1. Improvement in Dose Selection Through Clinical PK/PD in Antimicrobial Drug Development: Perspective of an Industry PK/PD Scientist Gregory A. Winchell, Ph.D. Clinical Drug Metabolism Merck Research Laboratories

2. 2 Dose Selection During Drug Development Dosage regimen for –Phase IIa –Phase IIb –Phase III –Marketed product Dosing adjustments –Sub-populations –Individualization

3. 3 Introduction Dosage Regimen Plasma Concentration Site of Infection Effects Pharmacokinetics (PK) Pharmacodynamics (PD) Adapted from Rowland and Tozer, Clinical Pharmacokinetics: Concepts and Applications, 3rd ed., 1995

4. 4 Factors affecting efficacy of dosing regimen Pharmacokinetics –Plasma concentration-time profile depends on many factors –Can be characterized, including variability, in clinical studies –Concentration at site of action Pharmacodynamics –Susceptibility –Microbial uptake/binding –Microbial efflux/off-rates –Host factors –Site of infection –Heterogeneity –Post-antibiotic effect

5. 5 Anti-Microbial PK/PD Paradigm Characterize activity in nonclinical studies –In vitro systems – MIC, Kill curves –Animal models Identify metric of exposure that best correlates with efficacy –AUC/MIC, Cmax/MIC,Time above MIC Develop population PK model based on early clinical studies Use simulation to identify dosage regimens for initial efficacy studies Confirm regimens and exposure metrics in clinical trials

6. 6 Opportunities for Improvement Use dynamic PK/PD models Increased use of simulation at all stages of development Population PK/PD analyses in Phase II/III Better characterize key factors, e.g. –Adherence –Emergence of resistance –Host factors

7. 7 Dynamic PK/PD Models Anti-microbial response is a function, often complex, of drug concentration at site of infection over time Focus on exposure metrics like time above MIC and AUC/MIC does not fully account for dynamics PK/PD models incorporating both concentration and time are useful. –Can account for more complex data, e.g. post- antibiotic effect –Allows more robust simulations –Requires more data

8. 8 Characterizing Key Factors Adherence –Not accounting for adherence may obscure PK/PD relationship –PK/PD models can help assess effect of different adherence patterns Emergence of resistance can be characterized in the framework of a stochastic PK/PD model Host factors can be assessed as a covariate in population PK/PD analysis of clinical data

9. 9 Challenges Limited dynamic range in clinical studies –Limited dose ranges and regimens –Difficult to characterize concentration-response curve for serious infections –Difficult to validate correlations Practical issues –Timing –Logistics and resources –Organizational inertia/resistance Combination therapy

10. 10 Correlation between AUC and Trough - Caspofungin Phase II patients r 2 =0.89

11. 11 Anti-Infective Drug Development at Merck Indinavir (CRIXIVAN TM) – HIV protease inhibitor –Post hoc analysis of PK/PD Ertapenem (INVANZ TM ) IV/IM carbapenem –Dose selection based on time above MIC, human PK Caspofungin (CANCIDAS TM ) – First echinocandin antifungal –Prospective population PK/PD in all trials Drug in early clinical development –Human PK predictions to select initial clinical dose –Prospective population PK/PD to select dosage regimen for subsequent trials –MEMS-caps ® on bottle to capture compliance

12. 12 Contributors Julie Stone Anup Majumdar Carole Sable Jose Vega