HCV Model Development: Industry Perspective

1. HCV Model Development: Industry Perspective Larissa Wenning Quantitative Translational Models to Accelerate Hepatitis C Drug Development August 2, 2012

2. What Do We Want From HCV Models? OUTPUTS INPUTS Data Portable, integrated form of knowledge Clearly Defined Assumptions Exploration of Knowledge Gaps MODELING Integrated, mathematical representation of all inputs Ideas & Scientific Knowledge EnhancedUnderstanding Predictions vs. Observations EnhancedDecision Making

3. What Kind of Questions Might We Answer With Models? • What is the therapeutic window for my compound? • Is there a dose where we can maximize efficacy and minimize adverse events? • What are the optimal combinations of compounds? • What is the optimal dosing regimen and duration of therapy for each of the many patient populations we are interested in? • What is the impact of factors that alter the pharmacokinetics of my compound(s) on efficacy and/or safety? • Drug-drug interactions, formulation changes, special populations

4. What Will It Take To Get to Enhanced Decision Making? • Flexible, standardized model structures • Understanding of relationship between in vitro and in vivo data • Ability to leverage data from the outside world

5. HCV Viral Dynamics Model: End Goal System parameters • Rates for infection & production of virus • Rates for clearance of virus & hepatocytes • Regeneration of hepatocytes Drug parameters Patient parameters • Efficacy • Dose/exposure response • Against different GTs and RAVs • Patient population (naïve, experienced, null, etc) • PR responsiveness • IL28B genotype • Other factors relevant for IFN-free regimens? • Resistance • Shift in drug efficacy • Baseline amounts and relative fitness of RAVs selected by drug • HCV genotype • GT1, 2, 3, etc • Baseline HCV • Pre-existing RAVs from prior treatment or polymorphisms • High vs low baseline viral load • Combinations • Efficacy is additive, synergistic, etc? • Resistance with combination Core viral dynamics model with clear separation between parameters associated with the biological system (virus, hepatocytes), and those associated with the drug; can “plug and play” parameter sets to simulate combinations of drugs in different populations of interest

6. Complex, Multifactorial Problem • As a single company, we do not have enough data to address all of the relevant factors in a timely manner! • Must draw data from the outside world & leverage non-clinical data

7. Merck HCV Viral Dynamics Model: Current State Drug effect only on production of virus Not shown in diagram, but RBV treatment assumed to result in production of non-infectious virus, which also decays at rate c; then measured total virus = infectious + non-infectious Total # of hepatocytes assumed to remain constant (T0) Model applied to several compounds: MK-5172, MK-7009, Peg-IFN, RBV, boceprevir, etc

8. Example 1: MK-7009Developing a Model for Multiple Compounds & Patient Populations • M&S objective: improve the understanding of MK-7009 dose and treatment duration needed to cure HCV in combination with SOC treatment of peg-IFN and RBV, accounting for viral dynamics with resistant virus • Approach: pool data across multiple studies, including MK-7009 monotherapy, MK-7009 + PR and PR alone (IDEAL study) Poland et al, American Conference of Pharmacometrics, San Diego, CA, April 2011.

9. Model structure is flexible enough to represent range of behaviors in viral load Illustrative example showing fit to 3 individual subjects in an MK-7009 Phase II study (MK-7009+PR x 4 wks followed by PR x 44 wks):

10. Can account for different patient populations using different parameter distributions • Example: response to treatment with PR using data from IDEAL study • IDEAL study is in treatment naïve population, but contains those who will in the future be treatment experienced. “Treatment Naïve” “Treatment Experienced”

11. Final Model Can Be Used to Simulate Many Scenarios Example: Simulated MK-7009 Dose-Response with PR in Treatment-Naïve Patients MK-7009+PR through treatment period

12. Example 1 Conclusions • A relatively simple viral dynamics model can predict short-term and longer-term response to HCV treatment with peg-IFN+RBV, protease inhibitor monotherapy, and triple combination therapy, in patients with little or no prior treatment. • With a very small estimated ED50, MK-7009 BID administered with Peg-IFN and RBV is predicted to sharply improve SVR over Peg-IFN and RBV alone. • Simulations show that proportion of patients cured increases with treatment time and continues to increase long after proportion with undetectable virus plateaus

13. Example 2: MK-5172Leveraging in vitro data • M&S objective: use existing viral dynamics model (developed for MK-7009+/-PR), clinical data from a monotherapy study, and in vitro data to project clinical response for MK-5172 in a number of scenarios • Challenge: Monotherapy data includes patients infected with GT1 and GT3. GT3 data shows dose response, but GT1 does not (all doses appear maximally efficacious) • Approach: Fit monotherapy data for GT3 and GT1 simultaneously and assume relative potency observed in vitro (24-fold more potent for GT1 vs GT3) translates directly in vivo; leverage existing model for PR to simulate combination of MK-5172 +PR Nachbar et al., EASL 2012 & 7th International Workshop on Hepatitis C Resistance & New Compounds

14. Data & Model Fit

15. Monotherapy Predictions Dose differentiation for GT1 predicted to be at or below 10 mg 50 mg dose for GT3 predicted to be no different than placebo

16. Setting up Simulation of Combination Therapy: Simulation for Efficacy Against RAVs • Simulate total viral load for a range of ED50,m/ED50,wt ratios to determine reasonable range for ED50,m • Sizable breakthrough on treatment in simulations for 30- and 100-fold shift in potency against resistant virus • Fold shift in potency against resistant virus therefore not greater than 10

17. Simulation of Combination TherapyPercent below limit of detection Very high percentage of patients are expected to become undetectable quickly, and remain so while on therapy

18. Simulation of Combination TherapyProjected % Breakthrough, Relapse, and SVR

19. Example #2 Conclusions • Monotherapy study: • In vitro data has been used successfully to bridge efficacy between genotypes in a viral dynamics model • This tactic may have broader utility to inform relative potency for genotypes and RAVs in these models for early clinical response prediction • For GT1: 10 mg QD dose is predicted to be noticeably less efficacious compared to higher doses • For GT3: 50 mg QD dose is predicted to be similar to placebo in terms of viral load decline • Subsequent studies: • Simulations with the 2-species combination treatment model predict high SVR rates with low viral breakthrough due to RAVs • Comparison of future clinical results to such prospective predictions is planned to further evaluate this early response prediction approach

20. Conclusions & Wrap-Up • HCV viral dynamics models have the potential to be very useful tools for enhancing decision making by development teams • Flexible, standardized model structures & ability to leverage outside and non-clinical data are critical in the current fast-moving, ever-changing development environment for HCV

21. Acknowledgments • The M&S Network at Merck • Merck’s HCV M&S team: Bob Nachbar, Luzelena Caro, Julie Stone, many others! • Project teams for MK-7009 and MK-5172 • Bill Poland; Pharsight • John Tolsma, Haobin Luo, Jonna Seppanen; RES Group

22. Back-Up Slides

23. Combination Therapy Model