Focusing on the key challenges Decision-making & drug development

1. Focusing on the key challenges Decision-making & drug development Peter Hertzman Paul Miller

2. Rationale • From societal perspective the case for bayesian analysis (BA) to inform adoption decisions for new technologies is strong • From an individual firm’s perspective there may be a lot more (other) reasons to use bayesian analysis • (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

4. Q1. What are the objectives of pharmacoeconomics?

5. 1st Objective of PharmacoEconomics Sales Revenue VOLUME PRICE

6. 2nd Objective of PharmacoEconomics PRICING APPROVAL FORMULARY LISTING Approval REIMBURSEMENT (PRODUCT LICENSING)

7. 3rd Objective of PharmacoEconomics R&D Costs Assist internaldecision-making and resource allocation during drug development

8. R&D Costs Approval Sales Revenue

9. Q2. Which tools do we use?

11. The Economist The Phase III Trial

12. Q3. Which tools could we use?

13. CTS PROs Longitudinal Databases Bayesian Analysis QoL Assessment Conjoint Analysis Contingent Valuation Economic Modelling Threshold Analysis Value of Information

14. So, how can we deliver more? • Exploit methodological advances in economic evaluation and decision theory • Integrate these into a broader range of activities • Review the timing of these activities in the product lifecycle

15. Quantifying Uncertainty Synthesis of Evidence Modelling Gathering information Predicting Optimizing decisions Benefits

16. 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

17. 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

18. Drug Development Process • Test 5,000 -10,000 compounds, to identify candidates for further development • Send approx. 250 for pre-clinical testing • 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: • Approval of the new drug: license • 10+ years after identification for development • Cost incurred per NCE = $ 600 million • Pricing & Reimbursement discussions

19. 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)

20. Two fundamental questions • Which projects do we invest in? • How do we maximise the efficiency of the projects we do choose? (i.e allocative and technical efficiency issues)

21. Decisions uncertainty Select candidate drugs to develop Clinical indication? Clinical trial design? P&R strategy? NICE REVIEW II III I time Stop/go?

22. Pilot outcomes & resource use questionnaire LAUNCH Preclinical Phase I Phase II Phase III Phase IV Collect cost & outcome data Populate economic models Inform external decision-makers

23. Ongoing evaluation in ‘real world’ Pilot outcomes & resource use questionnaire LAUNCH Preclinical Phase I Phase II Phase III Phase IV Scenario Modelling: estimate c/e ranges estimate budget impact determine price bands Collect cost & outcome data Populate economic models Inform internal decisions: 1. Project management 2. Portfolio management Inform external decision-makers

24. 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

25. 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?

26. Portfolio Management

27. 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.