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Pharmacogenomics and Medication Development for Methamphetamine Dependence

Pharmacogenomics and Medication Development for Methamphetamine Dependence. Keith Heinzerling, MD, MPH UCLA Medication Development Unit for Stimulant Abuse, UCLA Department of Family Medicine, and UCLA Integrated Substance Abuse Programs May 17, 2007. Agenda.

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Pharmacogenomics and Medication Development for Methamphetamine Dependence

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  1. Pharmacogenomics and Medication Development for Methamphetamine Dependence Keith Heinzerling, MD, MPH UCLA Medication Development Unit for Stimulant Abuse, UCLA Department of Family Medicine, and UCLA Integrated Substance Abuse Programs May 17, 2007

  2. Agenda • Introduction to pharmacogenomics • Examples of pharmacogenomics and medication development for addiction (nicotine dependence) • Exploratory pharmacogenomic study in trial of modafinil for methamphetamine dependence

  3. Problem: Pharmacotherapies Are Variably Effective Response to Anti-Hypertensives in African Americans Taken from: Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med. 2001 May;7(5):201-4.

  4. Problem: Pharmacotherapies Are Variably Effective Response to Anti-Hypertensives in African Americans Taken from: Kaplan NM, Rose BD. Choice of Therapy in Essential Hypertension, Up To Date, 2007.

  5. Problem: Pharmacotherapies Are Variably Effective Response to Commonly Used Anti-Depressants Taken from: Lenox-Smith A, Conway P, Knight C. Cost effectiveness of representatives of three classes of antidepressants used in major depression in the UK. Pharmacoeconomics. 2004;22(5):311-9.

  6. Problem: Pharmacotherapies Are Variably Effective Response to Smoking Cessation Medications Taken from: Gonzales D, Rennard SI, Nides M, Oncken C, Azoulay S, Billing CB, Watsky EJ, Gong J, Williams KE, Reeves KR; Varenicline Phase 3 Study Group.Varenicline, an alpha4beta2 nicotinic acetylcholine receptor partial agonist, vs sustained-release bupropion and placebo for smoking cessation: a randomized controlled trial. JAMA. 2006 Jul 5;296(1):47-55.

  7. Pharmacogenomics (PGx): influence of genetic variation on response to drugs • Pharmacogenetics: Variation in genes (DNA sequence) • Classically genes for drug metabolizing enzymes, i.e. cytochrome P450 • Pharmacogenomics: Variation in any product of the genome • DNA sequence, RNA expression, protein structure or function (proteomics) • In practice the terms are used loosely

  8. Genetic variation may influence pharmacokinetics or pharmacodynamics • Pharmacokinetic genes • Drug absorption, distribution, metabolism, excretion • Influence dose requirements and/or AEs • Pharmacodynamic genes • Drug targets (receptors, transporters, intracellular signaling pathways, enzymes and metabolic pathways) • Influence efficacy of drug

  9. Note: PGx is distinct from the genetics of disease susceptibility • Genetics of disease susceptibility • Genotype (DNA) = Phenotype (Disease) • Genes related to disease pathogenesis • Pharmacogenomics • Genotype (DNA) = Phenotype (Drug Response) • Identify genotypes associated with the phenotype of response to medication (therapeutic response and/or side effects) • Genes related to drug’s mechanism of action

  10. Potential applications of PGx in medication development • Clinical Trials • Rescue of “dead drugs” and negative trials • Enrollment limited to genotypes with enhanced efficacy or lower risk of AEs (lower cost for trials) • “Personalized Medicine” • Development of clinical genetic tests to guide medication prescribing • FDA approval of drug/test combination?

  11. Potential applications of PGx in medication development • Association between variation in gene and response to drug implicates that gene in the drug’s mechanism of action • Identify molecular/cellular targets of drugs: • Receptors • Transporters • Intracellular signaling pathways • DNA binding proteins • Enzymes and metabolic pathways

  12. Completion of Human Genome Project (2003): Acceleration of PGx research • Sequencing of human genome revealed high degree of genetic variation • Approximately 10 million polymorphisms in human genome (Goldstein DB, 2005) • 1 single nucleotide polymorphism (SNP) per 1,000 nucleotides (Luca Cavalli-Sforza L, 2005) • Automated genotyping technology developed for HGP has made genotyping accessible to clinical researchers

  13. Single Nucleotide Polymorphisms (SNPs): Most Common Type of Variation At least 1 percent of the population Most of the population G to C Common sequence Variant sequence SNP site Slide courtesy of the National Cancer Institute

  14. PGx study design: Association between SNP frequency and drug response? Taken from: Roses AD. Pharmacogenetics and the practice of medicine. Nature. 2000 Jun 15;405(6788):857-65

  15. The grammar of PGx analyses • Allele: Letters (G vs. C) • Genotype: Words (GG vs. GC vs. CC) • Haplotype: Sentence (GG_AA vs. GC_AT) AATGCG vs. AATCCG AATGCG vs. AATGCG AATGCG vs. AATCCG AATGCG / TTCATC vs. AATGCG / TTCATC AATGCG / TTCATC vs. AATCCG / TTCTTC

  16. How can SNPs influence medication response? • Non-coding (most SNPs) vs. coding region • Synonymous (silent) vs. non-synonymous • “Functional” SNPs • Non-synonymous SNPs alter protein structure • Non-coding SNPs may alter gene expression (transcription factor binding, splicing) • Marker for nearby functional variant • LD (linkage disequilibrium)

  17. Approaches to PGx studies • Candidate gene study • Choose genes of interest based on prior data • Genotype SNPs in genes of interest • Used for hypothesis testing • Whole-genome scan study • SNP microarrays with thousands of SNPs • Used for hypothesis generation • Haplotype structure may decrease number of SNPs needed to sample

  18. Example: Candidate gene study

  19. Association of COMT SNPs with response to bupropion for smoking (n=511)? • Functional SNP in COMT (rs165688) • G to A in codon 158/108 converting Valine (high activity) to Methionine (low activity) • Val (high) allele >> lower basal dopamine >> enhanced bupropion effect? • Also rs737865 and rs165599 • Associated with schizophrenia (high prevalence of tobacco use) • Functional significance not clear

  20. COMT SNPs examined(3’ UTR: Role in gene expression?)

  21. Results: rs165599 genotype associated with response to bupropion (Val/Met not) • GG associated with higher abstinence overall • But A allele assoc. with response to bupropion • Freq of A allele: 68% in EA and 33% in AAs • Practical Implications: Bupropion if A allele and NRT if GG?

  22. Results: rs737865 and rs165599 haplotypes and bupropion response • G_A haplotype with largest bupropion response • Neurobiological significance not clear (possibly SNPs in LD with functional variant?)

  23. Example: Whole genome scan in NRT +/- mecamylamine patients

  24. Results: 4,570 of 520,000 SNPs differ in quitters (n=55) versus non-quitters (n=79) • Identified SNPs were in the following genes: • Cell adhesion molecules (17) • Enzymes (39) • Receptors, signaling (37) • Channels (5) • Transcription factors (27) • Disease related (9) • Structural proteins (12) • Vesicular proteins (4) • Transporters (5) • DNA/RNA/protein processing (32) • Unknown (34) • Fascinating, but practical implications unclear…

  25. Other examples of PGx in addiction • DRD2 -141C Ins/Del and response to bupropion for smoking (Lerman C, 2006) • OPRM1 A118G SNP and naltrexone for alcohol dependence (Oslin D, 2003; Gelernter J, 2007) • DRD4 VNTR and olanzapine (D2/D4 antagonist) for alcohol dependence (Hutchison K, 2006)

  26. Methamphetamine (MA) Dependence: No Effective Medications Available • Objective: To use genomic technologies to accelerate the development of medications to treat MA dependence • Identify the mechanism of action and molecular/cellular targets of medications • Focus: Medications that may counteract the molecular/cellular changes that result from chronic MA use, e.g. modafinil?

  27. Modafinil for MA dependence: Rationale • Improved wakefulness, mood, cognitive function • May counteract MA withdrawal • Binds to DAT in vitro • May counteract MA-induced dopaminergic dysfunction • Increases basal glutamate levels (possibly via reduced GABA inhibition) • May counteract glutamte/cysteine exchange dysfunction

  28. Modafinil: Counter MA-induced ROS and damage to dopamine neurons?

  29. Modafinil: Counter MA-induced reductions in basal glutamate tone?

  30. Does variation in dopamine- or glutamate-related genes influence response to treatment for MA with modafinil?

  31. Study Methods • Isolate genomic DNA from participants in clinical trial of modafinil for MA dependence (n=70) • Use SNPs as “tags” for dopamine- and glutamate-related genes of interest • Use Applied Bioscience SNP Browser 3.5 to select SNPs equally spaced throughout genes of interest • Genotype SNPs using Taqman allelic discrimination assay

  32. SNP Browser: SNP Wizard

  33. Study Analysis • Compare frequency (allele, genotype, and/or haplotype) of SNPs among responders and non-responders to modafinil • Also examine secondary outcomes (baseline cognitive function and change in function with treatment) • Association between SNP and response suggests that SNP (or gene) is important in the mechanism of modafinil for MA dependence

  34. Study Limitations • Unclear efficacy of modafinil for MA dependence • But genetics may rescue negative trials? • May identify genes associated with endophenotypes (cognitive dysfunction) • Small number of participants and limited power to detect treatment x genotype interactions • DNA specimens will be banked for pooling with future studies

  35. Study Limitations: Relatively large samples needed for PGx studies Taken from: Berrettini WH, Lerman CE. Pharmacotherapy and pharmacogenetics of nicotine dependence. Am J Psychiatry. 2005 Aug;162(8):1441-51.

  36. Summary • Integration of PGx studies into medication trials is accessible to clinical researchers and offers exciting possibilities • Sample size requirements are an issue • Safest bet is PGx study of effective medication with variable efficacy/safety • PGx earlier in medication development is high risk, but also potentially high gain • Future: Genome scans, RNA expression arrays?

  37. Acknowledgements • Steve Shoptaw, PhD • Walter Ling, MD • Tom Kosten, MD • James McCracken, MD • Stanley Nelson, MD

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