Preclinical Models to Support Dosage Selection Lisa Benincosa, Ph.D. Pfizer Global Research & Development Groton, CT

Objectives of Early Drug Development Identification of critical risk factors prior to investment in full clinical development select most promising compounds Provide critical data to identify safe and effective dose and dose regimens more efficient development

Continuum of PK/PD Modeling in Drug Development Validation Of PK/PD Relationship Preclinical PK/PD in experimental models PK/PD in healthy subjects PK/PD in dose-ranging study in patients using efficacy & safety endpoints (POC) Confirm efficacy & safety in the pivotal studies NDA

Preclinical Models Animal models used at Pfizer Advantages Challenges Murine pneumonia model, thigh model and peritonitis model Gerbil otitis media model Advantages Explore the in vivo exposure response relationship Explore hypothesis Assessment of PD at suboptimum doses Supra-therapeutic doses to explore full dose range Assessment of tissue distribution Challenges Validation of animal models for extrapolation of results to clinical setting

PK/PD Indices for Efficacy Global Indices AUC/MIC (e.g. fluoroquinolones, macrolides) Cmax/MIC (e.g. aminoglycosides) Time above MIC (e.g. b-lactams, carbapenems) New Approaches Incorporating PK and PD time course using mechanism based PK/PD models

Example Comparisons of Different Dosing Regimens of Azithromycin

Pharmacodynamic Results Gerbil otitis media model infected with H. influenzae (MIC = 1.6 mg/mL) Azithromycin (200 mg/kg) 9 B 1-Day Therapy J 2-Day Therapy H 3-Day Therapy F Infected Control Limit of Detection 8 H J F B F F F 7 6 Bacterial Recovery (log10 CFU per ml) H 5 4 J 3 2 B H J B J H B 1 24 48 72 96 Time in Hours Post-challenge Girard et al. ASM A-57, 2002

Hypothesis For azithromycin, front-loading (1-day) appears to be more effective although 2- and 3-day regimens were also effective Next Step: PK/PD model for azithromycin to quantitate the effect of front-loading the dose and to differentiate from 3-d and 5-d regimens

Study Design Gerbil Otitis Media Model with H. influenzae Threshold oral doses (~ED50) of azithromycin were selected for comparison: 1-day vs. 3-day vs. 5-day regimens Humanized PK profiles were generated using adaptive design Two H. influenzae strains were tested: 54A1100 and 54A1325 (MIC 0.5 and 2 mg/mL) Plasma concentrations and CFU were determined pre-dose, 1,2,3,4,5,6,12,24,48, and 72 hr (n=3 animals/time point) One group of 33 drug free controls were also evaluated at the above time points for each strain

Rationale of Dose Selection Emax Most informative region is between ED20 and ED80 Doses < ED20 all result in a high probability of failure Doses > ED80 all result in a high probability of cure % of Cure ED80 Eo ED20 ED50 Exposure/MIC

Results: Global PD Blue: H. influenzae MIC 2 mg/mL 1 1 1 Day 3 Day 5 Day Blue: H. influenzae MIC 2 mg/mL Red: H. influenzae MIC 0.5 mg/mL Log10(AUC/AUCGC ratio) -1 -1 — The line was the fitted curve for 1-day (•) at different doses -2 -2 -3 -3 -4 -4 100 100 200 200 300 300 400 400 Dose/MIC

Azithromycin Pharmacokinetic Profiles 1 Day Equivalent Regimens in Humans & Gerbils 3 Day Equivalent Regimens in Humans & Gerbils 0.4 0.4 0.3 0.3 AZM Concentration (mg/L) 0.2 0.2 AZM Concentration (mg/L) 0.1 0.1 0.0 0.0 24 24 48 48 72 72 24 24 48 48 72 72 Time (hours) Time (hours) Time (hours) Time (hours) — human; ---- gerbils

Dynamics of Bacterial Growth and Death Time course of total bacteria growth is a result of a mixture of homogenous sub-populations (mixture model) Model incorporates bacterial replication modelled as a capacity limited function 1st order rate constant for death Drug effect enhancing bacterial death or inhibiting replication Bacteria CFU/mL Pop 1 Pop2 Pop3 KD Replication IC50 Drug (+) (-) δCFUi/δt = Vgmax.CFUi/[CFUM + CFUTOT] – kd.I(t).CFUi I(t) = 1± [Emax.(C/MIC)H]/[SITMiH + (C/MIC)H]

Pharmacokinetic Results H. Influenzae (strain 54A1325) 404.8 mg/kg total dose – 1 day PK H. Influenzae (strain 54A1325) 404.8 mg/kg total dose – 3 day PK

Pharmacodynamic Results H. Influenzae (strain 54A1325) 404.8 mg/kg total dose – 1 day PD H. Influenzae (strain 54A1325) 404.8 mg/kg total dose – 3 day PD

Simulation based on PK/PD model 1-day Concentration vs Time 1-day % of Baseline Kd 3-day Concentration vs Time 3-day % of Baseline Kd Strain 54A1325

Summary Preclinical PK/PD modeling provided a means to quantitate the effect of front-loading the dose of azithromycin Front-loading the AUC of azithromycin results in a more rapid and complete bacterial kill Concentration related amplification of bacterial death (Kd) Having the highest AUC at the time of greatest bacterial count results in the greatest kill possible for both the sensitive and resistance strain Optimizes the likelihood of positive clinical outcome

Conclusions of Preclinical PK/PD Modeling Preclinical PK/PD models are useful for the selection of clinical dosing regimens Best surrogate of efficacy should be identified using mechanism based PK/PD models Global PK/PD indices of anti-bacterial efficacy may not be optimal Preclinical PK/PD models can be used to support the overall clinical benefit of the proposed clinical dosing regimen

Acknowledgements Pfizer Colleagues SUNY at Buffalo Cognigen Dennis Girard Amar Sharma Ping Liu Barbara Kamicker Mary Lame Steve Finegan Scott Seibel Judy Hamel L Dean Kendall Phil Inskeep SUNY at Buffalo Alan Forrest Lanre Okusanya (Pfizer Fellow) Brent Booker Cognigen Paul Ambrose Sujata Bhavnani