Pharmacogenomics: an important step in the quest for biomarkers of drug response

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1 PHARMACOGENOMICS Development drugs/biologicals against completely new drug targets Pharmacogenomics: an important step in the quest for biomarkers of drug response Studying genetic variants as effect modifier of currently marketed drugs (pharmacogenetics) Bruno H Ch Stricker Erasmus University Medical School & Inspectorate of Healthcare 1 2 Pharmacogenetics: Why? DRUG EFFECTS To utilize drugs more effectively and safely by using biomarkers (markers of biological response) To gain scientific insight into biological effects and pathways 30% NO beneficial effects 30% beneficial effects 10% only adverse effects 30% noncompliant WHY? WHY? AND The step from clinical trial to realife can not be solved by pooling of halthcare databases and other forms of big data Biomarkers might facilitate populationbased Pk/Pd modelling as well as tailored pharmacotherapy 3 4 The magic of confounding OC + OC Are genetic variants confounders or effect modifiers? MI OR: 39*154/24*114 = 2.2 OC + OC OC + OC MI MI Young women: OR: 21*59/17*26 = 2.8 Old women: OR: 18*95/7*88 = 2.8

2 The CLINICAL reality of effect modification GENES ARE (MOSTLY) EFFECT MODIFIERS OF DRUG RESPONSE NSAID + NSAID GI blood loss OR: 39*154/24*114 = 2.2 NSAID + NSAID NSAID + NSAID GI GI Young women: OR: 9*59/17*26 = 1.2 Old women: OR: 30*95/7*88 = NEED FOR DETAILED POPULATONBASED STUDIES: ROTTERDAM STUDY COHORT Pharmacogenetics: mostly 2 scientific approaches 15,000 study participants 5 crosssectional interviews plus extensive physical examinations and imaging Complete coverage of medication and 5 drug interviews [including adherence and OTC] DNA available GWAs, exome sequencing, metabolomics, proteomics Candidate gene studies, e.g. CYP2C9, CYP2D6 Genomewide analysis 9 10 Results: QT interval (msec) duration Difference in QT interval duration by NOS1AP genotype Genotypic model Allelic model rs Genotype Per Gallele Subjects RR adjusted RR, age, sex adjusted TT 2100 TG (2.34.1) 3.3 (2.44.2) GG (5.78.3) 7.1 (5.88.4) (2.84.0) 3.5 (2.94.1) 11 12

3 Results: NOS1AP and SCD risk rs Genotype (cases) All SCD TT (90) TG (95) GG (36) Crude 1.0 (0.71.3) 1.3 (0.91.9) Full model 1.0 (0.71.3) 1.3 (0.91.9) Witnessed SCD TT (47) TG (43) GG (26) Crude Full model 0.8 (0.51.2) 0.8 (0.61.3) 1.7 (1.02.7) 1.7 (1.01.8) QTc (msec) HR (95% CI) Full model: adjusted for age, sex, BMI, smoking, hypertension, diabetes, heart failure and myocardial infarction shift of QTc in persons with risk genotype 14 Results: Effect of digoxin and NOS1AP on QTc QTc difference in msec msec No digoxin Digoxin TT TG GG Genes are probably very important effect modifiers Absorption & distribution ATP Binding Cassette (ABC)transport proteins, e.g. P glycoprotein Solute Carrier (SLC)transporters Organic anion transporters (OCT) Metabolism Cytochrome P450 isoenzymes, e.g. 3A4, 2C9 Receptors 17 18

4 Neuropsychiatric Adverse Reactions to mefloquine and ABCB1gene Genome wide association study acenocoumarol Haplotype Noncases Cases OR OR* 95 % CI* VKORC1 p= CGCCGC CGCTTT TTTTTT ( ) ( ) CYP2C9 p= TTTTTT versus CGC carriers ( ) CYP2C9 & VKORC1 variants and overanticoagulation First INR measurement Example 1 CYP2C9, VKORC1 and coumarin dose 7,00 6,00 5,00 First INR 4,00 3,00 2,00 1,00 0,00 *1/*1 *1/*2 *1/*3 *2/*2 *2/*3 *3/*3 *1/*1 *1/*2 *1/*3 *2/*2 *2/*3 *3/*3 *1/*1 *1/*2 *1/*3 *2/*2 *2/*3 *3/*3 CC CT TT VKORC1 and CYP2C9 genotypes 21 Teichert M, et al. CPT 2009;85: CYP2D6 phenotype CYP2D6, ßblockers and heart rate CYP2D6 Ultrarapid Metabolizers (UM) 23% of Caucasian population Poor Metabolizers (PM) 510% of Caucasian population metoprolol Polymorphisms Intermediate Metabolizers (IM) Extensive Metabolizers (EM) atenolol Bijl MJ, et al. CPT 2009;85:4550.

5 CYP2C9 and tolbutamide dose Metformine & Transporters and LIVER BLOOD OCT2 KIDNEY PMAT INTESTINE 25 Becker ML, et al. CPT 2008;83: Metformine, & Metformine, & Sufficient transport inside (); poor transport outside () liver cell Poor transport inside (); sufficient transport outside () liver cell Liver cell Liver cell Metformine, interaction & Metformine, interactie & Delta HbA1c (%) 1 0,5 0 0,5 1 AA AC CC 68% good 8% very good Delta HbA1c (%) 1 0,5 0 0,5 1 AA AC CC 9% poor 15% moderate 1,5 1,5 GG GA AA 29 GG GA AA 30

6 DESPITE A 99.9% SIMILARITY IN GENES, WE ARE REMARKABLY DIFFERENT From gene to protein DNA Gene RNA Protein Chromosomes From protein to event? Conclusions Genetic determinants are important effect modifiers (metabolism, receptors) However, pharmacogenetics is only one group of potential biomarkers Protein?????? Event We need more knowledge about biomarkers for safe and effective drug response from detailed populationbased studies The limitations of clinical trials can never be resolved by big data only 33 34

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