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Benefit-Risk Assessment: IMI-PROTECT Case Study Example Benefit-Risk Assessment: IMI-PROTECT case study example Juhaeri Juhaeri, Ph.D. Head of Pharmacoepidemiology, Sanofi Adjunct Assistant Professor, UNC Chapel Hill 30th International Conference on Pharmacoepidemiology & Therapeutic Risk Management, Taipei, 24 October 2014 Disclaimers Juhaeri Juhaeri is an employee of Sanofi. The views and opinions represented in this presentation are solely those of the presenter and are not endorsed by nor necessarily representative of those of Sanofi The 5th Annual Meeting: Patient Safety - A Sustained Focus from Scientific Ideas to Innovative Medicines May 12-15, 2013 2 Objectives • To understand existing frameworks for a structured BR assessment • To describe quantitative BR methods to integrate benefits and risks Using rimonabant as an example 3 FDA Structured approach Decision Evidence and Conclusions Factor Uncertainties and Reasons Analysis of Condition Current Treatment Options Benefit Risk Risk Management Benefit-Risk Summary Assessment http://www.fda.gov/ForIndustry/UserFees/PrescriptionDrugUserFee/ucm326192.htm 4 EMA Project: 2 level approach • Qualitative approach • Quantitative approach: Multi-Criteria Decision Analysis (MCDA) method to derive a numerical value for the benefit–risk balance recommended for more complex situations *Zafiropoulos N, Phillips L, Pignatti F, Luria X. Evaluating benefit-risk: an agency perspective. Regulatory Rapporteur 2012 (9): 5-8 5 ISPE: Benefit-Risk Assessment, Communication and Evaluation (BRACE) 6 The Innovative Medicines Initiative (IMI) • The largest public- private initiative in biopharmaceutical research – € 1 billion from the European Commission (7th Framework Program for Research) – € 1 billion in kind contribution by EFPIA 7 IMI PROTECT Public Regulators: Private EMA (Co-ordinator) EFPIA companies: DKMA (DK) GSK (Deputy Co- AEMPS (ES) ordinator) MHRA (UK) Sanofi Roche Academic Institutions: Novartis University of Munich Pfizer FICF (Barcelona) Amgen INSERM (Paris) Genzyme Mario Negri Institute Merck Serono (Milan) Others: Poznan University of Bayer Medical Sciences WHO UMC Astra Zeneca University of Groningen GPRD Lundbeck University of Utrecht SMEs: IAPO NovoNordisk Imperial College London Outcome Europe CEIFE Takeda University of Newcastle PGRx (LA-SER) Eli Lilly 8 Recommendation Roadmap Exploration Evidence Conclusion and gathering and dissemination data preparation Analysis Planning http://www.imi- protect.eu/documents/HughesetalRecommendationsforthemethodologyandvi sualisationtechniquestobeusedintheassessmento.pdf 9 Classifications of approaches Mt-Isa S, et al. Balancing benefit and risk of medicines: a systematic review and classification of available methodologies. Pharmacoepidemiol Drug Saf. 2014 Jul;23(7):667-78. 10 Case studies: Methodologies Rimonabant Telithromycin Efalizumab Natalizumab Rosiglitazone Warfarin PrOACT-URL ✓ ✓ ✓ ✓ ✓ BRAT ✓ ✓ ✓ ✓ ✓ MCDA ✓ ✓ ✓ ✓ ✓ SMAA ✓ ✓ ✓ NNT & NNH ✓ ✓ Impact ✓ Number QALY Q-TWiST INHB ✓ BRR ✓ ✓ ✓ ✓ PSM ✓ ✓ ✓ MTC ✓ ✓ DCE ✓ Other: Direct utility SBRAM, Decision Decision Decision Individual elicitation, Swing- conferencing conferencing conferencing benefit risk Dashboard, weighting MACBETH, Probabilistic assessment MCDA AHP MCDA model (NCB) simulations 11 Rimonabant Case Study: Disclaimers • “The processes described and conclusions drawn from the work presented herein relate solely to the testing of methodologies and representations for the evaluation of benefit and risk of medicines. • This report neither replaces nor is intended to replace or comment on any regulatory decisions made by national regulatory agencies, nor the European Medicines Agency.” 12 Descriptive framework: PrOACT - URL Problem Uncertainty Objective Risk tolerance Alternatives Linked decisions Consequences Key references: Hunink M, Glasziou P, Siegel J, Weeks J, Pliskin J, Elstein A, et al. Decision making in health and medicine: Integrating evidence and values. Trade-off Cambridge: Cambridge University Press; 2001. Hammond JS, Keeney RL, Raiffa H. Smart choices. A practical guide to making better life decisions. New York: Broadway Books; 2002. 13 Descriptive framework: PhRMA BRAT (6) (1) (3) (5) (2) (4) Display & Decision & Define Identify Assess Identify Customise interpret communication of decision data outcome outcomes framework key B-R B-R assessment context sources importance metrics Key references: Coplan PM, Noel RA, Levitan BS, Ferguson J, Mussen F. Development of a framework for enhancing the transparency, reproducibility and communication of the benefit-risk balance of medicines. Clin Pharmacol Ther 2011 Feb;89(2):312-5 Levitan BS, Andrews EB, Gilsenan A, Ferguson J, Noel RA, Coplan PM, et al. Application of the BRAT framework to case studies: observations and insights. Clin Pharmacol Ther 2011 Feb;89(2):217-24 14 BRAT vs. PrOACT-URL vs. Regulatory PrOACT-URL BRAT Regulatory landscape Problem Define decision context Define decision context Objective Identify benefit and risk Identify important outcomes relevant information Alternative Define the decision Define decision context context Consequence Extract source data Identify data Customise framework Identify important relevant information Trade-off Assess outcome Weigh the information importance Uncertainty Weigh the information Risk tolerance Display & interpret key BR metrics Linked decisions 15 Decision context: Rimonabant Indication Weight loss in obese and overweight patients with co-morbidities in adults (>18y) Regulatory history Approved June 2006, Voluntary withdrawal in January 2009 Safety issue Increased risk of depression Data source EPAR Published clinical trials Methodologies PrOACT-URL, BRAT, MCDA, SMAA, tested NNT&NNH, Impact numbers, INHB, BRR, PSM + direct utility elicitation via survey 16 Rimonabant: value tree 17 Rimonabant: effect table Outcome Rimonabant Placebo Difference Benefits Weight loss (kg) -6.3 kg -1.6 kg 4.7 kg [4.1-5.3] Weight loss >10% 25.5% [23.8 , 6.6% [5.5 , 7.9] 19% [17 , 22] 27.3] OR=5.1 [3.6 – 7.3] Waist -6.2 [-7.2 , -5.2)] -1.9 [-2.3 , -1.4)] -4.3 [-5.5 , -3.0] circumference changes (cm) Systolic blood -1.3 [-2.0 , -0.5] 0.5 [-0.6 , 1.6] -1.8 [-2.8 , -0.8] pressure Risks Psychiatric 26.2% [24.5 , 14.1% [12.4 , 15.9] 12.1% [10 , 15] adverse event 28.0] OR=1.9 [1.5 , 2.3] Neurological 27.4% [25.7 , 24.4% [22.3 , 26.6] 3.0% [0.5 , 5.5] Adverse Event 29.2] OR=1.7 [1.1 , 2.7]* Serious adverse 5.9% [5.0 , 6.9] 4.2% [3.3, 5.3] 1.7% [0.4 , 3.0] event OR=1.43 [1.03 , 1.98] 18 Rimonabant: forest plot 19 Quantitative Methods • Quantitative frameworks: MCDA, SMAA • Utility survey techniques, eliciting stakeholders’ preference information: DCE • Estimation techniques: ITC/MTC, PSM • Metric indices: Impact numbers (including NNT/NNH), BRR 20 Multi-Criteria Decision Analysis (MCDA) MCDA has 3 ingredients: B-R evidence Value data functions Preference weights Overall benefit-risk balance Reference: Walker S, Phillips L, Cone M. Benefit-Risk Assessment Model for Medicines: Developing a Structured Approach to Decision Making. Epsom, Surrey: CMR International Institute for Regulatory Science; 2006. 21 MCDA 1. Select benefits and risks 2. Identify data sources and get the data 3. Apply a score to each benefit and risk (based on the data) 4. Apply a weight to each benefit and risk depending on their importance in the decision – Different perspectives 5. Calculate MCDA score for each drug 6. Assess sensitivity of the analysis: – Change in weight – Change in perspective Reference: Walker S, Phillips L, Cone M. Benefit-Risk Assessment Model for Medicines: Developing a Structured Approach to Decision Making. Epsom, Surrey: CMR International Institute for Regulatory Science; 2006. 22 1. Select benefits and risks (wave 1) 23 Wave 2 value tree: simplified 24 2. Get the data Criteria Placebo Rimonabant 10% Weight lost 0.11 [0.10,0.14] 0.40 [0.22,0.62] (proportion) HDL Cholesterol (mg/dL) 1.02 [0.95,1.10] 2.12 [0.26,3.93] Cardiovascular death 0.02 [0.01,0.02] 0.03 [0.00,0.99] (proportion) Depression (proportion) 0.01 [0.01,0.01] 0.02 [0.00,0.06] Constipation/Diarrhoea 0.05 [0.05,0.06] 0.07 [0.04,0.15] (proportion) 25 3. Apply score: rimonabant 85 26 Weight change 0.40 0.02 Psychiatric disorders 10% weight loss * Apply to comparison groups 4. Apply weights (medical/regulatory perspective) Benefits and Risks Weight Normalized weight Overall benefit 73 42.5 Overall risk 98 57.5 10% Weight lost 70 47.9 HDL Cholesterol 76 52.1 Cardiovascular death 100 35.7 Psychiatric disorders 90 32.1 GI disorders 90 32.1 5. Assess B/R - calculate MCDA Score: weighted sum model (WSM) ′ ● A: Total MCDA score of alternative i ● w′: normalized weight ● a: score/value of an event | 28 Rimonabant: Overall BR profile, MCDA model (wave 1) 29 Rimonabant: Benefit score 30 Rimonabant: Risk score 31 Weighted difference in score: rimonabant - placebo 32 6. Assess sensitivity: benefit 33 Sensitivity analysis: risk 34 Stochastic Multi-criteria Acceptability Analysis (SMAA) Data sample from distribution on each criterion Which alternatives is Utility score more preferred Repeat for n iterations (to account for variations in input) Weighted Weight average on sample from a each distribution alternatives Weighted utility score • Lahdelma R, Hokkanen J, Salminen P. SMAA - Stochastic multi objective acceptability analysis. European Journal of Operational Research 1998 Apr 1;106(1):137-43. • Tervonen, T. and Lahdelma, R. Implementing stochastic multicriteria acceptability analysis. European Journal of 35 Operational Research, 178 (2): 500-513, 2007 SMAA: illustration Iteration Value, Ranks
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