1. Principles of Pharmacogenomics Readings: Pharmacogenomics (Rothstein,ed. Chapter 2, 8, 9, 11) ©2003-2008 Gus Rosania

3. The Simple Definition ©2003-2008 Gus Rosania Pharmacogenomics: using genetic information to predict whether a drug will help make a patient well or ill. +

8. 2. Forces Driving Pharmacogenomics into Healthcare Practice ©2003-2008 Gus Rosania

9. QUESTION What are the driving forces behind pharmacogenomics?

10. 1A. Adverse drug reaction costs insurance co. billions $$ Reasons: Prescription error, overdosing, drug interactions, population genetic variables. Drug interactions: Two or more drugs metabolized or eliminated by the same mechanism administered at same time can saturate mechanism, leading to toxicity. Adverse drug effects are estimated to be the 5 th or 6 th cause of illness and death in the US. Costs estimates range between 30 to 150 billion a year. Adverse drug effects account for up to 7% of hospitalizations in US ©2003-2008 Gus Rosania

11. 1B. Many drugs are intrinsically unsafe and could be made safer Anticoagulants: Warfarin clots, stroke bleeding/death Anticancer: Paclitaxel Cancer grows immunocytopenia Pain killers: Morphine No effect Addiction/death Statins Lipitor high cholestrol myopathy/death Acetaminophen Tylenol No pain relief Liver failure/death Example Too little Too much ©2003-2008 Gus Rosania

12. 1C. Current dosage strategy: bias towards low efficacy/low tox Condition Efficacy Annual Rx cost Alzheimer’s 30% $1,500 Analgesics (Cox2) 80% $1,350 Asthma 60% $ 330 Cardiac arrythmia 60% $ 650 Depression 62% $ 700 Diabetes 57% $1,300 HCV 47% $5,000 Incontinence 40% $1,000 Migraine (acute) 52% $ 240 Migraine (prophylaxis) 50% $ 600 Oncology 25% $3,500 ©2003-2008 Gus Rosania

13. 1D. The cost of marketed drug failures Cost of developing a new drug: $500 to $700 million Time from drug patent to product launch: 12 years Time until patent expires: 7 years ©2003-2008 Gus Rosania

16. Drug X Efficacy in an Individual Patient Efficacy Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 ©2003-2008 Gus Rosania

17. Drug X Efficacy in Patient Population Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 ©2003-2008 Gus Rosania

18. DoseX-%efficacy curve Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 % Patients Cured by Drug 100 90 80 70 60 50 40 30 20 10 0 ©2003-2008 Gus Rosania

19. Drug X Toxicity for Individual Patient Toxicity Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 ©2003-2008 Gus Rosania

20. Drug X Toxicity in Patient Population Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 ©2003-2008 Gus Rosania

21. DrugX-toxicity curve Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 % Patients showing ADR 100 90 80 70 60 50 40 30 20 10 0 ©2003-2008 Gus Rosania

22. Drug X combined efficacy-toxicity curves % Patients Showing 100 90 80 70 60 50 40 30 20 10 0 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 % Patients 100 90 80 70 60 50 40 30 20 10 0 ©2003-2008 Gus Rosania

23. Safe drug: large window Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Therapeutic window Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 ©2003-2008 Gus Rosania

24. Efficacy-toxicity curves for safe drug % Patients Showing 100 90 80 70 60 50 40 30 20 10 0 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 % Patients Responding to drug 100 90 80 70 60 50 40 30 20 10 0 ©2003-2008 Gus Rosania

25. Unsafe drug: small window Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Therapeutic window ©2003-2008 Gus Rosania

26. Drug-Toxicity Curves for unsafe Drug % Patients Showing 100 90 80 70 60 50 40 30 20 10 0 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 % Patients 100 90 80 70 60 50 40 30 20 10 0 ©2003-2008 Gus Rosania

28. Use genetic info to enhance the therapeutic index TI without PG ©2003-2008 Gus Rosania Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Therapeutic index w/ PG

29. What are the steps for translating pharmacogenomic info. from research into practice? ©2003-2008 Gus Rosania

30. Step 1. Identify SNPs in genes relevant to drug efficacy or tox Human Genome: 2,900,000,000 total base pairs 10,000,000 total single nucleotide polymorphisms (SNP) 300,000 variant haplotypes 10,000 haplotypes in pharmacologically-relevant genes ©2003-2008 Gus Rosania

31. Step 2. Retrospectively, find SNPs associated with response Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Patient 11 Patient 12 Good response No response No response Good response No response No response Good response Good response Good response Good response No response No response ATGCTTCCCTTTTAAA ATTGTTCCCTTTTAAA ATTGTTGCCTTTTAAA ATGGTTGCCTTTTAAA ATAGTTGCCTTTTAAT ATAGTTGCCTTTTAAT ATGATTGCCTTTTAAA ATGATTGGCTTTTAAA ATGTTTCGCTTTTAAA ATGTTTTGCTTTTAAA ATTTTTTGCTTTTAAA ATCTTTTGCTTTTAAA SNP: single nucleotide polymorphism Good response Good response Good response Good response Good response 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ©2003-2008 Gus Rosania

32. Step 3. Prospectively, determine if those SNPs affect therapeutic outcome G G G G G G G G G G G G G G G G G G G G G G G G G G Treat 25% cure 50% cure Determine statistical significance (the probability that such a difference is due to random chance) ©2003-2008 Gus Rosania

33. Step 4. Require physician or pharmacist to perform those tests ©2003-2008 Gus Rosania