The document discusses the principles of pharmacogenomics. It defines pharmacogenomics as using genetic information to predict drug response. The key forces driving pharmacogenomics in healthcare are reducing adverse drug reactions and drug development costs. There are four steps to translating pharmacogenomic research to practice: 1) identify genetic variants related to drug response, 2) associate variants with past responses, 3) validate prospective impact on outcomes, and 4) require genetic testing before treatment.

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

2. Outline What is Pharmacogenomics? How does it work? What are the forces pushing pharmacogenomics into health care practice What are the four essential steps for translating pharmacogenomics info from research to practice. ©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. +

4. Pharmacogenomics as a field Pharmacogenomics studies how genes determine interindividual variability in drug response. Combines many different fields: Genetics, genomics, molecular biology, pharmacology, pharmaceutics, toxicology, population biology, statistics. For Pharmacists: It may be used to predict how a patient may respond to drug, with the help of a genetic test. ©2003-2008 Gus Rosania

5. What makes pharmacogenomics possible today but not 10yrs ago? Sequencing of the human genome reveals 2.9 billion base pairs that are constant, narrowing down variability to about 3 million base pairs, of which 100,000 capture the full human variation and <10,000 may be pharmaceutically relevant. Advances in genome sequencing technology make possible addressing those individual base pairs. Automatization and miniaturization significantly drive down cost of DNA sequencing reaction. Computer technology and computer networks facilitate handling of data. ©2003-2008 Gus Rosania

6. Current Impact Pharmacogenomics broadly adopted in drug discovery and development phases in the pharmaceutical industry. Pharmacogenomics research heavily invested upon by public and private companies 3. Pharmacogenomics seen as the future of medicine – so called PERSONALIZED MEDICINE. ©2003-2008 Gus Rosania

7. Future Trends Point-of-care (Doctor’s office) genetic testing Personal Genomics Population sequencing ©2003-2008 Gus Rosania

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

14. 1E. Consumer advocacy/Litigation The sequencing of the human genome has led to identification of genetic markers for drug toxicity. The patients may hold pharmaceutical companies liable for not requiring pharmacogenomic testing in association with drug prescription. ©2003-2008 Gus Rosania

15. Goal: Use genetic info. to broaden drug’s therapeutic index Efficacy: % patients cured at a given dose Toxicity:% patients exhibiting side effects at a given dose Therapeutic index: Dose range at which drug shows highest efficacy and low toxicity ©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

27. How Can Pharmacogenomics Be Used to Enhance Therapeutic Index? Don’t Treat Non-Responders (Patient Stratification) Don’t treat Those most likely to be affected by Toxicity (patient stratification) Adjust Dose To Maximize Efficacy While Avoiding Toxicity for each individual patient (Individualized therapy) ©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