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Presentation at ABPI -- NIHRT R&D Conference, 360 Degrees of Health Data – Harnessing Big Data for Better Health, London, 21 November 2013

Presentation at ABPI -- NIHRT R&D Conference, 360 Degrees of Health Data – Harnessing Big Data for Better Health, London, 21 November 2013

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Big Data and Stratified Medicine Presentation Transcript

  • 1. Stratified Medicine: International Examples What can be learned from other countries about clinical implementation of stratified medicine? Professor Adrian Towse 360° of Health Data: Harnessing Big Data for Better Health ABPI and National Institute for Health Research Conference London • 21 November 2013
  • 2. Agenda • Phases of translation – getting to clinical implementation • Translation into clinical practice: The INCa approach in France • • US evidence: physicians do not feel equipped to translate tests results into actionable prescribing decisions • • Regional provision of subsidised testing Use of evidence-based clinical pharmacogenetic guidelines offer a way forward to overcome this barrier Generating the evidence, linking it to value, getting it used: • Need institutional processes and a framework for value assessment of new diagnostics • Need innovative approaches to evidence collection • Generate evidence into practice guidelines • Tackle the “silo budget” problems 2
  • 3. Phases of translation: trastuzumab and HER2 testing as example Based on the framework proposed by Khoury et al (2007) 3
  • 4. France (INCa) – An approach to clinical implementation • 28 regional platforms • Partnerships between several laboratories located in University hospitals and cancer centres • Cooperation between pathologists and biologists • Compensation of local pathologists for sample shipment • Free of charge to patients and hospitals • Public-private partnerships for molecular testing • Early phase network of 16 early phase clinical trial centers (CLIP2) Source: Buzyn (2013) 4
  • 5. France – estimates of economic impact of molecular testing PFS = progression free survival Source: Calvo (2011) • Focus on cost-offset arising from not treating nonresponder subgroups of patients identified through testing • May explain willingness to fund the INCa initiative 5
  • 6. US evidence on physician barriers to implementation • US Survey: 97.6% of responding physicians agreed that genetic variations may influence drug response, but only 10.3% felt adequately informed about pharmacogenomic testing. Physicians who feel well informed and have had pharmacogenetic instruction as part of their education are more often early adopters. Source: Stanek et al (2012) • Approaches to fill the current knowledge gap • Inclusion of information about genomic biomarkers in drug labels • Examples of a coordinated multidisciplinary team approach with appropriate informatics infrastructure are being researched (e.g. the 1200 patients project – ClinicalTrials.gov no. NCT01280825 [O’Donnell et al, 2012]) • Clinical guidelines from professional organizations (e.g., Clinical Pharmacogenetics Implementation Consortium) 6
  • 7. US Clinical Pharmacogenetics Implementation Consortium (CPIC) Originated late 2009. Members worldwide. Shared effort of Pharmacogenomics Research Network (PGRN) and The Pharmacogenomics Knowledge Base (PharmGKB) – originally US projects • Provides guidelines that enable the translation of genetic results into actionable prescribing decisions • Designed to help clinicians understand HOW available genotype results should be used to optimize drug therapy, not WHETHER tests should be ordered. 62 guidelines available on PharmGKB’s website, 14 available as peer-reviewed publications, e.g.: Drug Gene Publication date Abacavir HLA‐B Feb 2012 Clopidogrel CYP2C19 Jun 2011, 2013 5‐FU, Capecitabine DPYD Source: http://www.pharmgkb.org/ page/cpic Sep 2013 7
  • 8. Institutional processes for the value assessment of new diagnostics Source: Garau et al (2013) 8
  • 9. Proposed framework for assessing value of co-dependent technologies Source: Garau et al (2013) 9
  • 10. Molecular diagnostic tests: the evidence hurdle Marker Main study design Study size (patient numbers) Sponsor KRAS mutations (Anti‐EGFR  monoclonal antibodies in  CRC) Retrospective cohort  analysis of an RCT 1198 Drug developer & public  research body (a) Oncotype DX® & (b) MammaPrint®  (Prognostic/predictive in  BrCa) Retrospective RCT cohorts (a) 688, 651, 895 (b) Prognostic:  117, 295, 307, 123  Predictive: 241 Diagnostic manufacturer RCTs (a) (b) Public research bodies Retrospective RCT cohort+  Healthy volunteers 1477, 162 Public research body Prospective cohort study 4471 (Terminated early) Payer Proof‐of concept RCT  187 Diagnostic manufacturer CYP2C19  (Clopidogrel in ACS) 11248 6600 Public research body Source: Towse et al (2013) 10
  • 11. Innovative approaches: examples of performance-based arrangements • Oncotype DX breast cancer essay in U.S. • • UnitedHealthcare agreed to reimburse OncotypeDx test for 18 months; change in price based on appropriate use CYP2C9 and VKORC1 Genetic testing in U.S. • Pharmacogenomic testing of CYP2C9 or VKORC1 alleles to predict warfarin responsiveness is covered only for patients enrolled in a prospective RCT meeting specific standards • Gefitinib in the UK • Supplied at a single fixed cost of £12,200 per patient irrespective of the duration of treatment. NHS is not charged until third month of treatment is supplied • Oncology risk / cost sharing in Italy Source: Carlson (2013) 11
  • 12. Conclusions and next steps International evidence suggests • Subsidising testing can promote uptake – but a flawed value assumption? • Evidence-based guidelines can support clinicians • Innovative approaches to evidence generation may be necessary • Both separate and combined HTA processes for assessing value are required May also be important to address “silo budgeting” with test budgets and drug budgets held and managed separately “Big Data” has a key role to play: • Use of data to generate evidence for clinical practice guidelines • Ability to monitor and feed back outcomes for patients and actual costs to later validate (or not) assumptions made at time of launch • Support guideline implementation and provide feedback to clinicians 12
  • 13. Sources Buzyn, A. (2013) How is INCa supporting the development of personalised medicine? Presentation. Worldwide innovative networking in personalised cancer medicine. Paris. 10-12 July. Available at: http://www.winsymposium.org/wp-content/uploads/2013/07/WIN2013_AgnesBuzyn-REVISED.190713.pdf. Calvo, F. (2011) Personalised medicine: A nationwide initiative for an equal access to cancer treatment in France. Paris: National Cancer Institute. Available at: http://ec.europa.eu/research/health/pdf/event06/13052011/fabien-calvo_en.pdf Carlson, J. (2013) Performance-based risk sharing arrangements. Pesentation at the Second Annual Health Economics and Personalized Medicine Symposium. Garau, M. et al (2013) Can and should value-based pricing be applied to molecular diagnostics? Personalized Medicine. Khoury, M.J. et al. (2007) The continuum of translation research in genomic medicine: How can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? Genetics in Medicine. O'Donnell, P.H. et al (2012) The 1200 patients project: creating a new medical model system for clinical implementation of pharmacogenomics. Clinical Pharmacology & Therapeutics. PharmGKB website. Clinical Pharmacogenetics Implementation Consortium (CPIC) page. Available at: http://www.pharmgkb.org/page/cpic Stanek, E.J. et al (2012). Adoption of pharmacogenomic testing by US physicians: results of a nationwide survey. Clinical Pharmacology & Therapeutics. Towse, A. and Garrison, L. (2013) Economic incentives for evidence generation: Promoting an efficient path to personalized medicine. Value in Health. Towse, A. et al (2013) Understanding the economic value of molecular diagnostic tests: Case studies and lessons learned. Journal of Personalized Medicine. Vijverberg, S.J.H. et al (2010) Ethical and social issues in pharmacogenomics testing. Current pharmaceutical design. 13
  • 14. About OHE To enquire about additional information and analyses, please contact Prof Adrian Towse at atowse@ohe.org To keep up with the latest news and research, subscribe to our blog, OHE News and follow us on Twitter @OHENews, LinkedIn and SlideShare The Office of Health Economics is a research and consulting organisation that has been providing specialised research, analysis and expertise on a range of health care and life sciences issues and topics for 50 years. OHE’s publications may be downloaded free of charge for registered users of its website. Office of Health Economics Southside, 7th Floor 105 Victoria Street London SW1E 6QT United Kingdom +44 20 7747 8850 www.ohe.org ©2013 OHE 14