Le sfide del futuro: la farmacogenetica

1. Le sfide del futuro: la farmacogenetica Corso di Genetica Umana Facoltà di Medicina e Chirurgia dell’Università di Torino Alberto Piazza

2. L’ identità genetica nella nostra specie Homo sapiens sapiens • I vari genomi sono identici al 99.9% • 3,200,000 nucleotidi sono differenti • Tra ogni coppia di individui i genomi differiscono per circa 3 milioni di basi nucleotidiche • Tra ogni coppia di individui i proteomi differiscono per circa 100,000 aa

3. Single Nucleotide Polymorphism (SNP) • Si tratta del polimorfismo più presente in natura • Circa 10 milioni di SNPs negli uomini GATTTAGATCGCGATAGAG GATTTAGATCTCGATAGAG L’ SNP consiste in una posizione nel genoma in cui sono presenti in una popolazione due o più basi differenti, ciascuna con una frequenza >1%.

4. Tipidi SNPs • Genici, SNPs codificanti • non-sinonimi • Mantengono/alterano la struttura/funzione della proteina • sinonimi • Mantengono/alterano lo splicing • Genici, SNPs non-codificanti • regolatori • Mantengono/alterano l’espressione genica • intronici • Mantengono/alterano l’espressione genica o losplicing • SNPs concatenati • comunemente intergenici

5. Insorgenza delle mutazioni nel tempo Disease Mutation Common Ancestor present time Gli aplotipi Variazione nei genomi individuali entro una popolazione

6. Whole Genome Associations Disease Population N=500 Matched Control Population N=500 • ~3,000,000 common SNPs across genome • Representing every gene 22 1 Regions of association P value 1 22 Chromosomal Location Informatics to identify gene(s) mapped to associated SNP

7. Control Non-responder Disease Responder Allele 1 Allele 2 Marker A: Allele 1 = Allele 2 = Marker A is associated with Phenotype Association studies

8. Examples of SNPs that cause disease Primary hypomagnesemia • patients unable to maintain sufficient levels of Mg2+ in their serum • Affected gene: Na/K ATPase g subunit (123G->A = Gly->Arg in transmembrane region) – mutation prevents targeting of the protein to cell membrane Mitochondrial SNPs • >50 known disease-causing SNPs in mtDNA • Most often affect tissues with high energy consumption BRCA1 (breast cancer-associated antigen 1) • Silent mutation in coding exon affects splicing (A. Krainer, CSHL) • Exons contain exonic spplicing enhancers and exonic splicing silencers – mutations lead to exon skipping and aberrant inclusion, respectively

9. Examples of SNPs that cause altered non-disease phenotypes Glucose-6-phosphate dehydrogenase and favism • First enzyme in the oxidative branch of pentose phosphate pathway, which reduces NADP+ to NADPH • Different mutations result in partially or completely inactive enzyme • People (10%) with inactive enzyme experience lysis of red blood cells when consuming fava beans (contains H2O2 – NADPH is needed to detoxify it) CytP450 mutations and drug responsiveness • Cytochrome P450 – activates many pre-drugs into active therapeutic compounds • Different people can be divided into typical, poor, and ultra-rapid metabolizers • two genes in human: • 2D6 – required by more than 40 pre-drugs for activation; 12 known SNPs altering the gene’s activity • 2C19 – activates mephentyoin (epilepsy); 2-3% Caucasoids and 23% Asians are poor metabolizers

10. Studying the relationship of genetic variation and drug efficacy genome-wide: Pharmacogenomics Drug dose (rel.)

11. Polymorphic drug metabolizing enzymes

13. Basel, 25 June 2003 Roche Diagnostics Launches the AmpliChip CYP450 in the US, the World’s First Pharmacogenomic Microarray for Clinical Applications It is the first chip using Affymetrix technology that meets federal standards for clinical use Amplichip CYP450: The first commercial clinical test platform

14. The route to a new drug…is a long one Exploratory Development Full Development Discovery Phase IV Phase I Phase II Phase III 0 15 10 5 Years 11-15 Years Marketed Drug Idea Patent life 20 years

15. …and an expensive one! It costs >$800 million to get a drug to market $ Millions spent in 9 months in 2001 3,332 2,660 2,487 2,281 1,916 1,955 1,740 1,645 1,499 1,402 1,116 934  SGP ABT AHP BMY LLY MRK PHA AZN AVE JNJ GSK

16. The study of genome-derived data, including human genetic variation, RNA and protein expression differences, to predict drug response in individual patients or groups of patients. Pharmacogenomics defined Pharmacogenomics includes Pharmacogenetics

17. Pharmacogenomics • Human Genetics • SNPs • Haplotypes • Sequencing • Expression Profiling • Specific transcript levels • Total RNA profiling • Proteomics • Specific biochemical markers • Protein profiling • Phenotype • Drug response • Disease Prediction

18. Applying Pharmacogenomics Discovery Development DISEASE TARGET SELECTING PHARMACO- GENETICS RESPONDERS GENETICS VARIABILITY Improving Early Decision Making Choosing the Best Targets Better Understanding of Our Targets Predicting Efficacy and Safety .

19. Goal: use geneticsto 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

20. Drug Efficacy in an Individual Patient Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Efficacy

21. Drug Efficacy in Patient Population Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10

22. Drug Toxicity for Individual Patient Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Toxicity

23. Drug Toxicity in Patient Population Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10

24. Unsafe drug: small window Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Therapeutic window

25. Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9 Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 TI with pharmacogenomics Use genetic info to enhance the therapeutic index(TI) TI without pharmacogenomics

26. What are the steps for translating pharmacogenomic information from research into practice?

27. Step 1. Identify SNPs in genes relevant to drug efficacy or toxicity Human Genome: 2,900,000,000 billion total base pairs 10,000,000 total single nucleotide polymorphisms (SNP) 300,000 variant haplotypes 10,000 haplotypes in pharmacologically-relevant genes

28. Step 2. Retrospectively, find SNPs associated with response SNP: single nucleotide polymorphism ATGCTTCCCTTTTAAA ATTGTTCCCTTTTAAA ATTGTTGCCTTTTAAA ATGGTTGCCTTTTAAA ATAGTTGCCTTTTAAT ATAGTTGCCTTTTAAT ATGATTGCCTTTTAAA ATGATTGGCTTTTAAA ATGTTTCGCTTTTAAA ATGTTTTGCTTTTAAA ATTTTTTGCTTTTAAA ATCTTTTGCTTTTAAA 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 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

29. 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 Treat G G G G G G G G G G 25% cure 50% cure Determine statistical significance (the probability that such a difference is due to random chance)

30. Clinical significance of DME polymorphism (1) Plasma concentrations in the different CYP2C19 genotype after omeprazole 20 mg dosing Clin Pharmacol Ther 1999;65:552-561. Omeprazole is mainly metabolized by CYP2C19. Distinct differences in plasma concentration are observed between CYP2C19 genotypes. DME: drug metabolizing enzyme; EM: extensive metabolizers; PM: poor metabolizers

31. Clinical significance of DME polymorphism (2) Median data on 24-hour intragastric pH profiles in the different CYP2C19 genotype after omeprazole 20 mg dosing PK difference between CYP2C19 genotype PD difference Clin Pharmacol Ther 1999;65:552-561. Genotype is required to rationalize the dosing PK: pharmacokinetics, PD: pharmacodynamics

32. Ideal flow considering PK-related polymorphism large small PK comparison between genotypes Genotype data collection as demographics Δsmall Δlarge Dosage regimen by genotype,etc. Population PK/PD: genotype as covariate To confirm utility of genotyping Genotyping is useful? Yes No No necessity to consider genotype • Dosage regimen by genotype • Pharmacogenomics-oriented TDM No No necessity to consider genotype Non-clinical Suggested genetically variability in PK Clin Pharm Studies Exploratory & Confirmatory Studies Product Launch

33. Polymorphic drug metabolizing enzymes

34. Drug metabolizing enzymes for β-blocker Drug metabolizing enzymes β-blocker metoprolol bisoprolol CYP2D6 CYP2D6/3A4/1A2 carvedilol

35. Effect of CYP2D6*10 allele on PK of S-metoprolol 500 400 CYP2D6*10/*10 300 Concentration in plasma (nM) 200 100 2D6*1/*1 0 0 2 4 6 8 10 12 14 Time (hr) Clin Pharmacol Ther 1999 ; 65 : 402-407

36. Chronic Heart Failure (CHF) βblocker responder non-responder CAUSES: Plasma Concentration of β blocker Polymorphisms Drug Metabolizing Enzyme Function of Target Molecules of β blocker Polymorphisms Adrenergic Receptor (AR) and Target Molecules

37. β1 AR Ser49Gly and Risk in CHF △ Ser49 homozygotes without β-blockers (n=63) ▲ Gly49 variant without β-blockers (n=28) ☆ Ser49 homozygotes with β-blockers (n=59) 60 Gly49 variant with β-blockers (n=33) ★ β-blocker is more effective in Patients with Gly allele △ p = 0.12 40 ☆ Risk of end-point (％) ▲ p = 0.016 20 ★ 0 0 2 5 3 4 1 Follow-up (years) Eur Heart J 2000;21:1853-8.

38. β2 Adrenergic Receptor polymorphism Ratio of Responders Gln/Gln 26% Gln/Glu Glu/Glu 62% Gln27Glu is a potential determinant for the response to carvedilol in heart failure Kaye DM et al. (2003) Pharmacogenetics 13: 379-382

39. Scientific Basis for Using Pharmacogenomics to Rationalize Dosing • Top 27 drugs more frequently cited in reports • 59% (16/27) metabolized by at least one enzyme having poor metabolizer (PM) genotype • 38% (11/27) metabolized by CYP 2D6 • mainly drugs acting on central nervous and cardiovascular systems Phillips et al. (2001) JAMA, 286 (18): 2270-2279

40. Summary of CYP2D6 activity Japanese Caucasoids activity genotype phenotype genotype phenotype PM PM Low Mainly CYP2D6*5 *3,*4,*5 etc ~1% 5-10% ??? (*2 with -1584CG SNP) *10/PM gene (about 3％) IM *10/ *10 (about 15％) hetEM: wt / PM gene EM hetEM: wt / PM gene EM wt / * 10 wt / wt （wild type） wt / wt （wild type) UM Ultra Rapid （ethnic difference） UM Ultra Rapid (low frequency） High Multiple active genes

41. Genetica della malattia cardiovascolare Dati in parte della British Heart Foundation

42. Heart disease statistics • Leading cause of premature death in UK • Deaths under 75, 39% of men, 30% of women • 270,000 heart attacks per year • 43% fatal within 28 days, 32% within 24 hours

43. Mortality from CVD and CHD in selected countries Rate per 100,000 population (Men aged 35 – 74 years) CVD deaths CHD deaths 1500 1000 500 0 Russia Poland Finland New England/ USA Italy Spain Japan Zealand Wales (Adapted from 1998 World Health Statistics)

44. Genetics of CVD • Positive Family History • 7-fold increase in mortality in first degree relatives of CAD patients compared with control subjects • Families share environment as well as genes • CAD is not a monogenic trait • rare exceptions involving mutation of genes e.g. LDL receptor, apolipoprotein B

45. Sibling recurrence rate (λs) for CVD CHD (MI<55yr) = 4 Hypertension = 2.5 TEXTBOOK IDDM = 15 Cystic fibrosis = 500 l s = 2 to 12 (premature CHD) RANGE IN LITERATURE l = 3 (fatal CHD <65yr) DZT l TWINS Male = 7 ; Female = 15 (fatal CHD <65yr) MZT ( Marenberg NEJM 1994) Female > Male HERITABILITY Early > Late disease

46. What is a heart attack? Blockage of the coronary arteries, preventing blood flow and hence oxygen delivery to heart muscle

47. Angiogram Atherosclerosis in vivo

48. Heart attack

49. What causes a heart attack ? 1. Atherosclerosis - slow build up of cholesterol, smooth muscle cells and macrophages in cells of arterial walls, may eventually cause ANGINA 2. Plaque rupture - plaque weakens and tears 3. Thrombosis - clot forms on the exposed surface of ruptured plaque, repairs damage or... 4. Clot blocks the already narrowed artery, blood cannot flow, no O2 delivery to tissue, ischemia - heart attack/stroke/thrombosis

50. Coronary heart disease l – Angina pectoris, myocardial infarction, sudden cardiac death Cerebrovascular disease l – Transient ischaemic attacks, stroke Peripheral arterial disease l – Intermittent claudication, gangrene Clinical manifestations of atherosclerosis