Impact of Prior Knowledge on Drug Development Decisions: Case studies across companies

1. Impact of Prior Knowledge on Drug Development Decisions: Case studies across companies Jaap W Mandema, PhD Quantitative Solutions Inc. 845 Oak Grove Ave, Suite #100 Menlo Park, CA 94025 Ph: 650-743-9790 Email: jmandema@wequantify.com ACPS 10-19-2006 October 19, 2006

2. Prior Information is always used for decision making Topic of today • The use of mathematical models to formally (quantitatively) use prior information to enhance decision making

3. What do models provide? Enhanced Data analysis • More effective use of the available data, resulting in increased knowledge and better (more precise) decision making Enhanced Trial design • Better understanding of the data we need and how best to obtain it to inform future decisions.

4. Models improve decision making by combining multiple pieces of information • Include information across time points • Understanding of the time course of response • Include information across doses • Understanding of the shape of the dose response relationship (e.g. Emax model) • Include information across trials • Accounting for differences in patient populations (e.g. disease severity) • Include information across drugs • Understanding similarities in dose response (e.g. similar Emax for analogues) • Include information across endpoints • Understanding of link between preclinical, biomarker and clinical endpoints (e.g. similar relative potency/ efficacy)

5. Trade-off between improved decisions and validity of assumptions Advantage Better decisions Disadvantage Validity of assumptions

6. Scope of data integration • Several to ~500 clinical trials • Several to ~15 endpoints • Preclinical, biomarker, clinical efficacy and tolerability • Summary level data +/- individual patient level data • Better understanding of impact of patient level covariates such as disease severity

7. Scope of application • Investment of several large pharma companies • All therapeutic areas • From late pre-clinical through post approval • Models are continuously updated as new information is obtained • Close collaboration between clinical pharmacology, statistics and medical specialties

8. Example: Importance of accounting for differences between patient populations

9. One of the conclusions of the meta-analysis • The net change in LDL-C is • Bezafibrate 8% (p=0.04) • Fenofibrate 11% (p=0.01) • Ciprofibrate 8% (p=0.005) • Clofibrate 3% (p=0.53) • Gemfibrozil 1% (p=0.68) • However, the LDL-C response is dependent on the baseline Lipid profile, which is quite different from trial-to-trial • Very different relative effects are calculated when the differences in baseline lipids are accounted for

10. Dependency of LDL effect of Fibrates on baseline triglycerides • mean LDL effect in trial normalized for dose and fibrate (size ~ sample size)

11. Example: value of pharmacological assumption • Meta-analysis of Statins, Ezetimibe, Fibrates, and Niacin to compare effectiveness/ tolerability profile as function of dose • Focus on combination products

12. With respect to LDL the only difference between Statins is dose After adjusting for differences in potency (ED50) all statins share a common dose response relationship for LDL

13. Interaction between statins and ezetimibe is characterized by simple interaction model

14. A simple interaction model for ezetimibe and statins • The interaction for lipid effects could be described by a simple interaction model • Only 1 additional parameter,  required to characterize the magnitude of interaction; •  > 0 means that the combined effect is greater than the sum of the effects of the drugs when given alone. •  of 0 means that the combined effect is the sum of the effects of the drugs when given alone. •  of -1 indicates a pharmacologically independent interaction. •  < -1 indicates a reduced benefit

15. Interaction model also characterized statin gemcabene combination

16. Interaction between Atorvastatin and gemcabene (600 mg) and ezetimibe (10 mg)

17. Value of model for development of novel lipid altering agent • Validated methodology of response-surface analysis • Significantly increased power of phase II design • Enabled assessment of the competitive clinical profile of a new lipid altering agent when given alone or in combination with a statin. • Precise quantitative assessment of benefit of gemcabene/ atorvastatin vs. ezetimibe/ atorvastatin combination

18. Example: accounting for random differences in patient populations • Meta analysis of 19 trials that evaluate Eletriptan and/ or Sumatriptan

19. Pain relief at 2 hoursObserved response (mean, 95% CI)

20. Pain relief at 2 hoursResponse adjusted for differences in placebo effect

21. Trial specific Random effects logistic regression model • P(Pain Relief)i represents the probability of a patient achieving pain relief in the jth treatment arm of the ith trial. • E0 represents the Placebo response; Emax is the maximum response; ED50 is the dose required to get 50% of maximum response. • i is a trial specific random effect with mean 0 and variance 2 to account for the heterogeneity among the trials. • No additional heterogeneity was found for Emax

22. Key question: Encapsulation does not impact the time course of response to Sumatriptan o Commercial Sumatriptan ΔEncapsulated Sumatriptan

23. But so much more was learned about the differences in speed of onset and magnitude of response between Eletriptan and Sumatriptan

24. Benefit of Eletriptan 40 mg over Sumatriptan 100 mg

25. Example: value of understanding comparative clinical profile of anti epileptic drugs (AEDs) • Comparative trials are limited because of large sample sizes required • Meta-analysis of 8 newer AEDs to compare effectiveness/ tolerability profile as function of dose • Literature data • FDA/ EMEA websites • Summary level data on almost 7000 patients with refractory partial seizures • Efficacy endpoints: • reduction in seizure frequency • proportion of patients with 50% or greater reduction in seizure frequency (responders) • Tolerability endpoint: • proportion of patients withdrawing from trial due to AEs

26. Dose response relationship for seizure frequency

27. Dose response analysis major findings • Significant random trial effect (heterogeneity) on mean response but not on treatment effect, validating placebo as an internal reference • Significant dose response relationship for each compound and each endpoint • High correlation between potency estimates for seizure frequency and responder endpoints • Significant differences between the AEDs in potency and selectivity for each endpoint, i.e. • Therapeutic window is significantly different between compounds

28. Comparison of Efficacy and Tolerability of AEDs

29. Comparison of Efficacy and Tolerability of AEDs

30. Value of model for novel AED development • Provided understanding of competitive landscape and product opportunities • Aided in quick assessment of potential of new AED • It is possible to get a good understanding of the competitive profile of the NCE with limited phase II data, i.e. small number of doses and limited sample size

31. Example: value of biomarker-endpoint models • Novel anti-coagulant for VTE prophylaxis • Analyzed dose response data for VTE and bleeding risk for Heparin, LMWH, Thrombin inhibitors, and FXa inhibitors after hip and knee surgery • Set targets and identify opportunity • Scale to NCE on basis of bio-marker data • Generated biomarker data internally because of inconsistency of methods • Used to optimize Phase II design for prophylaxis • Established link between efficacy and safety for prophylaxis of VTE and treatment of VTE • Acute and chronic treatment period • Used to select dose for VTE treatment

32. Example: value of biomarker-endpoint models • Novel PDE-5 inhibitor intended for the treatment of male erectile dysfunction • Scale clinical profile of PDE5 inhibitors to NCE on basis of relative potency (and efficacy) estimates from preclinical studies and Biomarker studies (efficacy) and first in man dose escalation studies (tolerability) • Model identified dose range to study • Wider instead of narrow range because of differences among “bio-markers” • Model allowed for scaling to moderate/mild patient population to set appropriate targets and expectations in that patient population. • Model enhanced power of phase II design • Analysis of prior data jointly with NCE data reduced sample size from 350 to 200 for equal decision making power • Model put trial in decision context of ability to identify dose and competitive positioning for phase III and not solely showing statistical benefit vs. placebo. • Better tolerability predicted by biomarker was confirmed in clinical trial

33. Example: value of biomarker-endpoint models • Preclinical and biomarker data show increased selectivity for beneficial effect vs. AEs for NCE • Biomarker-endpoint model put potency and selectivity from the biomarker study in a clinical context • How much more effect can we expect at similar AEs • Short and directed phase II study can quickly answer key development uncertainties: • Does biomarker selectivity translate into clinical selectivity? • Is Emax for clinical efficacy large enough to allow for a meaningful benefit

34. Opportunities at FDA • Important to engage with Industry • Wealth of Information to mine that can be used for patient benefit • Understanding of trial-to-trial variability in response • Explanatory covariates (disease severity) • Magnitude of random (non-explained) variability • Safety modeling • Therapeutic index across drugs: is reduced safety a drug effect or dose effect. • Biomarker linking • Predictive power of biomarkers (QTc)

35. Summary of Value • Better understanding of competitive landscape and targets • Better understanding of NCE earlier in development • Learn from other compounds, endpoints, and species • Enabling major improvements in clinical trial design • Better understanding of impact of patient and disease characteristics • Disease severity • Special populations • Objective quantitative assessment of information • as long as we state our assumptions

36. The current trend towardsModel-Based Drug Development • There is a tremendous opportunity to integrate the wealth of public and proprietary data spanning discovery and clinical into a probabilistic model of the compound’s product profile in relation to the compound’s competitors. • Utilize the smooth relationship across time, dose patient characteristics, and endpoints from our understanding of the underlying pharmacology and (patho)-physiology. • Models become knowledge repository and provide a quantitative basis for certain drug development and regulatory decisions