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Pre-decisional Draft for Workgroup Discussion v1 1/24/2008

Supporting the use of clinical pharmacogenomic test information in disease management through electronic information exchange

Discussion Background for Pharmacogenomics Subgroup of the PHC Workgroup

Personalized Health Care (PHC) represents a systems approach to support patient-centric health care by integrating genetic/genomic test information and health information technology (i.e., personal/electronic health records PHRs/EHRs). Pharmacogenomics (PGx) refers to individual’s genetic variation effects on therapeutic responses or adverse events. PGx has the potential to inform therapeutic choices, clarify dosing decisions, reduce adverse drug reactions, and may optimize prescribing patterns of clinicians.

Many efforts in PHC focus on the future health system; however, PGx provides current opportunities to improve patient outcomes. For example, recent progress in elucidating the genetic basis for variations in drug metabolism and response has motivated the Food and Drug Administration (FDA) to alter prescription drug labels (for example, warfarin1, carbamazepine-containing drugs2, and morphine3) to suggest the use of genetic testing prior to commencing treatment. It is likely that similar label-changes will follow with additional PGx research.

Various PGx tests analyze variations in genes that may affect drug metabolism. These tests are already used in practice or in clinical studies. Here are a few examples of clinical scenarios where PGx testing may apply:

  • Anticoagulation therapy (warfarin) 4

  • Schizophrenia5

  • ADHD6

  • Cancer chemotherapy (irinotecan)7-8

  • Asthma and chronic obstructive pulmonary disease (leukotriene antagonists and theophylline)9

  • Pain management (opiods)10

While the knowledge of specific genes involved in drug effects has been accumulating for many years, only recently have there been clinical applications for PGx testing. For example, the anticoagulant drug, warfarin (coumadin), is primarily affected in variants of CYP2C9 and VKOR (Vitamin K epoxide reductase). These two genes are associated

1 http://www.fda.gov/bbs/topics/NEWS/2007/NEW01684.html

2 http://www.fda.gov/cder/drug/InfoSheets/HCP/carbamazepineHCP.htm

3 http://www.fda.gov/bbs/topics/NEWS/2007/NEW01685.html

4 Anderson et al. Circulation, 116: p2563. (2007) http://circ.ahajournals.org/cgi/content/full/116/22/2563

5 de Leon et al. J Clin Psychiatry 66:p15-27. (2005). http://www.ncbi.nlm.nih.gov/pubmed/15669884

6 Trzepacz PT et al. 2008 Feb;18(2):79-86. Epub 2007 Aug 14. http://www.ncbi.nlm.nih.gov/sites/entrez?tmpl=NoSidebarfile&db=PubMed&cmd=Retrieve&list_uids=17698328&dopt=AbstractPlus

7 http://jco.ascopubs.org/cgi/content/abstract/22/8/1382

8 http://jco.ascopubs.org/cgi/content/full/24/28/4534

9 http://www.nature.com/tpj/journal/v6/n5/full/6500387a.html

10 http://www.painmanagementnursing.org/article/PIIS152490420700046X/abstract

with 35-40% of the variability in dose requirement for warfarin. Pharmacogenetic-guided dosing of warfarin is a promising application of personalized medicine but the algorithms used to determine dosing may require additional evidence through randomized clinical trials.4 The results from these PGx tests and others may contain an action summary for the clinician, detailed actual results, an interpretation of the data, any recommendations for altering the therapeutic dose, the methodology of the test, and background information. Second, a commercially available CYP450 test is a pharmacogenomic test kit that analyzes variations in two genes (CYP2D6 and CYP2C19) that play a major role in the metabolism of many widely prescribed drugs (i.e., antidepressants, antipsychotics, proton pump inhibitors, and antimalarials11). Information about CYP2D6 and CYP2C19 could be used by clinicians to determine therapeutic strategy and treatment doses for drugs that are metabolized by these two gene products.

Despite the promise of PGx, its integration into routine clinical practice has been slow due to several issues, including: lack of clinical utility and information concerning the effects that drug metabolizing enzymes have on specific pharmaceutical agents; lack of clinical guidelines for the use of PGx tests in pharmaceutical selection; lack of reimbursement for PGx tests; and clinician unfamiliarity with the field of PGx. The Secretary’s Advisory Committee on Genetics, Health, and Society (SACGHS) recently released a report on PGx, Realizing the Promise of Pharmacogenomics: Opportunities and Challenges. In this report, SACGHS recommends three components relating to HIT summarized here:

  • study how clinically validated PGx test results are being incorporated into EHRs

  • ensure infrastructure is in place to support PGx data in EHRs for clinical decision support (CDS) tools

  • explore development of pilot studies that examine the impact of CDS systems for PGx technologies at the point of care.

A PGx Subgroup of the American Health Information Community’s (AHIC) PHC Workgroup is proposed to deliberate on and develop actionable recommendations around harmonization of standards for electronic exchange of PGx data for medical applications. These recommendations would then be advanced to the full PHC Workgroup for consideration, followed by presentation to the AHIC for consideration of action.

Some of the discussion items below may not be relevant for recommendations, but are important areas for the subgroup to consider. The primary perspectives upon which to evaluate these issues should come from the consumer, clinician, laboratorian, pharmacist, and researcher perspectives.

11 http://www.roche.com/final_cyp_gene_family.pdf

Issues for Discussion:

1. Evaluating the need and opportunity for use case development for PGx testing

The subgroup could consider augmenting the existing PHC use case12 to include the specifics of writing a prescription for a drug, including necessary pharmacogenomic testing, and e-prescribing. The inclusion of PGx testing could include three different scenarios: PGx testing prior to prescription; coupling performance of PGx test with e-prescribing; and dosing and treatment selection based on prior PGx testing.

2. Fostering EHR data standards to enable clinical research activities

Clinical research records are used to address evidence development processes and safety assessments; however, standards need to be developed to include PGx data from clinical research into the evidence development processes and safety assessments. Current efforts are underway to develop common formats for presentation of PGx data from clinical research or trials to the FDA through a voluntary data submission program13-14. Clinical Data Interchange Standards Consortium (CDISC), Health Level 7 (HL7), FDA, and National Cancer Institute (NCI) have worked together to develop Biomedical Research Integrated Domain Group Model (BRIDG), which enables the clinical care standards of HL7 to have semantic interoperability with those standards used in research. In the future, transfer of genomic data from an EHR ensuring appropriate consent and privacy safeguards could be used to identify potential genetic causes for various adverse events through a pharmacovigilance system. Standards development for the current flow of information will prepare for future data flowing from an EHR. In addition, existing standards used in genomics by researchers that may need bridges for clinical data applications include:

  • Micro-Array Gene Expression (MAGE), which is standard for array data15

  • Bio-informatics Sequence Markup Language (BSML), which is used for genotypic data16

  • Gene Ontology is a vocabulary standard used in genomics17

Taking early steps in developing common data standards for PGx test results may avoid obstacles in their application of clinical care and incorporation in EHRs.

12 http://www.hhs.gov/healthit/documents/PersonalizedHealthcarePrototypeUseCase.pdf

13 http://www.fda.gov/cder/mapp/4180.3.pdf

14 http://www.fda.gov/cder/genomics/VGDS.htm

15 www.mged.org/Workgroups/MAGE/introduction.html

16 BSML is an XML data standard for describing and annotating genomic sequences. It includes descriptors for both the physical-chemical attributes of sequences, as well as the abstract annotations. BSML strives to encode the semantics of the data to facilitate human and computer interaction with the data.

17 http://www.geneontology.org/

3. Exploring electronic means to distribute clinical indications for specific genetic tests prior to initiating therapy

As previously mentioned, FDA has recently updated the labels of several drugs to recommend genetic testing prior to initiating therapy. The genetic tests may simplify initial dosing and prevent adverse reactions. It is likely that an increased understanding of PGx will motivate additional drug-label changes. Timely dissemination of this information to clinicians is difficult, but could be addressed through the use of clinical decision support or other web-based tools. In addition to label-changes, the evidence to support the use of these tests is still being developed. Exploration of standardized electronic methods to communicate these changes in labeling and evidence may increase clinician knowledge and thereby improve patient outcomes. Stakeholders could include FDA, EHR vendors, and Electronic-prescribing (e-prescribing) vendors.

4. Integrating PGx test results in e-prescribing (i.e., clinical decision support)

E-prescribing is one of the most mature forms of health information technology as many pharmacies can currently receive e-prescriptions. By augmenting the information that is provided to pharmacists, they could become potential point-of-care resources for improving therapeutic choices and health outcomes. Providing pharmacists with PGx results or interpretations can improve communication and verify proper dosing decisions. Including clinical decision support in e-prescribing systems may improve the safety, quality, efficiency, and cost-effectiveness of care.18 This will require developing standards, optimizing the messaging both to and from the pharmacy, examining the work flow, and determining the policy and technical issues associated with e-prescribing and transmittal of laboratory test results.

5. Identifying obstacles for submitting PGx data to databases and repositories of genetic information, such as the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) and related clinical trials databases

PGx data is complex and may not be consistently structured by disparate research and clinical laboratories. This poses a challenge for useful compilation and additional analysis of PGx data. To clarify the submission of current research data to various databases and prepare to receive phenotype data in the future from electronic sources, such as an EHR, standards should be created. These standards should include nomenclature for common genetic variants, such as polymorphisms, insertions, and deletions and may include standards for content, messaging, terminology, security, privacy, and others to ensure interoperability. The development of standards should include clinical sources of PGx data, such as from http://www.clinicaltrials.gov/.

18 Teich et al. JAMIA 12, p 265-376, 2005. This also contains detailed recommendations for CDS tools within e-Rx system.

PharmGKB19 curates information that establishes knowledge about the relationships among drugs, diseases and genes, including their variations and gene products. Currently, submissions to PharmGKB are not required to adhere to standardized formats. Recommendations 6B and 6C from the SACGHS report, Realizing the Promise of Pharmacogenomics: Opportunities and Challenges, connect to this idea:

  • HHS should work with the private sector to identify obstacles to data sharing and to develop solutions to overcome these obstacles (e.g., legal and data confidentiality assurances, intellectual property protections, funding of databases and health information technology).

  • HHS should work with other relevant Departments (e.g., DVA, DOD, DOC) and the private sector to improve data sharing and interoperability among databases. Specifically HHS should work with existing organizations to create uniform genomic data standards, explore ways to harmonize data analysis methodologies, and develop an infrastructure to enable data exchange. Data sharing and interoperability of research, regulatory, medical record and claims databases will facilitate the study of molecular pathogenesis of disease, the identification of targets for drug development, validation of PGx technologies, assessment of health outcomes associated with use of PGx technologies, and determination of the cost effectiveness and economic impact of using these technologies.

Prior Testimony for PHC Workgroup:

  • The Wisconsin Warfarin Collaboration Case Study http://www.hhs.gov/healthit/ahic/materials/09_07/phc/wisconsin.html

  • Clinical decision support, September 17, 2007 meeting http://www.hhs.gov/healthit/ahic/healthcare/phc_archive.html#08

  • Pharmacogenomics Overview (Greg Downing) http://www.hhs.gov/healthit/ahic/materials/11_07/phc/downing.html

  • Clinical Genomics Interoperability Using CDISC-HL7 Standards (Rebecca Kush and Ed Helton) http://www.hhs.gov/healthit/ahic/materials/11_07/phc/cdischl7.html

  • The Pharmacogenomics and Pharmacogenetics Knowledge Base (Michelle Carrillo) http://www.hhs.gov/healthit/ahic/materials/11_07/phc/pharmGKB.html

Potential Presentations at Subsequent PGx Subgroup Meetings:

  • E-Prescribing

19 http://www.pharmgkb.org/

Potential PGx Subgroup Members:

  • Richard Anderson (PharmGKB) NIGMS

  • Marcie Bough (APhA)

  • Michael Caldwell (Marshfield Clinic)

  • Paul Cusenza (Entrepreneur and Consultant)

  • Beryl Crossley (Quest)

  • Jason DuBois (ACLA)

  • Greg Feero (NHGRI)

  • Felix Frueh (FDA)

  • Lynne Gilbertson (NCPDP)

  • Alan Guttmacher (NHGRI)

  • Joyce Hernandez (Merck)

  • Anne Johnston (Gold Standard, Inc.)

  • Rebecca Kush (CDISC)

  • Frederick S Lee (McKesson)

  • Larry Lesko (FDA)

  • Roberta Madej (Roche Molecular Systems)

  • Steve Matteson (Pfizer)

  • Andrew Mellin (McKesson)

  • Dina Paltoo (NHLBI)

  • Gurvaneet Randhawa (AHRQ)

  • Lisa Rovin (FDA)

  • Pauline Sieverding (VA)

  • Ansalan Stewart (HHS/ASPE)

  • Annette Taylor (Kimball Genetics)

  • Michele Vilaret (National Association of Chain Drug Stores)

  • Phillip Vuchetich (Alegant Health)

  • Mollie Ullman-Cullere (Harvard Partners)

  • Janet Warrington (Affymetrix)

  • Ken Whittemore (SureScripts)