1. Dr Richard FitzGerald Molecular & Clinical Pharmacology Institute of Translational Medicine University of Liverpool Richard.Fitzgerald@liverpool.ac.uk Same Medicine, Different Result Pharmacogenetics: Where Are We Now?

2. The drugs don’t work.......

3. ....... they just make it worse.

4. The problem: variability ‘If it were not for the great variability among individuals, medicine might as well be a science and not an art.’ Sir William Osler, 1892

5. Pythagoras (6 th Century B.C.) “…..be far from fava beans consumptions” Met death in Ancient Italy because he refused to cross a field of beans Many theories:  Contained souls  Looked like testicles  flatulence  Medical reason Fava beans RBC haemolysis FAVISM

6. ‘Chemical Individuality’ First suggested by Sir Archibald Garrod that genetics may affect chemical transformations He used the example of alkaptonuria (1902) ‘One gene, one enzyme’

7. Modern pharmacogenetics

9. Types of Genetic Variation

11. Drug Response: a complex trait?

12. The early years: one gene, one disease Robert Smith investigated debrisoquine (a commercially available anti-hypertensive) He took the tablet, along with most of his laboratory staff He collapsed and became markedly hypotensive. Nobody else did.

14. CYP2D6 Major Alleles

15. Nortriptyline pharmacogenetics

16. Codeine phosphate

17. Drug metabolising enzymes Most DME have clinically relevant polymorphisms Those with changes in drug effects are separated from pie.

18. Azathioprine 6-Mercaptopurine 6-thioinosine nucleotide 6-thioguanine nucleotides Thiouric acid 6-Me MP TPMT Xanthine oxidase HGPRT IMPDH ImmunosupressionClinical benefit

19. TPMT (Thiopurine methyltransferase) Allelic polymorphism High TPMT 89% Intermediate TPMT 11% Low TPMT 1/300 ?very high TPMT Severe Bone Marrow Suppression High risk of marrow suppression Low risk ? poor responders - + clinical response

21. PGx: current applications

22. Abacavir Hypersensitivity Nucleoside analogue Reverse transcriptase inhibitor Hypersensitivity 5% Fever, skin rash, gastro- intestinal symptoms, eosinophilia within 6 weeks Re-challenge results in a more serious reaction

23. Abacavir Hypersensitivity Clinical phenotypeCausal chemical Association with HLA-B*5701 Clinical genotype Incidence before and after testing for HLA-B*5701 CountryPre testingPost testingReference Australia7%<1%Rauch et al, 2006 France12%0%Zucman et al, 2007 UK (London)7.8%2%Waters et al, 2007

24. PGx: effects on drug usage Data from RLBUHT courtesy of Prof Saye Khoo

25. PREDICT-1

26. Abacavir Genetics: Why so Rapidly Implemented? Implemented even before RCT evidence  In some cases, observational study designs may provide adequate evidence Successful implementation was because of several factors:  Good and replicated evidence of a large genetic effect size  Clinician community amenable to rapid change in clinical practice  Vocal and knowledgeable patient lobby

27. Carbamazepine-induced hypersensitivity reactions 5% of patients on carbamazepine (CBZ) develop hypersensitivity reactions 10% in prospective SANAD study (UK) Clinical manifestations Maculopapular exanthema usually mild Hypersensitivity reaction (HSS) 1/1000 patients Fever, hepatitis, eosinophilia Stevens-Johnson syndrome Toxic epidermal necrolysis 5-30% fatality rate

28. FDA warning PATIENTS WITH ASIAN ANCESTRY SHOULD BE SCREENED FOR THE PRESENCE OF HLA-B*1502 PRIOR TO INITIATING TREATMENT WITH Carbamazepine.

29. To prospectively identify subjects at risk for SJS 4877 CBZ naive subjects from 23 hospitals The Taiwan SJS Consortium HLA-B*1502 testing → 0 incidence of SJS/TEN

30. University of Liverpool (SANAD, EUDRAGENE, Swiss, WT Sanger, Harvard) EPIGEN Consortium (Ireland, Duke University, UCL, Belgium) Faculty of 1000 -top 2% of published articles in biology and medicine American Academy of Neurology meeting- voted as one of the top articles in neurology this year

31. 22 patients with HSS 43 patients with MPE 2691 healthy control subjects 1296 healthy control subjects McCormack et al. NEJM 2011 HLA-A*3101

32. P= P=0.03 P=8 x10 -7 P=8 x10 -5 P=1x10 -7 Pooled analysis of case-control studies McCormack et al. NEJM 2011

33. GWAS identifies HLA-A*3101 allele as a genetic risk factor for CBZ- induced cutaneous adverse drug reactions in Japanese population HLA-A*3101 Ozeki et al. Hum Mol Genet 2011

34. Conclusions HLA-A*3101 - a prospective marker for CBZ hypersensitivity Associated with several phenotypes Further work needed to enable clinical use Need for consortia Possibility of rare variants and CNVs (exome-sequencing/WGS) Mechanistic studies to follow genetics

35. Flucloxacillin-Induced Cholestatic Hepatitis: Whole Genome Scan Illumina 1 million SNP array Strong (P=10 -30 ) association with SNP in LD with HLA-B*5701 Weaker association with novel marker on chromosome 3 (p < 1.4 x 10 -8 ) Weak association with copy number polymorphism Performed in collaboration with the Serious Adverse Event Consortium Daly at al, 2009

36. 1.Implicated SNP is in the SLCO1B1 gene (transporter) 2.Shown with simvastatin 40mg and 80mg 3.C variant may account for 60% of the cases of myopathy

37. Clopidogrel Pharmacogenetics

38. Stent Thromb HR 2.61; 95% CI 1.61-4.37, P<0.00001

39. All events: HR 1.57; 95% CI 1.13-2.16, P=0.006

40. Conclusions Clear adverse effect of the CYP2C19*2 polymorphism on clinical and pharmacodynamic outcomes  PD Meta-analysis limited by multiple outcome measures Potential utility in CYP2C19*2 as marker of clopidogrel non-response and risk of adverse outcome Translation into clinical practice  Increase dose of clopidogrel from 75mg/day to 150mg/day – Evidence from CURRENT-OASIS 7 trial – Bleeding risk  Use of alternative anti-platelet drugs (Prasugrel, Ticagrelor) – Better platelet inhibition – Higher rates of bleeding (+ other adverse effects) – Benefit may be only seen in those with the CYP2C19*2 allele – Cost

41. Warfarin: a more complex variation Widely used drug A variety of acute/chronic indications Large numbers of patients 6% of all patients over 80 years of age Narrow therapeutic index Drug interactions and alcohol Efficacy

42. Bleeding complications: 10-24 per 100-patient years 10% of all ADR-related hospital admissions

43. The clinical phenotype 10-50 fold variability in dose requirements Increased age; decreased requirements  8% decrease in warfarin dose per decade  Enhanced responsiveness (PD)  Reduced clearance (PK)

45. Warfarin and metabolism by CYP2C9 CYP2C9*1Wild Type Arg 144 Ile 359 CYP2C9*2Arg 144 Cys : interaction with cytochrome P450 reductase CYP2C9*3Ile 359 Leu : substrate binding site : affects K m, V max Steward et al, Pharmacogenetics (1997), 7, 361-367 Variant alleles have 5-12% of the activity of wild-type

46. Warfarin and pharmacokinetics CYP2C9 genotype Number of patients Aggregate mean dose (mg) CYP2C9*1*16395.5 CYP2C9*1*22074.5 CYP2C9*1*31093.4 CYP2C9*2*273.6 CYP2C9*2*3112.7 CYP2C9*3*351.6

47. Warfarin and pharmacodynamics Polymorphisms in vitamin K epoxide reductase (VKOR)C1 Associated reductions in warfarin dose Accounts for greater variance in dose than CYP2C9 Variation in genes encoding γ-glutamyl carboxylase and factors II, VII and X

48. Genetic and Environmental Factors and Dose Requirements of Warfarin Independent effects of VKORC1 and CYP2C9: VKORC1:p<0.0001, r 2 = 0.29 CYP2C9:p=0.0003, r 2 = 0.11 Wadelius et al. 2005 Age:p<0.0001, r 2 = 0.10 Body weight:p=0.0018, r 2 = 0.05 55%

49. GENETIC Cytochrome P450 polymorphisms Vitamin K epoxide reductase Phase II metabolising genes Drug transporters Clotting factors Disease genes ENVIRONMENTAL Sex Age Smoking Interacting drugs Alcohol Compliance Diet Warfarin: multiple genes/factors

51. Test interpretation

52. The potential for complication

53. Will pharmacogenetic testing be any better than more intensive INR monitoring?

54. Pharmacogenetic algorithm was superior to clinical algorithm or fixed dosing Greatest benefit seen in 46% of the population who require either 7mg/day

55. Two Randomised Controlled Trials COAG NIH-sponsored US trial 1200 patients Genetic algorithm vs clinical algorithm %TIR as primary outcome measure EU-PACT EU FP7 sponsored EU trials 3 trials: warfarin, phenprocoumon, acenocoumarol 900 patients in each (2700 total) Final study design completed %TIR as primary outcome measure

57. Closing The Loop Replicate the association Demonstrate clinical validity and utility Demonstrate a positive clinical outcome Identify a variant Show an association

58. Pre-clinical Phases I, II, III Phase IV Systems Biology Minimise risk and maximize benefit Uncertainty reduced but not abolished Minimise risk and maximize benefit Uncertainty reduced but not abolished New technologies: Pharmacogenomics Proteomics Metabolomics New technologies: Pharmacogenomics Proteomics Metabolomics

59. Advances in Technologies 14 billion bases/day

60. PGx and Prospective Utility Drug development process Potential prospective use of PGx to enhance success Increase confidence US$1 billion to market a new drug Target discovery Proof of concept Candidate gene/whole genome association

61. Current Status of Genetic Tests “Today, there is no mechanism to ensure that genetic tests are supported by adequate evidence before they are marketed or that marketing claims for such tests are truthful and not misleading. Misleading claims about tests may lead health-care providers and patients to make inappropriate decisions about whether to test or how to interpret test results.” Science, 4 April 2008

62. Personalised Medicines: The Future? Many recent advances Here to stay, and likely to be supported by increasing evidence Evolutionary process, not revolutionary Lot of cynicism about personalised medicine approaches Evidence being required is much greater with other tests

63. Personalised vs. Empirical Paradigms ObservationAction Trial and error response Empirical (intuitive) medicine ObservationTestAction Predictable response Personalised (precision) medicine

64. Terminology Personalised Medicine Personal Medicine not We cannot truly personalise medicines No test or prediction rule will be 100% effective

65. “ What we know about the genome today is not enough for all the miracles many expect from this field. There’s a lot about what regulates the genes and how they interact that we still need to understand. We won’t have the answers by tomorrow.” 29 th April 2008 Arno Motulsky