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Pharmacogenetics: From DNA to Dosage – Just A Click Away

Pharmacogenetics: From DNA to Dosage – Just A Click Away. Cindy L. Vnencak-Jones, PhD, FACMG Vanderbilt University Medical Center April, 2011. DISCLOSURE INFORMATION. Cindy L Vnencak-Jones, PhD FACMG. No relationships to disclose. Pharmacogenetics: From DNA To Dosage – Just A Click Away.

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Pharmacogenetics: From DNA to Dosage – Just A Click Away

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  1. Pharmacogenetics: From DNA to Dosage –Just A Click Away Cindy L. Vnencak-Jones, PhD, FACMGVanderbilt University Medical Center April, 2011

  2. DISCLOSURE INFORMATION Cindy L Vnencak-Jones, PhD FACMG No relationships to disclose

  3. Pharmacogenetics: From DNA To Dosage – Just A Click Away Pharmacogenetics: • influence of genetic variation on an individual’s response to pharmacologic agents • Pharmacogenetics testing is not routinely used in clinical practice • when ordered, is done “as needed” preventing usefulness for initial dosing • many drugs, many genes, many studies result in information overload for the provider

  4. Pharmacogenetics: From DNA To Dosage – Just A Click Away PREDICT: PharmacogenomicResource for Enhanced Decisions In Care and Treatment

  5. PREDICT Initiative Rationale • Provide real-time decision support thereby facilitating individualized drug therapy to maximize efficacy, minimize adverse drug reactions, and reduce health care costs

  6. PREDICT Initiative Assemble multidisciplinary, multidepartment team Pathology, Informatics, Pharmacy, Clinicians, Ethics, Legal, Regulatory Proof of Concept Which drug/gene relationship should test the model? Genotyping Which methodology? Research or CLIA lab? Informatics Data management, Electronic health record, decision support Implementation – 9/15/2010 Assessment of the initiative – ongoing Measure utility of decision support and clinical impact of genotyping

  7. PREDICT Initiative Vanderbilt University Office of Personalized Medicine Vanderbilt Informatics Center Ethics/Legal/ Regulatory Pharmacy and Therapeutic Committee PREDICT VUMC Computational Genetics Core Molecular Diagnostics Lab Clinicians Optimize Patient Management

  8. PREDICT Process – Phase I • Consent process • Adult Admitting & ED Registration • “CONSENT FOR ROUTINE TESTS, MEDICAL TREATMENT, AND GENETIC TESTS TO GUIDE DRUG THERAPY…” • Provider discusses genotyping studies • Blood drawn • Sample arrives in laboratory • DNA extracted (day 1) • Assay performed (day 2) • Results reviewed and released (day 3)

  9. PREDICT Process – Phase I • Raw data converted to drug genome interaction fact for computerized decision support in electronic health record (EHR) • Provider accesses EHR; alerted to results • Provider receives decision support regarding dosing or alternative medications • Provider optimizes patient management utilizing information provided by genotyping studies

  10. PREDICT Model Clopidogrel (PLAVIX) –CYP2C19 • FDA issued a “black box” warning regarding the clinical relevance of genotype analysis • Widely prescribed to patients at our medical facility • Could provide decision support and measure the change in prescribing behavior of the provider based on the given decision support • Targeted patient population to launch model – the cardiac catheterization lab

  11. FDA – Black Box WarningIssued March 12, 2010 • WARNING: DIMINISHED EFFECTIVENESS IN POOR METABOLIZERS • Effectiveness of Plavix depends on activation to an active metabolite by the cytochrome P450 (CYP) system, principally CYP2C19. • Poor metabolizers treated with Plavix at recommended doses exhibit higher cardiovascular event rates following acute coronary syndrome (ACS) or percutaneous coronary intervention (PCI) than patients with normal CYP2C19 function. • Tests are available to identify a patient's CYP2C19 genotype and can be used as an aid in determining therapeutic strategy. • Consider alternative treatment or treatment strategies in patients identified as CYP2C19 poor metabolizers.

  12. Clopidogrel - PLAVIX • Requires gastro-intestinal absorption and hepatic biotransformation • Is an inhibitor to the P2RY12 receptor thereby preventing binding of ADP • Increases risk of bleeding; especially GI bleeding when combined with warfarin and nonsteroidal anti-inflammatory drugs Simon T. et al, N Engl J Med 2009

  13. Clopidogrel - PLAVIX CH3 Prodrug Active CH3 • Antiplatelet therapy, often prescribed in combination with aspirin • Initial dose 300 mg followed by 75 mg daily • Indications for use: acute coronary syndrome; recent myocardial infarction or stroke; peripheral arterial disease; or patients managed following angioplasty, bypass surgery or stent placement

  14. Drug Metabolizing Enzymes Phase II Conjugation with endogenous substituents to form: Glucuronide Acetate Glutathione Sulfate Methionine Phase I Modification of functional groups: Hydrolysis Oxidation Dealkylation Dehydrogenation Reduction Deamination Desulfuration Evans and Relling, Science 1999

  15. VeraCode ADME Core Panel Illumina • Absorption • Distribution • Metabolism • Excretion

  16. CYP2C19 Multiple polymorphic sites with clinical significance W120Rc.358T>C CYtochromeP450Family2SubfamilyC polypeptide19 *8 X491Cc.1473A>C W212Xc.636G>A *12 g.-806C>T *3 *17 3’ 5’ Location: 10q24.1 – q24.3Gene: 90,209 basesmRNA: 1,473 Protein: 490 amino acid *6 *2 c.681G>AP681P *5 *4 c.395G>AR132Q c.1297C>TR433W c.1A>GATG>GTG missense truncation *7 splicing g.19294T>A promoter initiation codon insertion

  17. CYP2C19 – Clopidogrel Patients with reduced function alleleshave: • significantly lower levels of the active metabolite • diminished platelet inhibition and higher rate of platelet aggregation • higher rate of major adverse cardiovascular events and higher risk of stent thrombosis

  18. ADME Assay Design Gene 1:SNP-1 CCCTACACAGATGTGGTGCACGAGGTCCAGAGATACATTGACCTTCTCCCCACCAGCCTGCCCCATGC GGGATGTGTCTACACCACGTGCTCCAGGTCTCTATGTAACTGGAAGAGGGGTGGTCGGACGGGGTACG A T Gene 1:SNP-2 Gene 1:SNP-3 SNP-3 SNP-2 SNP-1 Patient 1 Patient 30 SNPs Optimized in 3 pools + control - control Adapted from Illumina

  19. Universal PCR Forward Sequences (1, 2) Universal PCR Reverse Sequence 3 3’ 5’ IllumiCode™ Sequence tag identifies bead Assay – Primer Design 5’ 3’ A (1-20 nt gap) G SNP Locus Specific Oligo Locus Specific Oligos A/G GENOMIC DNA TEMPLATE SNP Adapted from Illumina

  20. IllumiCode Sequence Tag A Universal PCR Sequence 3 Universal PCR Sequence 1 Ligase Polymerase G Universal PCR Sequence 2 Assay – Allele Specific Extension and Ligation T GENOMIC DNA SNP specific primer binds and is extended Adapted from Illumina

  21. Cy3 Cy5 Polymerase Universal Primer 2 Universal Primer 1 Assay – PCR Amplification Universal PCR Sequence 3 IllumiCodeSequence Tag Biotin Universal PCR Sequence 1 A Primer specific for G with red dye does not bind Adapted from Illumina

  22. Cylindrical glass microbeads 240 μm length x 28 μm diameter Bar-coded for identification VeraCode Technology – the glass microbead Adapted from Illumina

  23. A G G/G A/A T C C/T Assay - Hybridization of PCR Products to VeraCode Beads IllumiCode 1 IllumiCode 2 SNP 1 SNP 2 Homozygous Homozygous Red and green signal detection with the BeadXpress Reader IllumiCode 3 SNP 3 Heterozygous Adapted from Illumina

  24. BeadXpress Reader Adapted from Illumina

  25. VeraCode Bead Loading & Detection CAPILLARY FORCE ATTRACTS BEADS INTO GROOVES BEADS FALL INTO GROOVE PLATE BEADS ALIGN TIGHTLY FOR OPTIMAL SCANNING EFFICIENCY Adapted from Illumina

  26. VeraCode Bead Plate Scanning

  27. Reports with Automatic Translation

  28. Visualization of the Results

  29. PREDICT Database Samples with call rates >97.34% “Pass”

  30. Electronic Health Record

  31. Electronic Health Record Currently, CYP2C19 results sent to EHR, all other data is stored but can be sent to EHR in the future when drug genome interactions decisions become “actionable”

  32. Electronic Prescription Order

  33. Electronic Prescription Order

  34. Clopidogrel Response CYP2C19 Genotype • Genetic Factors • Polymorphisms in CYP2C19 and other CYPs, as well as SNPs in P2RY12,GpIIb/IIIa • Cellular Factors • P2RY12 and non P2Y pathways • Clinical Factors • Drug-drug interactions, elevated body mass, smoking, diabetes, poor compliance

  35. ADME QA/QC • Allele frequencies of all genotypes • Discordant results: controls and repeat patients (which SNPs and frequency) • Assay performance: # of samples per plate with average call rates <97.30% (7/185 SNPs no call) • Locus performance (<95% call rates)

  36. PREDICT Results 9/15/10 - 4/4/11 *1*1*1*17 *1,*2 *1,*3*1,*4 *1*5 *1*7*1*8 *1*12 *2*17 *17*17 *5*5 *4*4 *3*3 *2*2 1419 patients

  37. Assay Accuracy

  38. Assay Reproducibility 150 patients repeated

  39. ADME QA/QC Paragon controls # of plates

  40. Locus Performance (<95% call rates) 80 plates

  41. Summary • Implemented a mid-throughput assay to screen 34 genes (185 SNPs) involved in drug absorption, distribution, metabolism and excretion • Detected polymorphisms similar in frequencies to that previously reported • Established QA/QC parameters for assay • Developed a process to enable decision support to providers for drug dosing based on DNA findings which will facilitate genetically informed medicine

  42. Summary • Implemented a scalable process to allow expansion to other actionable SNPs with associated decision support rules • Process enables retrospective auto-population of stored data in patients EHR for future without the need for repeat testing • Measure clinical utility and impact of genotyping data and decision support services • Phase II - system permits identification of “at risk” patient populations for preemptive genotyping

  43. Acknowledgements Vanderbilt University Nicholas Zeppos - Chancellor Jeff Balser, MD, PhD – Vice Chancellor VUMC Gordon Bernard, MD – Vice Chancellor Research Office of Personalized Medicine Dan Roden, MD PREDICT Implementation Team Jill Pulley, MBA Russ Wilke, MD Jim Jirjis, MD Josh Peterson, MD John McPherson, MD Andrea, Ramirez, MD Mike Laposata, MD, PhD Center for Biomedical Ethics and Society Ellen Clayton, MD, JD Kyle Brothers, MD Molecular Diagnostics Lab Gladys Garrison, MS Jennifer Carter, PhD Lisa Rocha Sonia Byon Vickie Fraser VUMC Computational Genetics Core HolliDilks, PhD Doug Selph Brad Winfrey Vanderbilt Informatics Center Dan Masys, MD Joshua Denny, MD Ed Shultz, MD Marc Beller

  44. Pharmacogenetics: From DNA to Dosage –Just A Click Away Cindy L. Vnencak-Jones, PhD, FACMGVanderbilt University Medical Center April, 2011

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