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Preclinical Models to Support Dosage Selection

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

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Preclinical Models to Support Dosage Selection

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

  2. 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

  3. NDA 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

  4. Preclinical Models • Animal models used at Pfizer • 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

  5. 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

  6. Example Comparisons of Different Dosing Regimens of Azithromycin

  7. B 1-Day Therapy J 2-Day Therapy H 3-Day Therapy F Infected Control Limit of Detection Pharmacodynamic Results Gerbil otitis media model infected with H. influenzae (MIC = 1.6 mg/mL) Azithromycin (200 mg/kg) 9 8 F J F B H F F 7 6 Bacterial Recovery (log10 CFU per ml) H 5 4 J 3 B 2 B H J B J H 1 0 24 48 72 96 Time in Hours Post-challenge Girard et al. ASM A-57, 2002

  8. 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

  9. 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

  10. Rationale of Dose Selection • 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 Emax % of Cure ED80 Eo ED20 ED50 Exposure/MIC

  11. Results: Global PD 1 Day 1 1 3 Day 5 Day 0 0 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 0 0 100 100 200 200 300 300 400 400 Dose/MIC

  12. Azithromycin Pharmacokinetic Profiles 3 Day Equivalent Regimens in Humans & Gerbils 1 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 0 0 24 24 48 48 72 72 0 0 24 24 48 48 72 72 Time (hours) Time (hours) Time (hours) Time (hours) —human; ---- gerbils

  13. Drug (+) (-) Bacteria CFU/mL Pop 1 Pop2 Pop3 KD Replication IC50 IC50 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 δCFUi/δt = Vgmax.CFUi/[CFUM + CFUTOT] – kd.I(t).CFUi I(t) = 1± [Emax.(C/MIC)H]/[SITMiH + (C/MIC)H]

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

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

  16. 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

  17. 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

  18. 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

  19. Pfizer Colleagues 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 Acknowledgements

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