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

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

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 Contributors Julie Stone Anup Majumdar Carole Sable Jose Vega