Pharmacogenomics in Action – A Non-Discretionary Discipline in Tomorrow’s Drug Development

1. Pharmacogenomics in Action – A Non-Discretionary Discipline in Tomorrow’s Drug Development Lloyd Curtis PRISM May 2009

2. Disclaimer • All views presented are personal and not necessarily those of GSK

3. Pharmacogenomics - Definition • There is now an established regulatory definition of Pharmacogenomics • ICH HARMONISED TRIPARTITE GUIDELINE – E15 (November 2007) • DEFINITIONS FOR GENOMIC BIOMARKERS, PHARMACOGENOMICS, PHARMACOGENETICS, GENOMIC DATA AND SAMPLE CODING CATEGORIES • http://www.pmda.go.jp/ich/e/step4_e15_e.pdf

4. ICH HARMONISED TRIPARTITE GUIDELINE – E15 • Pharmacogenomics (PGx) is defined as: • The study of variations of DNA and RNA characteristics as related to drug response. • Pharmacogenetics (PGt) is a subset of pharmacogenomics (PGx) and is defined as: • The study of variations in DNA sequence as related to drug response.

5. ICH HARMONISED TRIPARTITE GUIDELINE – E15 • PGx and PGt are applicable to: • drug discovery, drug development, and clinical practice. • Drug response includes: • drug absorption and disposition (e.g., pharmacokinetics, (PK)), and drug effects (e.g., pharmacodynamics (PD), drug efficacy and adverse effects of drugs).

6. Pharmacogenomics – The Promise • Improve the diminishing productivity of drug development • Reduce the high proportion of patients who: • Receive no benefit from administered drugs • Experience adverse reactions

7. Pharmacogenomics & Regulators • USA - FDA Critical Path Initiative – 2004 • The Interdisciplinary Pharmacogenomics Review Group • Pharmacogenomics Working Group • Europe - EMEA Road map – 2005 • The Pharmacogenomics Working Party • Japan – PMDA • The Pharmacogenomics Discussion Group

8. FDA initiatives in Pharmacogenomics • Workshops - use of biomarkers in drug development and clinical practice • Issued guidances • Voluntary Genomic Data Submission Program • Developed online educational tools • ‘Re-labelling’ as well as inclusion in new labels • Pilot process to qualify novel biomarkers • Creation of consortia - e.g International Serious Adverse Event Consortium

9. Recognized challenges • Qualification of biomarkers • extent of information needed to understand clinical utility • What clinical trial data are required to qualify biomarkers • Acceptable clinical trial design • prospective Randomised Controlled Trial, observational cohort, retrospective? • Can modelling and clinical trial simulation be used as evidence of biomarker qualification?

10. Safety example - Antiretroviral drug abacavir • Abacavir hypersensitivity reaction (ABC HSR) affected 5 to 8% of clinical trial subjects • Multi-organ clinical syndrome – typically fever and/or rash and/or constitutional, GI and/or respiratory symptoms • Rechallenge contraindicated and can be fatal • Clinical diagnosis imprecise due to concurrent drugs/illnesses • Results in 2-3% false positive rate in blinded clinical trials

11. Abacavir - background • Effective risk management programme created (educational materials, labelling, pharmacovigilance, etc) • Pharmacogenetic research identified HLA-B*5701 allele more common in Caucasian patients with clinically suspected ABC HSR (2001) • Sensitivity/specificity of test varied between studies and racial populations – limitations of case-ascertainment (and unethical to rechallenge)

12. ABC HSR – PREDICT-1study • Prospective Randomized Evaluation of DNA Screening In a controlled Clinical Trial to determine the clinical utility of HLA-B*5701 screening prior to ABC-containing therapy • Two co-primary endpoints: • Rate of clinically-suspected HSR • Rate of immunologically confirmed HSR (HSR plus positive patch test reaction)

13. PREDICT-1 study design ABC-containing regimen HSR monitoring according to Standard of Care ABC-naïve Subjects N=1956 Exclude subjects with HLA-B*5701 Blinded randomisation (1:1) ABC-containing regimen HSR monitoring according to Standard of Care plus PGx screening Commence ABC in HLA-B*5701 negative Subjects 6 weeks of study observation

14. OR 0.40 P < 0.0001 (0.25, 0.62) 9 8 Control arm (Standard of Care) 7 Prospective HLA-B*5701 screening arm 6 5 OR 0.03 P < 0.0001 (0, 0.18) Incidence (%) 4 3 7.8% (66/847) 3.4% (27/803) 2 2.7% (23/842) 0.0% (0/802) 1 0 Clinically Suspected HSR Immunologically Confirmed HSR PREDICT-1 results

15. ABC HSR – SHAPE study • Study of Hypersensitivity to Abacavir and Pharmacogenetic Evaluation: • Retrospective, case-control trial in self-reported Black and White patients to assess the generalisability of HLA-B*5701 • Note: HLA-B*5701 carriage frequency lower in Blacks than Whites

16. Black and White subjects with clinically-suspected ABC HSR (CS-HSR) Black & White subjects enrolled in KLEAN, ALOHA, CNA30027, CNA30032 ABC skin patch test Identify ABC-tolerant subjects who provided PGx consent and sample CASES CONTROLS Skin patch test positive (IC-HSR) Skin patch test negative PGx evaluation Up to 200 controls for each race PGx evaluation PGx evaluation SHAPE study design

17. SHAPE pharmacogenetic analyses

18. EMEA and ABC HSR EU Summary of Product Characteristics update, Jan 2008 • ‘Before initiating treatment with abacavir, screening for carriage of the HLA-B*5701 allele should be performed in any HIV-infected patient, irrespective of racial origin’(Note: in EU ‘should’ means mandated)

19. FDA and ABC HSR • US Prescribing Information update, July 2008 • ‘Prior to initiating therapy with abacavir, screening for the HLA‑B*5701 allele is recommended; this approach has been found to decrease the risk of a hypersensitivity reaction’.

20. Advancing technology - Potential for Routine Genome-Wide Analysis • Current genotyping technologies make it possible to investigate adverse drug reaction (ADR) genetics during the course of clinical trials and post-approval pharmacovigilance • Candidate gene panels • Select up to 10,000’s genetic markers in 100’s of candidate genes • Genome-wide panels • 0.5-1+ million markers

21. The POPRES Initiative • Population Reference Sample • Facilitate exploratory population, disease, and pharmacogenetic research through access to • DNA from a variety of representative population samples • Genotyped for genome-wide, as well as other focused SNP panels • Genotypic and basic demographic data from POPRES is publicly available via dbGaP

22. Abacavir HSR Study Design • Sample • Cases • 22 abacavir-treated, White, HIV+ subjects that experienced HSR while on treatment • Reference sample (i.e. “Controls”) • 203 POPRES subjects of European origin (US, Canada, Australia) • Genotype data • Affymetrix 500K SNP chip set

23. “Standard” Genome-Wide Analysis • Analyze each marker of the 500K panel in all ABC HSR cases versus population reference controls • Identify markers and regions most strongly associated with ADR • Methods • Filter out markers with significant deviations from Hardy-Weinberg proportions: α = 0.05/5x105 • Single SNP allelic and genotypic exact tests • Can we identify the known HLA-B association with 22 cases and 203 population reference controls?

24. WGS Identifies HLA-B Region among Top Candidates

25. Flucloxacillin and Liver Injury • Flucloxacillin important cause of drug induced liver injury (DILI) in Europe and Australia • 8.5 cases/100,000 • GWS and Candidate gene studies in parallel • Cases – 51 definite/possible fluclox DILI • GWS controls 282 matched gender and population origin • Candidate gene controls 64 patients exposed fluclox without DILI Daly et al, 6th Wellcome Trust Conference on Pharmacogenomics (CSH) 2008

26. GWS and Candidate Gene Studies Identify HLA-B*5701 As the Major Risk Allele of DILI Caused by Flucloxacillin • GWS • Illumina Human1M chips • 84% cases vs <5% controls, odds ratio 36 • Candidate genes • major histocompatibility complex (MHC) region and a few other immune-system related genes • 84% cases vs 6% controls Daly et al, 6th Wellcome Trust Conference on Pharmacogenomics (CSH) 2008

27. Conclusion on Potential for Routine Genome-Wide Analysis • Large-scale genotyping offers new opportunities to consider the impact of PGx on ADRs • ADR genetic risk factors with reasonably large effects can be identified with relatively small case sample sizes • Availability of dense, genome-wide genotype data on suitable population reference samples can facilitate rapid, exploratory research • This strategy would have likely identified HLA-B region for abacavir-associated HSR with 15-20 cases • Application of sequential methods as ADRs accrue can lead to early identification of ADR PGx risk factors

28. Pharmacogenomics Promise Unfulfilled? • FDA Drug Safety Newsletter – Winter 2008 • Safety Examples • Drug resistance mutations in HIV • Rapid and slow metabolizers – codeine • Warfarin VKORC1 (target) & P450 2C9 (metabolism)- recommended (2) • HLA-B*1502 and carbamazepine associated Stevens Johnson syndrome in populations of specific Asian ancestry - recommended (2)

29. Newsletter Efficacy examples • Imatinib for bcr-abl tyrosine kinase in several tumour types – information only (3) • Cetuximab for epidermal growth factor receptor (EGFR) in • head and neck cancer - information only (3 ) • colorectal cancer– required (1) • Trastuzumab for variants in the Her2 receptor in breast cancer – required (1)

30. Conclusions • Strong regulatory pressure to pursue pharmacogenomic endeavours in drug development – particularly FDA • Modest results so far with more hope for safety than efficacy • Pharmacogenomics will be ‘non-discretionary’ driven by regulators rather than demonstrable success

31. Conclusions • Support technologies developing at rapid pace viz: • Make available a biomarker data repository to store and retrieve flexibly pharmacogenomic data types including gene chip data, sequencing data and other “omics” data • Enable the integration of traditional phenotypic clinical trial data with biomarker data including genotypic data • Provide flexible & powerful data visualisation, mining and statistical tools to seek for and find correlations between AEs and gene expression • Ensure workflow tools exist to document the provenance of an analysis and provide repeatability

32. Contributors POPRES Project • Sponsors • Eric Lai • Dan Burns • Project lead • Matt Nelson • Team members • Linda Briley • Clive Bowman • Meg Ehm • Kelley Johansson • Brendan Jones • Karen King • Heide Stirnadel • Additional support, former team members • Donna Backshall • Devon Kelly • Michael Klotsman • Yuka Maruyama • Annie McNeill • Tony Morris • Jill Ratchford Sequential PGx Methods • Clive Bowman • Silviu-Alin Bacanu • Michael Lawson Abacavir Case Study • Cindy Brothers • Charles Cox • Kirstie Davies • Seth Hetherington • Jaime Hernandez • Arlene Hughes • Mike Mosteller • Bill Spreen • Liling Warren GSK PGx • Allen Roses • Li Li • Stephanie Chissoe • David Yarnall Special Acknowledgement Thanks to the clinical trial and reference sample participants who provided both informed consent and blood samples for the PGx research Thanks also to the clinical investigators and their study staff, the GSK clinical development teams and all POPRES collaborators

33. The chemical name of abacavir sulfate is (1S,cis)-4-[2-amino-6-(cyclopropylamino)-9H-purin-9-yl]-2-cyclopentene-1-methanol sulfate (salt) (2:1). Abacavir sulfate is the enantiomer with 1S, 4R absolute configuration on the cyclopentene ring. It has a molecular formula of (C14H18N6O)2•H2SO4 and a molecular weight of 670.76 daltons. It has the following structural formula: