genomics of adverse drug reactions the need for a multi functional approach n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Genomics of Adverse Drug Reactions: The Need for a Multi-Functional Approach PowerPoint Presentation
Download Presentation
Genomics of Adverse Drug Reactions: The Need for a Multi-Functional Approach

Loading in 2 Seconds...

play fullscreen
1 / 33

Genomics of Adverse Drug Reactions: The Need for a Multi-Functional Approach - PowerPoint PPT Presentation


  • 204 Views
  • Uploaded on

Genomics of Adverse Drug Reactions: The Need for a Multi-Functional Approach. Munir Pirmohamed David Weatherall Chair of Medicine and NHS Chair of Pharmacogenetics Department of Molecular and Clinical Pharmacology University of Liverpool. Adverse Drug Reactions: Classification.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

Genomics of Adverse Drug Reactions: The Need for a Multi-Functional Approach


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
    Presentation Transcript
    1. Genomics of Adverse Drug Reactions: The Need for a Multi-Functional Approach Munir Pirmohamed David Weatherall Chair of Medicine and NHS Chair of Pharmacogenetics Department of Molecular and Clinical Pharmacology University of Liverpool

    2. Adverse Drug Reactions: Classification • ON TARGET REACTIONS • Predictable from the known primary or secondary pharmacology of the drug • Clear dose-dependence relationship within the individual • OFF TARGET REACTIONS • Not predictable from a knowledge of the basic pharmacology of the drug and can exhibit marked inter-individual susceptibility • Complex dose-dependence

    3. Outline • Phenotyping • Sample sizes • Genomic approaches • The path to clinical translation • Genetic exceptionalism

    4. Genetic Contribution • Many factors predispose to adverse drug reactions, many of which are environmental and clinical • We do not know the overall genetic contribution to the occurrence of adverse reactions • The genetic effect will vary according to drug and reaction • ADRs account for: • 6.5% of all hospital admissions • 15% rate in in-patients • 8000 NHS Beds in the UK

    5. Deep Phenotyping • ADRs can affect any organ system, can be of any severity – MIMIC OF DISEASE • Important to be aware of the phenotypic heterogeneity – link between clinicians and genomics experts • Although overall burden of ADRs is high, the incidence of individual ADRs may be low or rare in many instances – so patient identification can be difficult (cf. Type 2 Diabetes)

    6. Power of Studies • Many pharmacogenetics studies in the past had small sample sizes, compunded by poor phenotype • Led to low effect sizes with lack of replication in independent cohorts • But since ADRs may be uncommon, it will never be possible to attain samples sizes seen in complex diseases • International consortia • Electronic medical records Toxic epidermal necrolysis 1 in million per year

    7. InTernational Consortium on Drug Hypersensitivity (ITCH) • 12 international centres • 50 UK centres • 1500 patients Sponsored by the International Serious Adverse Event Consortium (iSAEC)

    8. Electronic Medical Records: Clinical Practice Research Datalink • Previously GPRD • 12 million patient records (March 2011) Increased to 52 million with the transition to CPRD • Feasibility study using statin myopathy as paradigm • 641,703 patients prescribed a statin • 127,209 with concurrent CPK measurement

    9. The R&D Governance Burden Statin myopathy Identified via CPRD Link to DNA samples 132 R&D approvals

    10. Implicated SNP is in the SLCO1B1 gene (transporter) Shown with simvastatin 40mg and 80mg

    11. Statin Myopathy GWAS All myopathy (n=128) vs. WTCCC2 (unimputed) SLCO1B1

    12. Severe myopathy (n=32) vs. WTCCC2 (unimputed) SLCO1B1

    13. Carbamazepine Hypersensitivity • More complicated than abacavir hypersensitivity • Different phenotypes • Skin (mild → blistering) • Liver • Systemic (DRESS) • Predisposition varies with ethnicity and phenotype • HLA-B*1502 (Chinese) • HLA-A*3101 (Caucasian)

    14. CPT, 2012 HLA-B*1502

    15. Replicated in Japanese, Chinese, South Korean, Canadian and EU populations • NNT = 47 • SmPC/drug label changed (for information) • Patient and clinician preferences • Cost effectiveness • 55% likelihood • Cluster RCT being planned Liverpool 22 patients with HSS

    16. Whole Genome Sequencing in CBZ Hypersensitivity N= 48 (28 CBZ-induced severe hypersensitivity and 20 tolerant controls)

    17. HLA-A* Loci Using NGS data • 30 HLA-A* loci typed • 18 HLA-A* alleles identified • 40% CBZ hypersensitive patients are A*31:01 positive

    18. Rare Variant Pathway Analysis

    19. T Cells in Carbamazepine Hypersensitivity: HLA-A*31:01+ patient Clinical data Lymphocyte transformation test

    20. Carbamazepine-Responsive T-cell clones FasL IFNγ IL-13 Perforin Granz.B 0 Specificity and Phenotype CBZ FasL IFNγ IL-13 Perforin Granz.B Secretion of cytokines and cytolytic molecules a) CD4+ TCC 0 CBZ b) CD8+ TCC

    21. HLA Restriction of CBZ-Specific TCC MHC restriction of CD4+ (a) and CD8+ (b) TCC b) CD8+ (n=3) a) CD4+ (n=3) * p = 0.03 * p = 0.03 ns HLA class II restriction of CD4+ TCC HLA A31 restriction of CD8+ TCC ns * p = 0.03 n = 3 n = 3 p = 0.008 ** p = 0.004

    22. Hierarchy of Evidence What type of evidence is required for demonstration of clinical utility?

    23. Association with HLA-B*5701 Clinical phenotype Clinical genotype Technology-Based Reduction in the Burden of ADRs: The Case of Abacavir Hypersensitivity

    24. Uptake of HLA-B*5701 in Different Continents

    25. Drug label changed before prospective study Two prospective studies did not contradict previous data from retrospective studies

    26. Evidence standards differ between non-genetic and genetic tests • 3 examples given: • Drug exposure • Prevention of adverse drug reactions • Health technology assessment

    27. Drug Exposure: Differential Evidential Standards • Example: Aztreonam SmPC • “after an initial usual dose, the dosage of aztreonam should be halved in patients with estimated creatinine clearances between 10 and 30 mL/min/1.73 m2” • Many different examples in hepatic and renal impairment with dose instructions based on PK studies and occasionally PK-PD modelling • No need for RCTs – in fact, would be impractical • However, a genetic polymorphism leading to same degree of change in drug exposure is often ignored and/or RCT data are required for implementation

    28. Differential Evidence Standards • Unfamiliarity with genetic tests • Lack of experience in interpretation • Perceived cost of genetic testing • Lack of availability of tests • Poor turnaround time recommendations on dosing evaluation in patients with polymorphisms in known metabolic pathways

    29. Summary • Prediction of adverse drug reactions (safety biomarker) • Insights into mechanisms of the adverse drug reaction Poste, Nature, 2011

    30. “Hierarchies of evidence should be replaced by accepting—indeed embracing—a diversity of approaches..... ...It is a plea to investigators to continue to develop and improve their methods; to decision makers to avoid adopting entrenched positions about the nature of evidence; and for both to accept that the interpretation of evidence requires judgment.”

    31. Acknowledgements • The University of Liverpool • B Kevin Park • Ana Alfirevic • Maike Lichtenfels • Dean Naisbitt • Ben Francis • Dan Carr • Ann Daly (Newcastle University) • Panagiotis Deloukas (Sanger Institute) • SERIOUS ADVERSE EVENT CONSORTIUM • EPIGEN • EU-PACT • FDA • Funders: Dept of Health (NHS Chair of Pharmacogenetics) • MRC, WT, DH, NIHR, EU-FP7