Focusing on the key challenges Decision-making & drug development Peter Hertzman Paul Miller.
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Focusing on the key challenges Decision-making & drug development Peter Hertzman Paul Miller
Rationale 1.From societal perspective the case for bayesian analysis (BA) to inform adoption decisions for new technologies is strong 2.From an individual firm’s perspective there may be a lot more (other) reasons to use bayesian analysis 3.(2) may be the best foundation for (1) benefits (internal to the firm) during drug development will provide incentives to invest (promote) in BA this will impact societal HTA
Bayesian Analysis Longitudinal Databases CTS Conjoint AnalysisQoL Assessment Contingent Valuation Economic Modelling PROs Value of InformationThreshold Analysis
So, how can we deliver more? 1.Exploit methodological advances in economic evaluation and decision theory 2.Integrate these into a broader range of activities 3.Review the timing of these activities in the product lifecycle
Gathering information Modelling Predicting Optimizing decisions Benefits Synthesis of Evidence Quantifying Uncertainty
Need for Bayesian Analysis (Regulator’s perspective) To synthesise all available evidence in an explicitly quantitative analysis To quantify uncertainty To understand the marginal value of more information –Weigh contribution of more information (certainty) vs. Opportunity costs of delayed adoption Adopt in awareness of level of uncertainty; or Adopt, retricted to more certain domains (populations) Reject, value of more information > cost to society
Need for Bayesian Analysis (Pharma perspective) Where ’regulator’ requires it! Eg.UK NICE –Still not viewed as a real barrier to market access What proportion of global sales will be affected? –“only one (small) market” argument Does Pharma need to change the way it works? Weak incentive: “the stick” is not perceived as big enough! Carrots may be more effective! – scope for bayesian analysis in drug development process is large
Drug Development Process 1.Test 5,000 -10,000 compounds, to identify candidates for further development 2.Send approx. 250 for pre-clinical testing 3.Enter approx. 5 into: –Phase 1 trials (<100 healthy volunteers, to determine safety and dosage). If successful: –Phase 2 trials (<300 volunteers, to test for efficacy and side effects). If successful: –Phase 3 trials (> 1,000 volunteers, to monitor longer-term use and adverse reactions). If successful: 4.Approval of the new drug: license –10+ years after identification for development –Cost incurred per NCE = $ 600 million 5.Pricing & Reimbursement discussions
Observations Process is long, costly and risky! Highly regulated industry: –Gather drug profile information for regulatory authorities to make decisions (license, price, reimburse) –Some information then used for promotional claims to persuade customers to make decisions (also regulated)
Two fundamental questions 1.Which projects do we invest in? 2.How do we maximise the efficiency of the projects we do choose? (i.e allocative and technical efficiency issues)
uncertainty time Select candidate drugs to develop Clinical trial design? NICE REVIEW Clinical indication? Stop/go? I IIIII P&R strategy? Decisions
PreclinicalPhase IPhase IIPhase IIIPhase IV LAUNCH Pilot outcomes & resource use questionnaire Collect cost & outcome data Populate economic models Inform external decision-makers
PreclinicalPhase IPhase IIPhase IIIPhase IV LAUNCH Pilot outcomes & resource use questionnaire Collect cost & outcome data Populate economic models Inform external decision-makers Ongoing evaluation in ‘real world’ Scenario Modelling: estimate c/e ranges estimate budget impact determine price bands Inform internal decisions: 1. Project management 2. Portfolio management
Early = data vaccum? Not necessarily… Use what we do know! –PK & PD data, CTS, predict drug profile –Disease knowledge & Epidemiology –Market knowledge –Competitor knowledge –Regulatory requirements
Project Management Clinical development programme design: optimising decisions, eg. Number and timing of decision points Speed of development Order of trials Dose Sample size Sample selection About efficiency in trial design Optimise what? Intermediate or final endpoints?
References: Burman CF, Senn S. Examples of option values in drug development. Pharmaceut Statist. 2003;2:113-125 Poland B, Wada R. Combining drug-disease and economic modelling to inform drug development decisions. Drug Discovery Today 2001: 6(22):1165-1170. Shih Y-C T. Bayesian approach in pharmacoeconomics: relevance to decision-makers. Expert Rev. Pharmacoeconomics Outcomes Res. 2003; 3(3): 237-250.