossover studies because of the prolonged treatment time potential carryover.
[Slide.]
Let's take a little further look at just what it would take to do a good bioassay study using this kind of methodology.  I am not going to go into detail at all here although, perhaps, Dr. Hauck and others would like me to as to what methodology we use to do sample size, statistical power and sample‑size calculations.
For here, it is not a straightforward matter but something that there have been some recent publications on and my biostatistitican, Dr. Singh Ho Hahn and I, have done some work with.  But, I will suffice it here to say that it is not related simply to the variability of the response.  It is also related to the steepness of the dose‑response curve and, in fact, those two things do not function independently.  They function as a ratio.
The smaller that ratio, the more powerful and discriminative the study.
[Slide.]
So we did some sample‑size calculations with assumptions that we were going to try to demonstrate using two one‑sided hypothesis testing, that a generic between 0.5 and 2.0 times as potent as the innovator, typical alpha and power level using a two‑by‑two bioassay study design, two doses of each preparation.
[Slide.]
Here is the S over B ratio that I talked about earlier.  Sample size calculated‑‑this is using the data from that 3M Busse study‑‑looking like it would take in the neighborhood of a thousand patients to really fulfill that goal.  This was an exceeding rigorous and difficult study to do.  I don't know for sure where typical studies are, but they are probably‑‑most other studies are up here someplace.
That is not a very practical number of patients to do, particularly if you are a generic company trying to get a product approved.
[Slide.]
Because of this problem, we launched into a pilot‑‑well, actually, we did a number of pilot studies searching for the holy grail of a better outcome measure that would give us greater reproducibility.  This was part of that series.  It was a study that we did in collaboration with 3M using their HFA preparation.
We did it as a crossover study and we justified that in terms of dealing with carryover by the following, by giving a prednisone burst at the beginning of the study to essentially maximize carryover in patients, give everybody, as near as we could, maximal steroid effect by maximally improving their asthma.
That, in fact, models what clinicians do all the time.  If we see a patient who is in trouble with their asthma, we generally give them a course of oral steroids and then start them on their inhaled steroid to maintain that control.
We then looked at the stability of asthma over the subsequent three weeks, looked at virtually every outcome measure, again, in search of that holy grail, that we could think of expressed in every way we could think of for a total of 58 different outcome variables searching for the lowest S over B ratio.
[Slide.]
What we found was that, in this model, some outcomes were terrible.  There are some that are even up here further, but look at the computed sample size here over S over B ratio.  We are up in the thousands.  
I had this great idea that if patients got up at 4Êa.m. routinely, set their alarm and did their peak flow meters, that that would model morning dipping and that would give us a good response.  Not only did it not work, it got a lot of patients angry with me.  That wasn't a very good idea.
Here, you can see maybe your other favorite outcome, and there are others on this list, that didn't work very well.  I would now like to zoom in, down on this corner down here.  
[Slide.]
Computed sample size; now, we are dropping down to 100 or under.  To my surprise, the best outcome measure was, essentially, morning home spirometry.  We had a portable electric spirometer that gave us FEV 1, FEF 25, 75, as well as peak flow.  I am not sure that those differences are so really important as the fact that it was upon‑awakening morning spirometry that turned out to give us the best power.
But the remarkable thing is that it appears, in contrast to that Busse study I showed you earlier, you could achieve this sort of thing with 100 subjects or under.
[Slide.]
Now, I think, the most important issue with this.  Here are those same points that I just showed you down here with the best outcome measures.  Here are the same outcome measures using the same pilot data but now doing sample‑size calculations for a parallel study, as if we had done this as a parallel study rather than a crossover study.
It become apparent to me that these data suggest, at least, that the problem with the traditional study that we have done‑‑there is the Busse study right in the middle of it‑‑of the outcome measures that turned out pretty good in a crossover study.
The real study is not the outcome measures.  The problem is the study design.  It is a parallel versus a crossover study design.
[Slide.]
We are not the only ones to have stumbled upon this although I think the implication has not been quite so clear.  This is a study done by Soren Pedersen a few years ago looking at exercise‑induced asthma in children, again, a small number of children, yet got a highly significant dose‑response relationship.	
By the way, in that study I just showed you with those best outcome measures, the dose‑response relationship is with only twelve subjects.  It was less than 0.0001.
There is nothing new under the sun.  It is not exactly a great revelation to Dr. Hauck that crossover studies are more powerful than parallel studies.  That is old as the sun.  So that is not exactly a revelation and yet I think what is a revelation is that, in no place does it seem to be more true than in dealing with inhaled‑steroid dose‑response relationships and comparison of preparations.
I think this may be the answer to this asthma clinician's paradox that I showed you earlier.  It may well be that there are very real dose‑response relationships in individual patients, that we are not deluding ourselves when we think we see that.  What these data suggest is that that very real response, in at least some patients, gets lost in a great deal of interpatient variability between asthmatics which we, as clinicians, also, all know is there.
[Slide.]
So, in summary, the task has been to develop a capable method of demonstrating in vivo bioequivalence for inhaled steroids.  The concept I have presented is to use a clinical bioassay dose‑axis comparison.  The common wisdom is that the dose‑response curve is just too darned flat to really do this.  I am presenting an iconoclastic potential alternative that the real problem may just be that you can't do this if you are going to persist in doing parallel studies.
[Slide.]
The solution may be to not say we can't do crossover studies because of carryover.  For heaven's sake, most parallel studies have to deal with carryover because many of the subjects came into those studies already on inhaled corticosteroids.  There is carryover there to begin with.  So it is not a matter of getting rid of carryover so you don't have to worry about it.  It is a matter of how to control it.
We presented at least one method by starting each treatment period now with a wash‑out but with a wash‑in, with a burst of oral steroids to minimize the potential for carryover to be able to do these crossover studies.
This should allow accurate assessment of bioequivalence if it really works with tens to hundreds rather than thousands of patients.  However, the proof of the pudding is in the eating.  This is just a pilot study.
There hasn't been a study that actually has accomplished this yet.  You will have to wait for that, this is the commercial message, it is no great surprise that I continue to look for additional opportunities, collaborative opportunities, to address this issue.
Thanks for your attention.
DR. LEE:  Thank you very much, Dr. Ahrens.  I invite you to take the hot seat and maybe entertain some questions.
Subcommittee Discussion
DR. LEE:  We now go into the discussion period.  There are two major groups of questions to be addressed following the two presentations, one on nasal aerosols and sprays and the other one on orally inhaled corticosteroids.
In order to keep the momentum, let me propose that we take on the second group of questions first which is, "Clinical studies for local delivery of orally inhaled corticosteroids, ICS."  These are two questions that the agency would like to get input on.  
The first one is, "A number of approaches have been proposed to assess bioequivalence of ICS."  You have heard some of those.  The question is, "Are any of these study designs proven to offer better discrimination in terms of dose‑response sensitivity?"
Here, we will look to the clinician colleagues on the committee to guide us.
DR. LI:  I will make a comment.  I appreciate the discussion that we have had so far.  I think that one of the issues with the dose‑response for inhaled corticosteroids has to do with the usual doses at which we conduct our studies and the usual doses that we use in clinical practice.  Those doses tend to be on the high end where we get a maximal or a near maximal benefit.  That may be clinically appropriate.
But I think that the differences between preparations or formulations may be more apparent at the lower end of the scale.  So one of the suggestions that I have heard that makes some sense is when conducting a dose‑response study to use lower than usual doses to see if differences can be apparent at the lower doses rather than at usual doses.
DR. SZEFLER:  This is an area Richard and I and a number of people have discussed for a number of years.  The markers that seem to be attractive are exercise‑induced asthma, exhaled nitric oxide and then I would toss in a third one which would be bronchodilator reversibility.
We are in a process now with the Asthma Clinical Research Network of testing these in kind of a different model.  It is a little bit different than what Richard proposed and we are trying to match up efficacy measures with levels of cortisol suppression because, I think as you pointed out earlier, you could do one measure and compare it to another.
We kind of took the position of trying to assess each of the individual steroids for levels of cortisol suppression and then take those doses for given levels of cortisol suppression and look at efficacy.  We are now kind of ending the third phase of that trial and we will have some answers on that.  But at least looking at the literature, the things that seem to be appealing are the study that Richard mentioned in terms of exercise‑induced asthma.
Exhaled nitric oxide, there are a few studies on that, but people don't look at that as kind of a‑‑it is not acceptable yet as a surrogate marker of inflammation.  It is a nice bioassay, maybe.
The third one that I am kind of intrigued with looking at that seems to be surfacing as a potential marker of response is beta‑agonist reversibility.  So there are three that I think I would look at.  The other ones, like FEV 1 and peak flow, the changes appear to be small.
John Toogood, early on, had identified that very low doses can achieve your maximal effect but you need higher doses to get more at the exercise‑induced areas.  
If you look at other levels of reactivity like methacholine and histamine, you just can't differentiate changes.  That is probably because they are very slowly reversible.
DR. AHRENS:  The fact that, in what I just said here, I emphasized the basic study design, parallel versus crossover studies, doesn't diminish the importance of selecting the right patients and choosing the right outcome.  In terms of the issue that was just brought up about patients being often at the top of the dose‑response curve in typically clinically used doses.
For many patients, that is clearly true, even at the very lowest dose.  There are certain patients that it takes almost‑‑the biggest step in the dose‑response curve goes from nothing to something.  Once you get to that, anyplace else you go in the dose‑response curve doesn't have much effect.
For those patients, probably the dose‑response curve is very flat and the dose probably doesn't make a big difference.  A broad difference in dose delivered probably wouldn't hurt those patients.  But there are other patients where the dose‑response curve really is steeper.  For sake of time, I didn't go into the details for this pilot study, for example, what patients we selected.  But they clearly were steroid‑dependent patients in the sense that they were not well‑controlled unless they were on substantial doses of inhaled steroids.
There are clues in other studies that have been done that those patients are not a tiny minority, though, that there is a substantial percentage of those patients around.  It is those patients we need to protect by having properly done bioequivalence studies and, therefore, it is those patients who really ought to be the subjects for those studies.
So it isn't a matter of‑‑what you said, James, I think is correct that, for some patients, even at typical clinical doses, they are at the top of the dose‑response relationship.  But I don't think it is true for all subjects and we ought to be able to develop entrance criteria to studies to identify those patients during run‑in periods.
DR. SZEFLER:  There is some appeal, in terms of your model from the ethics standpoint and the availability.  We try to recruit those patients and they are a challenge to find because now of the extensive use of inhaled steroids and also because of some of the problems we run into with institutional review boards in terms of delaying treatment and observation period.
So the attractiveness of your model is that you can treat that right away and you can even take patients who are in inhaled steroids because you are escalating the treatment and then you are withdrawing and watching carefully.  So there are some ethical and, also, some availability in terms of numbers of patients to look at that offer some attractiveness in terms of your model.
DR. AHRENS:  Stanley, you brought up the issue of what outcome measures to choose.  I think that is also an important issue, that there are some outcome measures that are clearly much less discriminative than others.  
For what it is worth, it was only a small pilot study, but bronchodilator reversibility was one of the things that we looked at in the study and it didn't come out very well.  I was amazed that one of the simplest things, just doing morning peak flows, is what turned out to be as discriminative as anything.  We didn't do exercise in the study.  It is certainly more cumbersome to do, but, based on Pedersen's data, that also is right if you use his data to do the same sample‑sized calculations.  It is right in there with the morning spirometry, the peak flows that we looked at.
It is not a lot better, but it is not worse either.  I think it is an alternative kind of model.
DR. SZEFLER:  In our studies, we were really trying to answer the question, are steroids different.  I think the question we have facing us today is, given a steroid with a different delivery device, is it equivalent.  So it is kind of the opposite end of the spectrum.
So then you get into setting the goal posts with the model in terms of what is acceptable, tight goal posts or wide goal posts and everybody can kick a field goal.
DR. AHRENS:  I guess I would respond to that by saying the handling of those two situations is slightly clinically different but not that much.  In one, you want to make sure, based on two one‑sided hypothesis testing, that the confidence intervals are inside the goal posts.  For the other, you don't have goals posts.  You just want to know what the truth is.  If something is three times a potent, you want to know that but there, again, you need to have the same kind of confidence limits.
If you wanted to know between half and twice as potent in terms of proving bioequivalence, if something is three times as potent, you really want to know, okay, my estimate is three times.  Is it 1.5 to 6.0 or where?  You want to narrow the confidence interval on that estimate, wherever it is, to the point where it is clinically useful and not tenfold or more wide that isn't clinically useful.
DR. LEE:  Let me interject here and ask the question, is there any discriminatory study design known today?
DR. AHRENS:  Based on what I have presented here, It should be obvious my opinion is, in terms of things that have been done and proven and used in this kind of‑‑validated by having actually been used and been successful, no.  What I have been holding out here is that I think there may be some promise looking at things a bit differently than in the past.
But that still may be entirely doable.  But it has not been done yet, to the best of my knowledge, in a way that would be truly usable for bioequivalence.
DR. LEE:  So would question B2 be a logical follow up to that, looking for surrogate markers that might be sufficiently sensitive?
DR. AHRENS:  As I think I was just saying, my opinion to that is that that is an important thing to do but, in itself, insufficient.  We did a grand total of, I believe it was six pilot studies searching for that holy grail of what is the best outcome measure to make this problem go away.  It was in the sixth study, the sixth pilot study, that we finally came around to the idea that the problem really may not be the marker.  It is the basics of the study design.
That is important, but I think you are going to have to come up with a way to do a crossover study or you are never going to be able to use this in a bioequivalence kind of approach, in a bioassay kind of approach, like was done with albuterol, and apply that to inhaled steroids.
You can decide that you don't really want to do that, that it is more trouble than it is worth.  But if you want to do it, then I think that is what it is going to take.
DR. LEE:  Other points or opinions?  Could it be because we don't understand the disease stage well enough?
DR. AHRENS:  Stanley, do you want to respond to that?
DR. SZEFLER:  I think we understand its manifestation.  We don't understand totally its etiology.  But I think the variability is a problem.  We would like to think it is not product related and so we trust that the products that we are working with would limit that variability.  But I think it is the disease variability that makes it statistically hard to sort out.
DR. AHRENS:  An asthmatic is not an asthmatic is not an asthmatic even if you bring them into the study like is often done within the same FEV1.  Their FEV1 is between 50 and 70 percent of predicted, or whatever the criteria are.  You still, even though you do that, get some very different people in terms of their characteristics of their asthma and what apparently is the case in terms of their responsiveness of inhaled steroids in that kind of study.
DR. LEE:  Thank you.
Lester?
DR. HARRISON:  We certainly support the crossover study design as well.  It is very appealing to us and, based on our knowledge with BDP, it seems the way to go.  We would be very encouraged if you would‑‑somebody would‑‑actually do the real definitive study and prove the point.
We were involved in the pilot study but that needs to be taken the next step.  It did seem like it has the potential to actually discriminate.
DR. LEE:  Very good.  Wally, do you have the information you need?
DR. ADAMS:  Yes.  I think that is helpful.
DR. LEE:  Okay; great.  Let's move on and talk about question A1 and A2 which are similar to the ICS situation.  You may recall Dr. Roman's presentation about the three models and the same question was posed to see whether or not is it feasible to demonstrate a dose response for locally acting drugs using any one of these designs.
Let me see what the committee has to say.
DR. BEHL:  From what I have seen so far, and it is for question B1, B2 questions also on the steroids, the dynamics of locally acting compounds in the nose and in the lung are such that I don't see how one could replace the need of, if not a full‑scale clinical trial, then at least a bridging clinical trial to show that, after showing all the pharmaceutical equivalence of the product and other equivalence that can be easily shown, that the results of therapeutically equivalent.
I don't see what we know today, in terms of valid approaches which can be used in lieu of, if not a full‑scale, then a bridging clinical trial.
DR. LEE:  Any opinion?  Dr. Roman, you would like to respond?
DR. ROMAN:  Yes.  I agree, actually, that the crossover is a much better design in terms of controlling some of the variability.  However, I cannot, at the moment, imagine any crossover design for nasal‑allergy studies which are very much depending on season and shortness of it, if you wish.
Of course, the fact that the endpoint measurement is not so reliable as the pulmonary‑function test, the rhinomometries.  I don't know if it is in infancy or agony.  I am not quite sure which phase of this we are observing, but it is not a very reliable method so far.
So I would be very hard‑pressed to come with a design which will be as elegant and interesting as Dr. Ahrens.  Another thing if, if I may ask and think here, will this‑‑you see, the problem is that most of the reference drugs do not show much of the dose response in their full development programs.
So the companies will have to do the reference‑drug study tests as well as the dose responses in addition to the tested drugs.
DR. DALBY:  In the hierarchy of acceptable testing, you have the clinical test and, if that is found to be nondiscriminating, you drop down to the in vitro tests.  It seems to me, although, again, it is not, perhaps, a population thought that the intermediate ground is the scintigraphic study of deposition which at least incorporates the anatomy of the nose, and it is not such a big stretch, in my opinion, to go from if the drug is depositing equivalently in two formulations, that it will act equivalent and it is not necessary to fall straight down to the in vitro tests.
I fully accept the difficulty of validating the labeling technique, but, by the same token, if we are going to develop criteria that can find two products to be bioequivalent, as Andy said, that is no different than finding an innovator product to be the same as the labeled innovative product.
So, putting those things together and accepting that a nasal solution is probably the easiest type of dosage form to reliably radiolabel with a deposition marker, it would seem to me at least appropriate to consider scintigraphy as a measure of equivalence.
DR. BEHL:  It might be a case of trying to prove the impossible.  If the original innovator product in two different dose strengths was approved, of course, there is no burden on their part to show the equivalence or a good inherent dose response, for example, that has just been on the market.	
If they go back and do them again, and prove the dose response and prove that a 2‑mg dose is better than a 1‑mg dose on the market, they might fail in doing that now.  It depends on what the suggestion is how we are going to show the bioequivalence of the same dose but different formulations, or same formulations and maybe a different company.
Maybe we are asking to do something that is not doable.
DR. MacGREGOR:  I guess I have a couple of comments on what you said and that is to get a nasal product on the market, you had to run it against placebo.  We all know that the placebo response is 35 percent, is a typical number.  The response of the active drug is typically 45 or 50 percent. 
So that is why you needed 400, 600, patients.  So there is a problem right there, if you had a three‑way study, parallel‑group study, where you had placebo, the innovator and the test, I guess what you are worried about is that you ended up with going in the opposite direction from the placebo or that you are just the same as the placebo.
But it took a lot to get there so that is where I see there is a problem studying.  As far as your comment about solutions, it is my understanding, in reading the guidance, that solution formulations, there is no request for bioequivalence of solutions.  There is a request for suspensions for multiphase products but not for a solution.
I have looked as hard as possible to see why you would need a study for bioequivalence of a solution being an innovator with a solution on the market, I am asked to look at that and say, come up with a reason why someone has to do bioequivalence, and I can't.  Clinically, I just don't see‑‑once you show you have the same solution and the same droplet size and plume pattern met, I don't see why there is a need for a clinical trial.
I agree with the guidance from that point of view.  As far as a suspension, then you have other things going about.  The problem is, in the clinical trial, it took 600 patients to show that difference from 35 to, say, 45 or 50.  That is where I look for more innovation in the design.  I think we are too often going back to what it took to get the innovator on the market.
That is why I am interesting in seeing alternatives where we do crossovers or something along that line, one nostril for this one, one nostril for that, and go in and look for inflammation.  What is there that we can do?
DR. DALBY:  I am just concerned that, in the drop‑down from a clinical study to the in vitro testing, that it is possible to design tests that are so discriminating but completely irrelevant that you can falsely keep an appropriate product from the market.
DR. MacGREGOR:  I just think that scintigraphy in a multiphase product, having done that in the past, is one of the most difficult things, even going back and making‑‑in the case of the drug that we had, we made a hot bromine into the molecule so that we could study it.  I remember that, because it was a multiphase system, it was just so difficult to get it to be identical to what was on the manufacturing line, because you are only making ten or fifteen of these devices, it is technologically‑‑if we walked in here, there would be so many questions about the data that we would lose sight of any clinical benefit that we had seen from it.
DR. HAUCK:  I just wanted to take the discussion back to a little bit of basics again.  A key, really a requirement, of anything to show bioequivalence is that it be able to show differences.  If you have a test that comes back and says 7 all the time, then everything is equivalent and it is real easy to show equivalence.
So I kind of had problems when we were hearing Dr. Roman earlier, the note I wrote down is, "Why are we doing these?"  They didn't seem like they are going to discriminate against anything so, showing equivalence out of at least the parallel‑design clinical studies that she was discussing, don't seem terribly relevant.
I would much rather be, and now back to Dr. Dalby's comments‑‑I would much rather be in the overly discriminating case because that is a goalpost question.  You just don't have to set a tight goalpost if it is discriminating things that‑‑if something is very tightly discriminating, you just set a wider goalpost.  But you have to have the discrimination or you just can't do equivalence at all.
Where that puts your labeling, that I can't help you with.
DR. LEE:  It seems to me, based on what I have heard, it is not feasible to demonstrate dose response for a locally acting drug; is that correct? 
DR. HAUCK:  That's what I hear.
DR. LEE:  At the present time.  And, if not, what other approaches may we rely upon.  I don't think we have the answer.
DR. HAUCK:  Wait a minute.  That sounded like too general a statement.  I mean, it is an issue of the type of study and the type of drug.  We just saw a dose‑response study.
DR. LEE:  That is for nasally acting drugs.
DR. HAUCK:  He said locally acting.
DR. LEE:  I'm sorry; for locally acting nasal drugs.
DR. HAUCK:  Is that a true statement for all types, antihistamines as well, steroids?  I just don't know, but I raise that question.
DR. AHRENS:  I am tempted to say it is true, but I don't really know for sure.
DR. BEHL:  From the dynamics of how they act and the mechanics of what has to happen for them to show the effect, I would say that that statement, even though not shown for all compounds, is probably very close to be true.
DR. LEE:  I think back to the old question again; we need more data.
DR. HAUCK:  I think it is hard for us to say that such a thing can't exist.  It is sort of the absence of data is not the data for its absence, or something.  Other than saying we have not been shown it today.
DR. LEE:  I guess a more prudent way to say it is that we need to explore this further.
DR. DALBY:  One thing I think would be worth considering is, since, if I understand Harmut's presentation correctly, for every known steroid that is now on the market, it is possible to detect it in plasma.  I wasn't sure what his method was.  If you have an equivalent plasma time‑profile, must that have resulted from an equivalent deposition and an equivalent local concentration?
Could we not extrapolate that to equivalency because pharmacokinetic profiling is the number‑one acceptable criteria that the age says it prefers.
DR. LEE:  I think, along the same lines, Richard, is basically discomfort about the scintigraphy studies is that we are not certain what has been deposited is available and, therefore, maybe some sort of sophisticated techniques like receptor imaging is the way to go.
DR. AHRENS:  It would seem to me, though, in reference to that, that we are in a bit different ball game here than with inhaled drugs intended to go to the lung.  Inhaled drugs intended to go to the lung, there can be some real questions about aerosol particle behavior and where they wind up in the lungs.
While there may be some of the same issues about distribution in the nose, it has got to be a lot less problematic in terms of knowing the total percentage of what you spray in nose that winds up staying there.  Maybe that is the kind of thing‑‑that could easily be done with scintigraphy or, perhaps, other methodologies, so there is a while issue about delivering something that is a long distance away with chances for particles to misbehave versus spraying it essentially directly on a topical surface.
So it seems to me that, if that is true, then it may be that you could just go plasma pharmacokinetics for a drug that is actively absorbed from the nose.  If the time profile is the same, and perhaps you could maybe combine it with a charcoal block so you knew if it is orally absorbable drug.  
Therefore, what went in, must have gone in through the nose.  Wouldn't that reflect the dissolution‑time profile which is really the issue, isn't it?  Isn't that the reason that solutions, you wouldn't need to do all this, but, suspensions, yes?  It is because of the issue of the dissolution profile of the particles.
DR. DALBY:  I think, for most nasal products, that criterion is met.  You generally see, from an aqueous nasal spray, a very small percentage getting through into the lung, and there is support for that.  So I think that it is possible that it as least worth investigating.
DR. LEE:  Dr. Szefler, the last word?
DR. SZEFLER:  I was just going to say you could combine this two‑dose analysis in kind of a different way.  I think each drug has to be defined by a lowest effective dose and a maximal safe dose, but you kind of work with ranges with these drugs.  I don't know the nasal literature as well as I know the inhaled steroid literature, but it would seem to me that you would have to define that your lowest dose would be equivalent in terms of efficacy with your lowest dose of the reference product, and that your highest dose should not be any more toxic in terms of systemic measures, whether you use blood levels or cortisol suppression than your highest labeled dose of your reference product.
So if you took those two and kind of set your standards in terms of looking at comparative efficacy at the low range, comparative systemic effect at the high range, then you would kind of be able to pinpoint where the product fits in in terms of equivalency, I think.
DR. DALBY:  I just think it is important not to mix up a safety study with an equivalency study although we sometimes derive safety inferences from an equivalence study.
DR. LEE:  There is one more question to be addressed, and that question might even be equally challenging which is, what we have talked about so far is based on the so‑called seasonal allergic rhinitis.  "How much can bioequivalence, established based on that endpoint, assure bioequivalence for other indications such as recurrence of nasal polyps or other non‑SAR conditions?"  This is question A2.
Wally, is that your territory?
DR. ADAMS:  Yes.  The concern here is that, in our draft nasal BA/BE guidance, we have recommended that the studies to document bioequivalence use the seasonal allergic rhinitis model for all of the drugs, whether it be antihistamine or cortical steroids or anticholinergic.
The question that we are asking here is is there any reason to suspect that bioequivalence based upon the SAR model does not establish bioequivalence for perennial allergic rhinitis or for nasal polyps for the one drug, beclomethasone, for which nasal polyps is an indication.
DR. LEE:  Is that clear to the committee members?  Richard, do you want to take this on?
DR. AHRENS:  I think we are all hesitating to answer because any answer we give is likely to be right out of the air.  We have been saying all day we need more data, but, in this area, I know of no correlations between those two.  If you went Richard's route with a drug that was well‑absorbed and you showed similar time plasma profiles that must have to do with a similar dissolution pattern in the nose, and, therefore, absorption across that mucosa, you could be reasonably comfortable in that setting that, if they behave the same, they would dissolve the same, and they probably would act the same.  It is the same compound.
DR. LEE:  You can argue that the dose requirements will be different.
DR. AHRENS:  But the dose requirement will be different for both preparations.  So the issue is bioequivalence between the two.
DR. LEE:  Right.
DR. AHRENS:  I think that is all you can do is kind of reason it out of how it might be.  But I know of no way to really nail it down.
DR. DALBY:  If you don't like it for the simple case, you definitely wouldn't like it for this more complicated case.
DR. ADAMS:  Dick, are you suggesting, then, a slightly different question, that if you have in vitro data and PK data for nasal drug products that that would assure bioequivalence?
DR. AHRENS:  Assuming that that PK data was discriminatory, we all know that there are some nasally deposited products which are absorbed very little into the systemic circulation that, obviously, isn't going to work very well for those.  For other products, where there is substantial absorption from the nose and that is measurable and is discriminative, I, personally‑‑I haven't spent a lot of time thinking about it, but, at least where I am sitting here now, to me, that sounds reasonable.  It is a very different ball game, I think, from drugs that are inhaled into the lung.
DR. ADAMS:  And this would be a case where, with charcoal block, you could prevent the drug coming in from the GI tract or for a drug with low oral bioavailability.
DR. SZEFLER:  A quick comment.  I think you could reverse the question by asking does that disease pathology change the toxicity of the drug or the dynamics of the delivery.  I am not aware of any studies that would suggest that the absorption is different in terms of allergic rhinitis versus nonallergic rhinitis or polyps.
With polyps, it could be more but I don't know of any data that suggests that it is changed in any way.  So I think you have to look at the question the reverse way, too, in terms of‑‑
DR. BEHL:  My question to Wally is, given that the  PK can show that, then it is something very difficult to show for even nasal products.  The in vitro pharmaceutical equivalence, for example, in my opinion is not sufficient to show the therapeutic equivalence of locally acting compounds intranasally.  Having talked to a lot of others over the years, I don't see how we can escape the need to do at least a small bridging clinical trial to show the therapeutic equivalence along with a need to show other equivalence that can be shown very easily.  
DR. DALBY:  I guess I would turn the question around and ask what phenomenon do you think could get in the way of a finding that if you saw the same plasm concentration time profile, you would not have had the same profile of drug deposition and absorption in the nose.
DR. BEHL:  One is the way a compound would travel, through ciliary movement in the nasal tissue, the surface area to cover from the spray, for example.  The spray patterns become very important.  By changing them, you could maybe still get the same PK information or the same PK data, but not the same coverage of the same surface in the nose.
The rate of ciliary currents in the back of the throat may be different from formasin(?) formation, may not show much of a difference in the absorption depending on how it is deposited in the nose; a number of factors.  If we have particles in the formulation, it makes it more difficult.
[Slide.]
I think I need to move on.  Obviously, Wally has posed some very interesting questions which require some educated guesses.  I think that is the best that we can offer.
We now move into the final session of today dealing with PK and PD for systemic exposure of locally acting drugs.  Dr. Uppoor from the agency is going to provide us with the FDA practices.
PK and PD Studies for Systemic Exposure
of Locally Acting Drugs
Current FDA PK Practices
DR. UPPOOR:  Good afternoon, everyone.
[Slide.]
We have heard from Dr. Derendorf as to some of the systemic PK studies that are done and some of the reasons‑‑what we can rely on and what we cannot.  Let me go through my presentation and I will talk about the current FDA practices primarily for new oral inhalation and nasal aerosol products.
During the course, I will actually point out the level of systemic exposure.
[Slide.]
During my presentation today, I will be dealing with the general pharmacokinetics and bioequivalence studies that are needed for new molecular entities at the same time they are developed as new inhalation or nasal‑spray products; as we have heard this morning from Dr. Adams, the different steps or different approaches we can use to generally assess bioavailability and bioequivalence; and then go through the methods and some of the issues.  Some of these have been discussed in the previous discussion as well.
Finally, I will go through something that we also want to get your input on, is the role of systemic pharmacokinetic and pharmacodynamic studies not only for a generic product‑‑that comes in‑‑but even when product changes are made for an innovative product.  We want to hear that from you.
[Slide.]
For a new molecular entity, from a pharmacokinetics and biopharmaceutics perspective, we want to know the fate of the drug.  Obviously, we want to know the mass‑balance studies.  We want to know the single dose, the multiple dose pharmacokinetics.  We would like to know the absolute bioavailability and relative bioavailability of the drug.
Dose proportionality is one of the key issues.  As we have been deliberating all day, bioequivalence studies made between the product that was tested in the clinical trials versus what is to be marketed.
[Slide.]
From a clinical pharmacological perspective, of course we would like to know what the pharmacokinetics are in the target population, especially if there are differences in delivery in healthy volunteers versus the target population.
Additional studies that are necessary to understand and label the drug; the age, gender and race effects; of course, special populations, what would happen in the renal‑ and hepatic‑impairment patients.  One of the things which is, of course, very important is to know what kinds of drug interactions might be expected, so from a pharmacokinetics perspective, we would like to see appropriate drug‑interaction studies conducted.
In addition, we would also like to see pharmacokinetic and pharmacodynamic correlations established wherever possible.  For inhalation and nasal products intended for local action, the pharmacodynamics that I am going to focus on and talk about are from a systemic safety perspective.
[Slide.]
So, as we have discussed, we know bioavailability is an important factor, but from a product‑quality perspective as well as from a clinical pharmacology perspective to understand the in vivo characteristics of the drugs.  However, with all the discussion, we still do not believe that in vitro testing alone is sufficient for all kinds of drug products.  There may be some cases where in vitro testing is sufficient.
[Slide.]
Knowing the sensitivity and accuracy of the pharmacokinetic data, the CFR outlines clearly the different approaches that can be used for bioavailability and bioequivalence.  Pharmacokinetic studies are, obviously, preferred.  However, there are issues with the products intended for local delivery.
[Slide.]
I won't really go into detail today because Dr. Derendorf talked about why we cannot rely solely on pharmacokinetics, systemic plasma concentrations, for bioequivalence and bioavailability purposes.
[Slide.]
Because systemic‑exposure data helps characterize the systemic safety for these locally acting drug products, and to address the local delivery and efficacy issues.
At this point, we definitely request that clinical data be submitted to look at the efficacy.
[Slide.]
So, recapping, for a locally acting, orally inhaled drug product is what I am focussing on now, but even for an inhaled product, the general, conventional clinical pharmacology and biopharmaceutic studies are needed.  If it is not a totally new drug, from the new product, we request single dose, multiple dose, dose proportionality and relative bioavailability as well, where as appropriate, and PK/PD studies.
Generally, pharmacokinetic studies are done in healthy volunteers.  However, we have been sensitized with several of the new dosage forms that are being developed.  These studies may be needed in the appropriate target patient population if the drug delivery is expected to be different.
[Slide.]
The two aspects that we have been touching upon a couple of times today are‑‑the first one is the inhalation pharmacokinetics with the charcoal block.  We really do not require that this study be done.  However, I think that it does have merits that can be useful in the drug‑development perspective.
It is helpful in comparing relative dose delivery to the lung from different formulations.  The concern that I have is that it really does not address the delivery to the relevant biospace.  It also doesn't really point out what is the oropharyngeal deposition.
[Slide.]
Similarly, with the gamma scintigraphy studies, we have been seeing a lot of activity in this area.  It is very useful.  However, again, as concerns were expressed today, we have concerns about possible lab‑to‑lab variation.  Obviously, the labeled drugs may have different aerodynamic characteristics or even a modification to the original product.
There is significant activity in terms of standardization of these tests, but still, at this point, we are not comfortable to use it from a regulatory perspective.
[Slide.]
So for new oral inhalation, and probably it could even apply to the nasal products, in addition to the pharmacokinetic studies, we do require clinical studies for efficacy and safety.  Some of the pharmacokinetic data can be acquired through population pharmacokinetic and pharmacodynamic studies.  One other thing that I would like to point out is when a specific topical claim is sought for that drug and drug product, the special topical versus systemic‑effect studies may be necessary.
[Slide.]
Knowing this three‑pronged approach of using the in vitro data, and the in vivo data from efficacy as well as pharmacokinetics or systemic pharmacodynamic data, I want to actually discuss a little bit on where we might be able to use the systemic pharmacokinetic and pharmacodynamic data, especially for new drug products.  When minor changes are made to a well‑characterized product, in vitro data as well as systemic PK/PD may sufficient in those cases.
However, when major changes are made to the product, either to the formulation or to the device, additional clinical data is necessary in those cases and, at this point, it has been a case‑by‑case scenario and it is a consultation with the division.  We would really like your help to clarify some of these and where the comfort factor lies.
We also know the pharmacokinetic studies are recommended.  But other pharmacokinetics data needs to be collected when a product is being changed or, for example, from a CFC to a non‑CFC‑based inhalation or nasal‑dosage form.  We do our guidance points‑to‑consider document that requests pharmacokinetic studies.
So I think, in summary, we have been talking about use of pharmacokinetic studies all along.  It is a sensitive approach.  There are some restrictions on how we can use this for a locally acting drug product.  However, the sensitive assays that are being developed, we do have an ability to measure or detect plasma concentrations after oral inhalation in nasal products although we do have some cases where we are still struggling with the measurement of these plasma concentrations, or detecting and quantifying these concentrations.
So I would actually say that we do require that pharmacokinetic‑based bioavailability studies be conducted, both to understand from a clinical pharmacology perspective as well as the product‑quality perspective.  However, for orally inhaled and nasal drug products intended for local action, it is multiple aspects that have to be address.  Bioavailability and bioequivalence cannot be solely addressed based on pharmacokinetics.
But, because of the accuracy and, wherever possible, we say pharmacokinetic studies are the first choice to characterize the systemic exposure.  However, that alone is not sufficient.  You need additional pharmacodynamic data from a safety perspective as well as clinical efficacy data where appropriate.
Thank you.
DR. LEE:  Thank you very much.
Dr. Harrison, you have the last words, but you only have twenty minutes.
Industry View
DR. HARRISON:  Good afternoon.  I want to thank you for allowing me to be the last presenter.
[Slide.]
My topic is PK and PD studies for systemic exposure of locally acting drugs.  I am giving an industry viewpoint.
[Slide.]
The value of PK for OINDP is that it measures systemic absorption or systemic exposure.  Both terms are used in the guidance.  I look at them as interchangeable.  Really, what they are doing is measuring systemic safety.  PK is an established bioequivalence metric.  It can be standardized.  It can be validated.  It is discriminating.  So certainly it has an awful lot of pluses for it.
[Slide.]
There are some concerns, however, with PK that were raised.  One is the low doses that are given nasally and by inhalation, what limitations that imposes.  The assay lower limit of quantitation; there is quite a bit of variability that is encountered in PK studies for the nose.  There could be draining of excess dose so that you really don't get a good dose response.  And, for oral inhalation, the dosing technique is quite critical.
[Slide.]
What I want to do is address those concerns up front.  The first one is low doses.  That really is not so important anymore.  The bottom line is can you quantitate.  With the new advances in analytical techniques, you can usually do it.  Low dose is not a big issue, I think, especially when you have a therapeutic dose range, as has been proposed in the new guidance‑‑the nasal guidance, that is‑‑you can go, say, one puff, two puffs or even up to four puffs.  Whatever is recommended in the dosing recommendations, it is fair game to use in the PK study.  That also will help in analytical sensitivity.
So that gives you a lot more dose options than doing a PK study.  To me, that is a good idea.  The nasal route, you may be limited by drainage on how much you can give but, again, there is sensitivity there even for that.
[Slide.]
Looking at the assay lower limit of quantitation, with LC mass spec/mass spec, now, you have got tremendous capabilities to go into the peak of gram per ml range.  In many cases, you can get down to about 10 to 20.
What I have listed there are commercial assays that are actually available.  Say, if you were a generic firm, you could find those assays available right now.  For BDP that is important because it has got a 17 monoproprionate metabolite that is really the primary material in plasma and it is the most active and there are assays for that as well as BDP.
So you can do a good kinetic analysis of BDP as well.  Again, because the equipment is so pervasive, you could get an analytical lab to help you out with whatever assay you wanted, I believe.  So that is not a big issue anymore, either.
[Slide.]
Variability is a concern.  There is large intersubject variability.  There is large intrasubject variability.  There is also variability with the dosing technique.  That needs to be addressed.
[Slide.]
This is just a slide showing, in one of the treatments given nasal formoterol, perhaps an example of a beta agonist, the variability you are seeing here with about an N of 27 is roughly on the order of about 40 or so percent.   That is fairly typical.  It is also, say, typical of a topical product or a variable oral product and it is something that would could live with.
DR. HAUCK:  Here, with a N of 12, the variability is a little bit higher.  This is nasal triamcinolone.  This variation, by the way, was somewhat similar to what was presented earlier by Dr. Derendorf or nasal fluticasone.
[Slide.]
Here, budesonide.  Again, very similar.  These are standard errors but, again, it is coming out to be 40 to 50Êpercent variability that you are encountering in plasma levels. 
[Slide.]
This is oral fluticasone.  Again, you can see the range that you get in the plasma levels in these twelve individuals.  So they vary broadly, but the curve pretty much is established by the mean.  It is something I think that we can live with.  We can reduce variability.  There are various possibilities.
Replicate study designs is an interesting possibility that I have not seen anybody, at least approach in the literature.  It is something that could be investigated.  
[Slide.]
What people have looked at, what we have looked at, is increasing the subject number.  With the nasal route, you may need to reduce the dose.
What we have looked at for oral inhalation is training the individuals to use proper technique.  A criticism there; it is not the real world and there are actually even little computer machines that could teach a person exactly how to inhale the product properly.  Certainly, we have used that in the past and with good results as well.
[Slide.]
So what are the limitations of, then, doing pharmacokinetics?  There really is no correlation with efficacy right now.  That has been seen.  I will show you some examples of that for the corticosteroids.  And it does represent only a fraction of the dose, usually less than 30Êpercent.
As we talked about for nasal, it could be just a few percent.  Again, if you compare the nasal PK, you may be working hard to get equivalence of an extremely small part of the real dose and what is being positive in the nose, where your efficacy is, may be completely different than what you are focussing on.
Again, there are even concerns with the fine‑particle fraction.  That is debatable.  What are the right ranges?  So there is still some confusion there.  That is, again, a limitation of how you interpret it. 
Really, when you look at it, PK is the summary parameter.  It represents absorption through many different routes; the mouth, the GI tract and, on first pass, going to the liver, the lungs.  Actually, the appearances really have different rates into the blood.  We have seen some sensitivities there.  In terms of depending on how much goes in the mouth versus the lungs, you actually can get some confusion in your datasets.
[Slide.]
Here is an example of what I want to get at now is that there is no good relationship between efficacy and blood levels.  This is a study with fluticasone given nasally.  C1 represents the concentration at one hour and the symptom scare represents your efficacy.  
What you see here is that, for the oral products and the placebo, you saw no difference in the symptom score but the nasal administration, you did whereas, in the blood levels, you had detectable levels only orally but not nasally.  So, again, they were separated.  Blood levels were seen orally.  Efficacy was only seen nasally.
[Slide.]
The same thing was done through the oral‑inhalation route, again with fluticasone.  Again, what you are seeing is a very similar type of design where now you are looking at your efficacy parameters, AM FEV1 and symptom score and you are seeing activity with the inhaled route but not the oral route.
Then, if you look at the Cmax and AUC as your pharmacokinetic parameters, what you are seeing there are your highest levels orally.  They are easily twice that of what is seen by your higher inhaled dose and yet you are not seeing any activity associated with that.
So, once again, what you have is really a dissociation between pharmacokinetics and efficacy.  So that is a limitation as well.
[Slide.]
The conclusions are that PK is useful to establish systemic absorption.  It really is not a surrogate for local efficacy but it is doable.  Right now, the assays are out there.  You can measure the levels, even nasally, and you can reduce the variability to make it worthwhile and doable. 	The next question to ask is can you actually do systemic bioequivalence.  
[Slide.]
We have got some examples there.  We have done a lot of work with BDP.  What I want to talk about first, when we are comparing two formulations.  Formulations; we will call them MDI‑A, MDI‑B.  The study designs that we used were single dose but multiple inhalations.  They were asthmatics with a crossover design and good inhalation technique.
So that will be common to the studies.
[Slide.]
In terms of the devices, if you look at the draft nasal bioequivalence guidance, what you could say is Q1 and Q2 were the same and identical, those two devices.  The particle‑size distribution, the spray pattern, would meet the criteria were essentially similar.  The route size was the same and the actuator, again, dimensions were essentially the same.
So there wasn't a lot of difference between the two.
[Slide.]
When we did the first study, it was in 18 asthmatics.  The objective was comparability.  What we found was that we came close to matching confidence intervals but we did not make it.  You can see Cmax was on the low side of the accepted 0.1 to 1.25.  AUC was on the high side.
Coefficients of variability, about 50 percent for Cmax, again, similar to what was seen in the earlier slides I showed you with others.  AUC also was variable.
[Slide.]
Another study was done, again with the exact same MDIs, MDI‑A, MDI‑B.  Here, the objective was systemic bioequivalence.  So, what we did is we increased in N number to 45 and we actually looked at two doses, a low dose and a high dose in this study.  
[Slide.]
You can see here coefficients of variation were reduced for the most part with a higher N number and now, essentially, all the parameters did actually meet strict bioequivalence criteria.
So we concluded from this that we could actually show systemic equivalence but we also did local delivery studies for efficacy.  We did not stop there.
[Slide.]
Another example we have got is now looking at MDI‑C versus MDI‑D.  In this case, we actually had just different strength products.  So, it is the same dose.  The only thing different here to give the same dose is different numbers of puffs because you had a different valve size.
So one MDI may require twice as much as the other to get the same dose delivered.  The study designs that we looked at to analyze C versus D again were single‑dose asthmatics, crossover, and a good inhalation technique.  Similar to what we found in the previous examples, you have everything matched identical in this case except for the valve size. 
So, again it was very similar, such as the same formulation but different valve sizes and we did a study with that.  We are looking at systemic comparability here in 18 asthmatics and we came very close to getting bioequivalence with an N of 18.  It was just outside, 7.6 for Cmax.  If you want to use a more liberal criteria of 7.5, it actually would make it.
CV wasn't that great in this case.
[Slide.]
If you look at the next study, when we went to 30, we actually met the criteria.  We could include equivalence as the conclusion, therefore.  We, again, had equivalence, in this case with a reduced N number but we did run a local delivery study to demonstrate efficacy as well.  We did not stop there.
[Slide.]
Looking at other PK options, we have talked about charcoal block.  It certainly allows differentiation of the pulmonary or non‑pulmonary absorbed drug.  It has got a lot of appeal there.  The nice thing is it utilizes the same drug assays and metrics so there is little added time or cost.  You really don't have to alter the reference or the test formulations as you would have to do for, like, gamma scintigraphy.  So it has got a certain appeal to it.
[Slide.]
However, the limitations that I see with the charcoal block is that there is no evidence that pulmonary absorbed drug correlates, again, with efficacy.  It is true, it gets into lungs, but that is where the real correlation stops.  And it does not discriminate potentially important product differences such as oropharyngeal deposition or regional lung deposition.
I look at it as a very useful laboratory took to get at the pulmonary drug absorbed but I don't see it, really as adding very much more to PK.  It could be looked at as a potential surrogate for local delivery, again if we can establish that link between what is put in the lungs and absorbed versus efficacy.
[Slide.]
Another option is urinary excretion.  Supposedly, when PK is not doable, that is a possibility.  There are examples of that in the literature.  It has been reported for the various products up there.  There are references for each one of them. 
[Slide.]
Here is one, for instance, in nasal ipratropium.  It is highly variable.  You can see the CV was 84 percent and the dose excreted also was 89 percent.   So, although you can do it, it really doesn't seem to have a lot of added value.  So I look at it‑‑it has got high variability.  It has got low sensitivity.  And, therefore, it is unlikely to be a reliable surrogate of what we are trying to do here.
[Slide.]
PD has been suggested as a surrogate when PK is not doable.  Now, the PD that I am considering is only systemic PD.  So you are looking at cortisol, markers of bone growth, of demineralization, things like that.  I am not talking about FEV1s at all here.  And, again, that requires an appropriate study design.
You usually need a dose‑response curve to show that your PD measures are sensitive.  It requires repeat administration.  
[Slide.]
Frankly, it is highly variable.  It has got low sensitivity.  It requires, again, multiple dose levels.  I don't see that as being very valuable.  If you can't do pharmacokinetics, the likelihood of doing PD is very low.  If you are looking at, say, what is out there published with nasal products, if you cannot do pharmacokinetics, I don't know how you are going to deal with, say, urinary cortisol or 24‑hour cortisols.  It just doesn't have the same sensitivity.
You get the best results when you can do PK as well so, therefore, I don't see that as a great surrogate either.
[Slide.]
PK/PD.  That is a very nice thing.  There has been a lot of work done there.  It, again, allows correlation of PK with PD.  PK is linear.  PD has got a dose‑response curve.  It certainly offers increased understanding of what is happening for systemic exposure and safety. 
So it has got, again, a lot of appeal in helping the understanding.  
[Slide.]
It is sophisticated work, though.  It requires several dose levels, additional analyses and I don't think it really increases the ability to discriminate which is the bottom line for doing bioequivalence.  So I look at as a very useful laboratory tool but I don't see it as needed for bioequivalence either.
[Slide.]
So, in summary, systemic PK assessment really is what is needed to assure systemic safety and it really is doable for most drugs.  The state of the art is you can do it, even nasally. 	
The other possibilities, PD, urine levels, are not likely surrogates.  Charcoal block and PK/PD, again, are nice development tools but I don't really see them making the leap, either.
[Slide.]
So my input into the last question, are there situations where in vitro data plus PK, and, again, even PD, can be relied upon to show assure local efficacy, they can be relied on is the key thing.  It really does imply predictability and the list of drugs.  It has not been established, really, for any of them.  
Certainly, there are a lot of questions there.  Until we can get better information, I think we need to have caution and err on the side of caution and not really look for situations where you can just do PK without having some type of local delivery component.
DR. LEE:  Thank you, Lester.
Subcommittee Discussion
DR. LEE:  Wally, would you like to provide some background for your question?
DR. ADAMS:  Yes.  I would like to ask Lester a question concerning his last slide.  Lester, you were talking about in vitro data plus PK plus systemic absorption PD in that case.
DR. HARRISON:  Yes; that is correct.
DR. ADAMS:  Our question was a general one related to whether in vitro data plus PK data would be able to assure bioequivalence.  Lester, you are saying no; that is your answer to this question?
DR. HARRISON:  That's correct. 
DR. ADAMS:  Yet there are cases where you are indicating if PK data are not doable, then you feel that the PD is not going to contribute.
DR. HARRISON:  That is my position.  Based on what I have experienced in the literature, I have never been convinced that, if you can't do one, you can do the other.  It is a nice objective but, in reality, I have not seen it done.
DR. ADAMS:  You could have situations where neither a test product nor a reference product may inhibit the adrenal axis.
DR. HARRISON:  Exactly; that is more likely to happen.  That is why going up in doses may be an absolute necessity in cases like that.  But, even for fluticasone, you can do nasal fluticasone now and the assays are so good that I think that it is getting to the point where we can measure almost anything.
DR. LEE:  Are there members of the committee who can shed some light on this question?
DR. LI:  I think, from the standpoint of orally inhaled drugs, that are sufficient variables in regional lung deposition, particle‑size distribution, that the sort of in vitro assessment along with pharmacokinetic data without any clinical types of evaluation is probably not going to be enough.
I would say that the orally inhaled products should have an in vivo assessment.
If we kind of look back to some of the cascade data that we saw and our attempts to use the chi square to get a numerical handle on comparability, chances are that any in vitro assessment for a new product is not going to be exactly the same as the reference product.  There are going to be some differences, and the differences may be at various stages of cascade or may be differences in particle size and different ranges.
So it is going to be really impossible to predict precisely the biological activity of that orally inhaled product.  So I, basically, would agree, at least certainly in the area of orally inhaled products, that in vitro assessment is important but not sufficient.  Pharmacokinetic data is also important but not sufficient.  Some in vivo assessment would be necessary.
DR. ADAMS:  Just for clarity, Dr. Li, you are talking about efficacy.
DR. LI:  That's correct; for orally inhaled products.
DR. BEHL:  Which could be a bridging study also as opposed to a full‑scale study.
DR. LEE:  Is Steve Forrester here?  He left?  Okay.
DR. ADAMS:  Just to follow up further on this question, Dr. Uppoor, did you wish to ask the subcommittee any question with regard to that last question?
DR. UPPOOR:  I actually just want to find out, even if you have an innovative product, for example, and that has been shown to be clinically safe and efficacious and you have done all these trials that have been approved, and some minor, some type of change is made to that product and it is the same product, you have a handle on what goes on with that product, you have some understanding or, hopefully, a reasonable understanding of the product, and some minor changes are made, even in those cases, what I am hearing is it doesn't matter what the change is, but if it is an orally inhaled drug product, we would like some kind of efficacy data in addition to in vitro and PK.
DR. LI:  If you are addressing that question to me, that would be a question that would, in my view, be extremely focused.  I did not, in fact, say that, in that particular set of circumstances, one would necessarily need to go through clinical studies and even to specify what kind of in vitro studies would be necessary.
I think, in a very narrow sense, depending on what those changes were, say, in the development of the product, if they were such change where one might not expect any significant, really, change in delivery, then probably I would say how things are handled now, case‑by‑case, would be the way to go.
If there are major changes in the formulation and the production and changes in propellent, for example, that would be an example.  A change in propellent is probably enough of a change that you would really need to do more extensive testing.
DR. GORE:  Just a comment from the perspective of those of us in product quality that have a lot of experience with cascade impactors, rather minor changes in the formulation of the composition of the material can, in fact, change what you are, in reality, measuring in the individual stages of the cascade impactor.
So, because of formulation and what is deposited on the cascade‑impactor stage is a combination of excipients as well as active ingredient.  That is something that would require a lot of validation if you were trying to make a crossover between two different formulations.
DR. LEE:  Are there any comments?  I think we are kind of supersaturated.
DR. LAGANIERE:  I would just add that the experience of Dr. Harrison concerning nasal drug administration, he seems to be alluding to the fact that you can increase the dose if you are not able to see it at the small doses that are usually administered in therapeutics.
But, in the context of safety or exposure, I would like to have maybe the opinion of physicians regarding the relevance of using a so much higher dose that would be usually higher than the recommended daily dose.
DR. HARRISON:  Let me just clarify that before you ask an opinion.  I meant within the therapeutic dose range.  You increase the dose.  As long as it is in the therapeutic dose range, say up to four puffs per nostril, you can do that much.
DR. LAGANIERE:  Okay.  So that would be a limit in establishing whether a pre‑case exposure study is feasible or not.
DR. HARRISON:  Yes.  I went fast through my slides, but what I did show is pharmacokinetically, you can get a nice dose response with pharmacokinetics in the nose.  It has easily been shown by inhalation, but nasal as well.
DR. LEE:  Wally, the short answer to your question is that, apparently, nobody around this table has any situations that would respond to your question.
DR. ADAMS:  I hear that.  Thank you, Vincent.
DR. LEE:  Guirag and Wally, are there any other questions for the committee before we adjourn the meeting?  Anybody else?
DR. GORE:  May I ask more of a procedural question because there was actually a comment made earlier about the need for another meeting.  I would like to say I think there is a need for another meeting.  There is a huge amount of information, particularly in the CMC area, that was brought forward in the afternoon that we did not have an opportunity to discuss and also some proposals for ways to bring more data into the discussion.
That is just my proposal.  I think we need another meeting.
DR. LEE:  If there are no further comments, I would like to thank everybody for participating openly.  I am surprised that I am still alive.  I thank you for your input and have a safe journey home.  Thank you.
[Whereupon, at 5:08 p.m., the meeting was adjourned.]
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