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Outline

Expanding the Research Domains of Rheumatoid Arthritis Clinical Databases: The Promise of Pharmacogenetics Jeffrey Greenberg, MD, MPH NYU Hospital for Joint Diseases New York University School of Medicine December 2006. Outline. Why Pharmacogenetics Case Study: TNF Antagonists

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Outline

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  1. Expanding the Research Domains of Rheumatoid Arthritis Clinical Databases: The Promise of PharmacogeneticsJeffrey Greenberg, MD, MPHNYU Hospital for Joint DiseasesNew York University School of MedicineDecember 2006

  2. Outline • Why Pharmacogenetics • Case Study: TNF Antagonists • Issues of Study Design and Analysis • Future Directions

  3. How Variable is the Human Genome? Size: 3 billion base pairs of DNA Content: 39,114 genes Variation: 1% of base pairs are polymorphic (i.e. 30 million base pairs)

  4. Pharmacogenetics and “Personalized” Medicine Marsh, S. and McLeod HL. Hum. Mol. Genet. 2006 15:R89-93.

  5. Why Is Pharmacogenetics Important? “ Let us […] imagine the despair of the mouse experimentalist when we suggest that he or she randomly allocate treatments to animals of different genetic backgrounds and immunized with different disease triggers.” ….[This is essentially how we treat RA in 2006] Klareskog, L. Nature Clinical Practice Rheumatology (2006) 2, 517.

  6. Arthritis Clinical Databases and Pharmacogenetics • 6 biologic agents have been FDA approved for RA. • Only 1 RCT (Early RA Trial) has published pharmacogenetic studies. • Arthritis clinical databases may represent the ONLY practical approach to advancing the field of pharmacogenetics. • As more biologic agents are approved, biomarkers that can predict response will be increasingly important.

  7. GENETIC POLYMORPHISMS Pharmacokinetic Pharmacodynamic • Receptors • Ion channels • Enzymes • Immune molecules • Transporters • Plasma protein binding • Metabolism

  8. Potential Consequences of Different Drug Metabolism and Drug Receptor Genotypes Evans WE and Relling MV. Science 286:487-491, 1999.

  9. Polymorphic Drug Metabolizing Enzymes

  10. Proof of Principle: Warfarin and Cytochrome P450 Cyp2C9 Alleles Dervieux T et al, Mutat Res 2005:180-194.

  11. Methylene Tetrahydrofolate Reductase (MTHFR) Dervieux T et al, Mutat Res 2005:180-194.

  12. Effect of the MTHFR C677T Polymorphism on Methotrexate Toxicity in RA Patients Urano W et al. Pharmacogenetics 2002: 12(3): 183-190

  13. Pharmacogenetics of TNF Antagonists for the Treatment of RACase Study

  14. How Can We Predict Response (or Failure) ofMethotrexate or TNF Antagonists in RA Patients? % Responders Source: Klareskog L et al. Lancet 2004; 363: 675-81.

  15. Comparison of RA StudiesMTX + TNF Antagonist in Established Disease % Achieving ACR Response Infliximab 10 mg/kg/q4w Adalimumab 40 q2w Etanercept 25 biw Lipsky Weinblatt Weinblatt NEJM 2000 A & R 2003 NEJM1999

  16. Can We Predict Response to Abatacept, but not anti-TNF? Abatacept in TNF Inadequate Responders (ATTAIN) Genovese et al. N Engl J Med. 2005;353:1114.

  17. Can We Predict Risk of Serious Infection for RA Patients Treated with TNF Antagonists? Meta-analysis of Risk of Serious Infection from Adalimumab and Infliximab RCTs Bongartz, T. et al. JAMA 2006;295:2275-2285.

  18. Effect of HLA-DRB1 on ACR 50 Response Etanercept vs Methotrexate in the Early RA Etanercept Trial N=151 in the Etanercept 25 mg arm; OR (95% CI) for effect of SE = 4.3 (1.8 – 10.3) Criswell LA et al. Arthritis Rheum. 2004; 50(9): 2750-2756.

  19. Study Drug Outcome Criswell et al. (2004) Etanercept Positive association Kang et al (2005) Etanercept No association Marotte et al (2006) Infliximab No association Martinez et al (2004) Infliximab No association Miceli-Richard et al (2006) Adalimumab No association HLA-DRB1 and Response to TNF Antagonists Summary of Published Literature

  20. Effect of the TNF –308 G/A Polymorphism on Clinical Response to Infliximab (n=53)EULAR Good Response (Decrease of DAS-28 by 1.2) P=0.0086 Mugnier B. Arthritis Rheum. 2003; 48(7): 1849-1852

  21. Study Drug Outcome Mugnier et al. (2003) INF (N=59) Positive association Fonseca et al (2005) INF (N=22) Positive association Cuchacovich et al (2004) INF (N=22) No association Martinez et al (2004) INF (N=78) No association Padyukov et al (2003) ETA (N=123) Positive association* Criswell et al (2004) ETA (N=151) Positive association† TNF Polymorphisms and Response to TNF Antagonists Summary of Published Literature * Combination of TNF and IL-10 SNPs. ** Extended Haplotype that included TNF/LTA and HLA-DRB1region

  22. Genetic Risk Factors are Stronger Predictors of Developing Infections (UTIs) than Clinical Risk Factors Etanercept vs Methotrexate in the Early RA Etanercept Trial Hughes LB et al.Genes and Immunity 2004; 5: 641-647.

  23. Study Design and Analysis Issues in Pharmacogenetics

  24. SNP Selection for Pharmacogenetics • “Functional” SNP of candidate gene • High density genotyping of coding and non-coding SNPs of a specific candidate gene • SNP(s) of multiple genes in a metabolic pathway • Whole genome scan

  25. High Density Genotyping Project across the Whole Genome: Insights into the Correlation Structure of Alleles

  26. Insights from the HapMap Project and Related Studies

  27. Allele Frequencies of Drug Metabolizing Enzymes and Other Genes Vary across Different Population Groups CYP1A1 A. Bantu, Ethiopian, Afro-Caribbean B. Norwegians, Ashkenazi Jews, Armenians C. Chinese, New Guineans From Wilson, et al. Nature Genet 29:265-269, 2001 GSTM1 CYP2C19 DIA4 NAT2 CYP2D6

  28. Gene CommonSNPs genotyped Related Haplotypes Haplotype Tag SNPs Haplotype Coverage by Tag SNPs TNF-α 9 4 4 87% IL-1β 20 4 3 100% IL-6 26 4 3 99% CTLA-4 11 12 5 100% 73 30 15 -- Can We “Tag” Candidate Inflammatory Gene SNPs Hypothesized to Modulate TNF Antagonist Response?

  29. Future Directions

  30. Example of a diagnostic DNA microarray of polymorphisms of candidate genes to predict response and risk of toxicity (e.g. chemotherapeutic agent) Evans WE, Relling MV. Pharmacogenomics: Translating functional genomics into rational therapeutics. Science 286:487-491, 1999.

  31. Pharmacogenomic Studies May Be More Relevant to Predicting Response for Pleiotropic Drugs such as TNF Antagonists Roden, D. M. et. al. Ann Intern Med 2006;145:749-757

  32. Gene Profiling in White Blood Cells Predicts Infliximab Responsiveness in Rheumatoid Arthritis • 33 RA patients treated with Infliximab • Responders vs nonresponders (decrease of DAS-28  1.2) • PBMC isolated from venous blood and total RNA extracted • mRNA collected at baseline hybridized to a microarray of 10,000 non-redundant cDNAs. • Real-time, quantitative reverse transcription PCR of selected mRNA also performed. • Statistical analysis included t- test and SAM (Significance Analysis of Microarrays) with a false discovery rate of <1%. Lequerre T et al. Arthritis Research and Therapy 2006: 8 R105

  33. Gene Profiling in White Blood Cells Predicts Infliximab Responsiveness in Rheumatoid ArthritisResults • Overall 16/33 (48%) were EULAR responders. • The 33 patients randomly divided into:a) “Training” cohort (n=13) b) “Validation” cohort (n=20) • 41 mRNAs were differentially expressed in responders versus nonresponders s a function of the response to treatment • Differentially expressed genes were confirmed by qRT-PCR: • 20 transcript set • 8 transcript set Lequerre T et al. Arthritis Research and Therapy 2006: 8 R105

  34. Gene Profiling in White Blood Cells Predicts Infliximab Responsiveness in Rheumatoid Arthritis

  35. The Future: Incorporate Biomarkers into Clinical Trials Evans and Johnson, Ann Rev Genom Hum Genet 29-39, 2001

  36. Pharmacogenetics and “Personalized” Medicine Marsh, S. and McLeod HL. Hum. Mol. Genet. 2006 15:R89-93.

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