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Prospettive della farmacogenetica e della farmacogenomica Stefano Vella Dipartimento del Farmaco Istituto Superiore di Sanità Pharmacogenetics & Pharmacogenomics • An opportunity to improve drug development • Choice of drug targets • An opportunity to improve clinical care • Individualized medicines through stratification • More rational decisions on therapeutic options Pharmacogenetics & Pharmacogenomics • An opportunity to improve drug development • Choice of drug targets • An opportunity to improve clinical care • Individualized medicines through stratification • More rational decisions on therapeutic options Druggable Genome Predictions Druggability Prediction Method GPCRs No. Molecular Targets Targets of approved NCEs 170 Sequence homology to NCE drug targets 945 21%GPCRs 21% 32% Kinases GPCRs Kinases Proteases Proteases Transporters 32% Transporters Ion Channels Ion Channels Transferases Transferases Targets of chemical leads with activities (binding affinities) below 10uM 707 Targets of Ro5 chemical leads with activities (binding affinities <= 10uM) 587 Sequence homology to targets with chemical leads* 2921 Feature-based druggability sequence probability prediction 2325 Structured-based prediction 427 Sequence homology to proteins predicted druggable by structure-based method 3541 Predicted Drugglable Genome (small molecules) 3505 Human Genome 24000 Other Enzymes Other Enzymes Kinases 15% 15% Phosphatases Phosphatases Cytochrome Cytochrome P450P450 Nuclear Hormone Recep Nuclear Hormone Receptors Phospholipases Phospholipases Phosphodiesterases 1% 1% Proteases 2% 7% 3% 2% 6% 5% Ion Channels 3% 5% Transporters 7% 6% Phosphodiesterases Other Receptors CellReceptors Adhesion Other Chemokines Cell Adhesion Other/Unclassified Chemokines Other/Unclassified Gene family distributions of predicted druggable genome Future Drug Target Space Human Genome 24000 6465 ~2400 ~145 160 170 578 ~320 1769 *Zambrowicz & Sands, Nature Drug Disc. Rev. (2003), 2,38-51C **Genetic association linkage data estimated by text-mining from entity co-occurrence within Medline abstracts. Data produced by Anna Gaulton and Andrew Hopkins, using a modified version of Lucene, by Lee Harland, to text-mine Medline, 3505 Accessible Genome: protein therapeutics Druggability Prediction Method No. of Molecular targets Targets of approved antibodies 15 Targets of approved biologicals 59 Secreted protein (high confidence) 1384 Secreted proteins (low confidence) 6560 Transmembrane predictions (high confidence) 973 Transmembrane predictions (low confidence) 1407 Unique, combined transmembrane and secreted predictions (high confidence) 2287 Feature-based biological target sequence probability prediction 1637 Total unique genes predicted to be accessible via protein therapeutics 3258 1516 genes likely to encode proteins drugable by both small molecules and protein therapeutics Total number druggable by protein therapeutics Total number druggable by small molecule = 3258 genes therapeutics = 3505 genes Human Genome 24000 Stages of HIV-1 Life Cycle Targeted by Anti-HIV Drugs In: Gulick RM, Topics HIV Med, 2002;10(4). The International AIDS Society–USA Chemokine Co-receptors in HIV Entry • HIV gains entry into cells that express CD4 and 1 of 2 secondary receptors, either: – C-C chemokine receptor 5 (CCR5) • Expressed on monocytes and T cells – C-X-C chemokine receptor 4 (CXCR4) • Expressed on T cells, B cells, monocytes, and neutrophils 1Deng H, et al. Nature. 1996;381(6584):661-666. Y, et al. Science. 1996;272:872-877. 2Feng HIV Attachment and Fusion Targets for Inhibition CD4 Binding CD4 binding inhibitors Co-receptor Binding gp41 Virus-Cell Fusion CCR5 antagonists Fusion inhibitors gp120 V3 loop CD4 Cell Membrane CCR5/CXCR4 (R5/X4) Adapted from Moore JP, et al. Proc Natl Acad Sci U S A. 2003;100:10598-10602. CCR5 D32 CCR5 wild type CCR5 D32 Normal Heterozygotes Homozygotes wt/wt wt/D32 D32/D32 2 normal copies 1 copy of D32 2 copies of D32 Delayed disease progression “Resistant” to HIV infection Standard disease progression CCR5-tropic HIV (R5 virus) • Nearly all new sexually transmitted HIV-1 infections are with R5 virus • Infect dendritic cells, macrophages, and T cells • Predominate throughout infection – X4 virus may appear over time, but ~50% of patients with HIV-1 subtype B who die from AIDS have only R5 virus1,3 – A tropism shift from R5 to X4 virus is associated with the presence of basic amino acids at codons 11 and/or 25 of the V3 loop of gp1204 Maraviroc (UK-427,857) Activity Results: Mean Reduction in Viral Load over Time Last day of dosing Change from baseline (log10 HIV-1 copies/mL) 0.5 0.0 n Maraviroc dose Placebo 015 4 Placebo 007 12 25 mg QD 8 50 mg BID 8 100 mg QD 8 100 mg BID 7 150 mg BID Fast 8 150 mg BID Fed 8 300 mg QD 8 300 mg BID 8 -0.5 -1.0 -1.5 -2.0 Baseline 5 10 15 20 Time (day) Study 1007/1015 25 30 35 40 Pharmacogenetics & Pharmacogenomics • An opportunity to improve drug development • Choice of drug targets • Optimization of clinical trials • An opportunity to improve clinical care • Individualized medicines through stratification • More rational decisions on therapeutic options Are Drugs Effective? Disease Efficacy Alzheimer’s Analgesics Cardiac arrhythmia Depression Diabetes Hepatitis C Incontinence Migraine Oncology 30% 80% 60% 60% 55% 45% 40% 50% 25% Annual Rx Cost $ 1,500 $ 1,350 $ 650 $ 700 $ 1,300 $ 5,000 $ 1,000 $ 600 $ 3,500 Prescribed drugs are generally effective in about 50% of patients. Are Drugs Safe? Adverse drug reactions (ADRs) represent the 4th leading cause of hospitalization (2 million/yr) and are responsible for 100,000 deaths/yr in the U.S. Pharmacogenetics of Phase I Drug Metabolism Weinshilboum R. N Engl J Med 2003;348:529-537 Pharmacogenetics of Nortriptyline Weinshilboum R. N Engl J Med 2003;348:529-537 Pharmacogenetics of Acetylation Weinshilboum R. N Engl J Med 2003;348:529-537 Genetic Polymorphisms in Drug Target Genes That Can Influence Drug Response Evans W and McLeod H. N Engl J Med 2003;348:538-549 Tamoxifen and Breast Cancer • 1971: some breast tumors express the estrogen receptor (ER), which drives tumor growth • Tamoxifen (ER receptor antagonist) was first administered regardless of tumor ER status • Ligand binding assay for ER status introduced – complex assay requiring fresh tissue • Immunohistochemical assays – variable results 1995 ErbB2 expression is associated with metastatic breast cancer! Reactivity on tumour samples About 25-30% of women who have metastatic breast cancer overexpress HerB2(EGF) receptor Relapse-free Survival (Panel A) and Overall Survival (Panel B) among Women with Breast Cancer, According to HER2 Amplification Status on FISH Pritchard K et al. N Engl J Med 2006;354:2103-2111 Herceptin (TrastuzuMAb) (anti-HER MAbs) 1999:Approved HER-2/neu Genetic Test Current genetic testing uses fluorescence markers (FISH technology) – look for increased copies of HER-2/neu gene with fluorescent DNA probes – labor-intensive and expensive Gene amplified Normal HER-2/neu positive patients Most responsive to therapy HER2 testing is covered by and required for most drug benefit plans Real-time quantitative PCR for detection of HER-2/neu gene amplification 10-fold amplified Her-2/neu non-amplified Her-2/neu Polygenic Determinants of Drug Response Evans W and McLeod H. N Engl J Med 2003;348:538-549 Potential of pharmacogenetics: the right dose of the right drug, the first time All patients with same diagnosis Non-responders and toxic responders Treat with alternative drug or dose Responders and patients not predisposed to toxicity Treat with conventional drug or dose Possible Designs for Pharmacogenomic Clinical Trials Retrospective Design Randomized, Double Blind, Placebo-controlled Trial Treatment period Placebo Genetic analysis Drug A (+) (-) (+) (-) entry sampling Patient numbers in a arm maybe unbalanced? Sampling maybe limited in some patients? Results are not confirmative? → Confirmative trial would be necessary Prospective Design 1 Drug A randomized Therapy as usual Treatment period sampling Genetic analysis (+) (-) To test clinical utility PGx test is really necessary? Cost-benefit relationship Results are confirmative Prospective Design 2 (+) Genetic analysis randomized Randomized, Double Blind, Placebo-control Trial Treatment period Placebo Drug A (-) No entry sampling Enrichmen t approach Increase analytical power of trial Results are confirmative But, data in gene(-) patients can not be obtained May lose a chance of treatment for (-) patients Prospective Design 3 Randomized, Double Blind, Placebo-control Trial Treatment period Placebo (+) randomized Drug A Genetic analysis Placebo (-) Drug A sampling Benefit of Pharmacogenomics • Improving benefit/risk ratio – More safe, more effective drugs • Adjusting Dose – Determine the best dose • Increasing successful rate of clinical trials – Focusing on data in responder More drugs, more appropriate •Except for monozygotic twins, each person's genome is unique. •All physicians will soon need to understand the concept of genetic variability, its interactions with the environment, and its implications for patient care. •With the sequencing of the human genome, the practice of medicine has now entered an era in which the individual patient's genome will help determine the optimal approach to care, whether it is preventive, diagnostic, or therapeutic. A possible future…. 1. Doctor Examines Patient and Makes Initial Diagnosis 2. Laboratory Buccal Swab or Blood Sample Maximum Density Score at Allele Blood Sample 3. Genetic Analysis Haplotype pairs -654 0.30 G/A -367 -1.90 T -47 -2.70 T +46 -0.10 A/G +491 3.71 C +523 0.23 C/A B 3.20 G 3.20 C 2.50 C -2.90 G 3.78 C 3.36 C 2/2 C 3.20 G -0.20 C/T 0.10 C/T -2.90 G 3.75 C 0.19 C/A 2/6 D 0.20 G/A -2.00 T -2.80 T -0.10 A/G 3.61 C 0.15 C/A 4/6 E -3.00 A -2.00 T -3.00 T 1.90 A 3.74 C 3.46 C 4/4 F 3.30 G -0.20 C/T 0.20 C/T -2.80 G 3.71 C 0.19 C/A 2/6 G 0.20 G/A -0.10 C/T 0.10 C/T 0.10 A/G 3.68 C 3.40 C 2/4 H 3.40 G 3.80 C 2.50 C -2.30 G 3.66 C 3.66 C 2/2 A 4/6 Drug Prescribing Based on the Patient’s Genetic Markers 20 Maximum Density Score at Allele Blood Sample -654 0.30 G/A -367 -1.90 T -47 -2.70 T +46 -0.10 A/G +491 3.71 C +523 0.23 C/A Varient B 3.20 G 3.20 C 2.50 C -2.90 G 3.78 C 3.36 C 2/2 C 3.20 G -0.20 C/T 0.10 C/T -2.90 G 3.75 C 0.19 C/A 2/6 D 0.20 G/A -2.00 T -2.80 T -0.10 A/G 3.61 C 0.15 C/A 4/6 E -3.00 A -2.00 T -3.00 T 1.90 A 3.74 C 3.46 C 4/4 8 F 3.30 G -0.20 C/T 0.20 C/T -2.80 G 3.71 C 0.19 C/A 2/6 6 G 0.20 G/A -0.10 C/T 0.10 C/T 0.10 A/G 3.68 C 3.40 C 2/4 4 H 3.40 G 3.80 C 2.50 C -2.30 G 3.66 C 3.66 C 2/2 2 A 4/6 18 16 14 12 10 0 -5.0 10.0 NonResponders 25.0 40.0 55.0 Responders