Quantitative Clinical Pharmacology: Applications of Modeling and Simulation in Clinical Development

1. Quantitative Clinical Pharmacology:Applications of Modeling and Simulation in Clinical Development Rajesh Krishna, PhD, FCP October 9, 2006 Program in Integrative Information, Computer and Application Sciences (PICASso), Princeton University

2. Prior Knowledge -Analogues -Disease -Competitors -Patients -Discovery/Pre-clinical Drug Product -indication -patients -formulation -dose -safety/ efficacy Drug Molecule Clinical Development Plan Trial 1 Trial 2 Trial … Trial N Learn Learn / Confirm Confirm Efficacy Tox PK/PD Mechanistic Therapeutic Benefit PPK/PD Clinical Endpoint MTD, Efficacy D-R PK/PD, (P)PK/PD Biomarker, Surrogate Typical Drug Development Program

3. Reality of Present Day New Drug Development • High NME attrition • High failure rate before IND • NME IND = NDA <20% of time • Reported >50% failure rate in Phase 3 (Carl Peck, CDDS) • Decreased NME NDAs despite increased INDs • Cost per NME approved estimated at >$800M (Tufts) Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004 (3):711-715

4. Probability of Success for New Mechanisms ~11% Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004 (3):711-715

5. New Mechanisms Often Fail Because of Lack of Efficacy or Demonstrated Benefit Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004 (3):711-715

6. Drivers For Change • Escalating R&D costs despite flat growth • R&D expenditure not proportional to number and quality of NMEs • Need to  productivity (do more with same amount of investment) • Low POS of NME’s entering Phase I - ~11% (Kola and Landis, 2004) • Phase II as stage for decision making often the default today • High attrition given low POS for new mechanisms without evidence of pharmacological benefit in Phase I • R&D costs required to support decision-making for new mechanisms this late, given POS, prohibitive • Lack of resources to create tools to facilitate early decision-making • Knowledge management and integration • New tools to link outcomes, predict hazards, reduce uncertainty in risk/benefit • Quick win-quick kill paradigm

7. FDA Critical Path Initiative • Goals • Develop new predictive “tools” • Improve the productivity and success of drug development • Speed approval of innovative products Adapted from: http://www.fda.gov/oc/initiatives/criticalpath/

8. Areas for Change • Key objective: • Need to dramatically improve predictions of efficacy and safety in clinical development • Enablers: • Biomarkers ~ target validation • Biomarker qualification, qualifying disease specific biomarkers • M&S ~ effective knowledge management leveraging bioinformatics • Drug disease models, clinical trial simulation • Clinical trials ~ better decision making, improving efficiency • Adaptive trial designs, seamless trials

9. PK Mixed Effects Modeling 0.20 0.15 Drug Concentration 0.10 0.05 Safety Efficacy 0.0 0 1 2 3 4 5 6 Time (h) Pulse Check - Terminology

10. Model Based Drug Development • Hypothesis based drug development emphasizing integrating information and improving the quality of decision making in drug development • Preclinical and clinical biomarkers • Dose-response and/or PK-PD relationships • Mechanistic or empirical disease models • Novel clinical trial designs • Clinical trial simulations and probabilities of success • Baseline-, placebo- and dropout-modified models • Outcome models

11. Roadmap for Model Based Drug Development Capture Prior knowledge Model Clinical trial Simulate Optimize

12. Case Example 1: Meta-Analysis of Statin Efficacy • Accumulated data from 25 trials (~9500 patients) • 5 Pfizer sponsored trials for Lipitor • 7 AstraZeneca summary basis trials for Crestor • 9 Merck summary basis trials for Zetia • 4 Pfizer sponsored trials for an investigational non-statin • Epidemiology Trials • Wilson et al, Framingham risk equations, • (Prediction of Coronary Heart Disease Using Risk Factor Analysis, Circulation, 1998, 97:1837-1847) • Riker et al, C-Reactive Protein and LDL • (Comparison of C-Reactive Protein and Low-Density Lipoprotein Cholesterol Levels in the Prediction of First Cardiovascular Events, NEJM 2002, 347:1557-1565) Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 

13. Pharmacodynamic Model Development • Trials looked at the following alone or in combination with Ezetamibe or gemcabine: • Atorvastatin • Rosuvastatin • Simvastatin • Lovastatin • Pravastatin Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 

14. Dose vs LDL lowering Response: Population, Mixed Effects Response Meta-Analysis:

15. Statin Dose-Response Relationship: Absence (E 0) and Presence of 10 mg Ezetimibe (E 10) Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 

16. Dose-Response Relationship for Non-Statin Without and With Statin With Atorvastatin Alone Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 

17. Prediction of Simvastatin Risk Reduction vs Dose Using a Model Based Approach Adapted from D. Stanski

18. Case Example 2: Gabapentin Approval and Label • Gabapentin was approved by FDA for post-herpetic neuralgia • Approved label states under clinical studies: “Pharmacokinetic-pharmacodynamic modeling provided confirmatory evidence of efficacy across all doses” • Model and Data Provided with Submission • FDA reviewers used model to test various scenarios • Supported doses and conclusions of Pfizer • Provided confidence to eliminate need for replicate doses • FDA proposed language in the label on PK-PD modeling and clinical trials Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005

19. Gabapentin PHN Study Designs • Used all daily pain scores • Exposure-Response analysis utilized titration data for within-subject dose response Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005

20. Gabapentin PHN Data Fits Time Dependent Placebo Response, Emax Drug Response and Saturable Absorption Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005

21. Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005

22. Case Example 3: Optimizing Dose Selection for an ACE Inhibitor • A 2-compartment PK model with first order absorption and first order output • Daytime variation of ACE is described with a cosine function with time period tp, amplitude A and shift ACE(t)=ACEo+ A cos(2π (t+S)/tp) • An Emax model and a sigmoidal Emax model are tested to describe the relationship between concentrations and plasma ACE activity Adapted from: Pfister. Dose selection of M100240. J Clin Pharmacol 2004 Jun;44(6):621-31

23. Simulation Scenarios • Target: • > 90% inhibition of plasma ACE activity in at least 50% of patients • Simulations at steady state: • For comparison of oral daily doses ranging from 25 to 150 mg • PK and plasma ACE activity profiles (n=500) under these dosage regimens are simulated with parameters drawn from the population PK and PD distribution

24. Model Based Simulations of BID Regimens PK ACE activity Fraction of patients achieving target; horizontal lines denote 50 and 80% 24h 24h

25. Model Based Simulations of TID Regimens

26. Simulations at Steady State • Simulations are used to evaluate candidate QD and BID dose regimen to achieve >90% plasma ACE inhibition at 24 hours • For comparison of oral daily doses ranging from 25 to 250 mg, PK and plasma ACE activity profiles (n=500) under these dosage regimens are simulated with parameters drawn from the population PK and PD distribution

27. Fraction of Patients Achieving 90% ACE Inhibition at Trough

28. Case Example 4: Tipranavir (TPV) Approval and Label • Protease inhibitor for experienced patients or patients with viral resistance to other PIs • Plasma TPV levels are a major driver of efficacy and toxicity, boosted with ritonavir (RTV) • HIV-1 protease mutations represent a major driver of resistance and decreased efficacy • 500/200 TPV/RTV dose employed in Phase III • Plasma TPV levels > IC50 to suppress viral load and avoid development of resistance Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005

29. Inhibitory Quotient (IQ) As A Predictor of Efficacy • Protein Binding Correction Factor (PBCF = 3.75x) • TPV is highly bound in plasma (99.96 - 99.98%) • Cell culture media only contains 6% fetal bovine serum (99.88%) • PBCF estimated using 2 methods: • Method 1: Equilibrium Dialysis: 0.120% free / 0.034% free = 3.5x • Method 2: Addition of 75% human plasma to antiviral assay resulted in a 4x shift • IQ = Cmin / (IC50 fold WT ● mean WT HIV IC50● 3.75) standardized TPV susceptibility PBCF susceptibility in patient isolate PK Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005

30. Control+T20 Control TPV Cmin, IC50, T20 Parameters and Viral Response For IQ ≥ 100, 54% responded to TPV and 73% responded to TPV+T20For IQ < 100, 21% responded to TPV and 52% responded to TPV+T20 Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005

31. Benefit: Viral Load Change From Baseline (log10) Risk: Grade 3-4 ALT, AST or Bilirubin Risk vs. Benefit: Impact of IQ on 24-Week Viral Load Response and Cmin on Liver Toxicity Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005

32. TPV Label Statements • “Among the 206 patients receiving APTIVUS-ritonavir without enfuvirtide…..the response rate was 23% in those with an IQ value < 75 and 55% with an IQ value > 75.” • “Among the 95 patients receiving APTIVUS-ritonavir with enfuvirtide, the response rate in patients with an IQ < 75 vs. those with IQ > 75 was 43% and 84% respectively.” Adapted from: TPV Label, under “Pharmacodynamics”.

33. Case Example 5: Drug Disease Model • Mechanistic disease model for HIV/AIDS • Pharmacodynamic model incorporating dose, concentration, HIV viral load time course • Biomarkers of efficacy – viral RNA time course • Biomarkers of safety – GIT events time course • Dose response relationships or PK/PD model • Outcome analysis

34. Components of Drug Disease Models

35. Viral Dynamics Adapted from: Bonhoeffer (1997) Proc. Natl. Acad. Sci. USA 94, 6971-6976

36. p d2 PI Active Infected l fAbVT CD4+ Cells (N)NRTI Virus a + fLbVT Latent Infected (N)NRTI d1 c d3 Drug Disease Models l:production rate of target cell d1: dying rate of target cell c: dying rate of virus b: infection rate constant d2: dying rate of active cells d3: dying rate of latent cells p: production rate of virus Adapted from: J Acquir Immun Defic Syndr 26:397, 2001, FDA EOP2A Slides

37. Case Example 5: Applying Drug HIV Disease Model • Maraviroc (MVC;UK-427,857) • Novel CCR5 antagonist in development for the treatment of HIV • Blocks the CCR5 receptor, which is used by HIV to enter CD4+ cells • Simulate decline of HIV-1 RNA plasma levels for 400 patients per treatment arm • Dosing regimens simulated were as follows: 150 mg twice daily fed, 150 mg twice daily fasted, and 300 mg once daily fasted • HIV-1 RNA measurements were performed daily for 40 days after the start of treatment

38. HIV-1 RNA Log10 Time Course Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191

39. Measured and Predicted HIV-1 RNA log10 Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191

40. Measured and Model Simulated HIV-1 RNA log10 Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191

41. Predicted Inhibition Fraction in Function of Mean Viral Load Drop Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191

42. Prior Knowledge -Analogues -Disease -Competitors -Patients -Discovery/Pre-clinical Drug Molecule Clinical Development Plan Trial 1 Trial 2 Trial … Trial N Summary: QCP Enabled Drug Development Program Drug Product -indication -patients -formulation -dose -safety/ efficacy QCP Enablers: Reducing Uncertainty in Risk/Benefit Drug and Disease Modeling Dose Response, PK-PD and Dosing Targeted Label Information Optimal Use Adaptive Trial Design “The best way to predict the future is to create it” – Peter F. Drucker

43. Acknowledgements • John Wagner (Merck) • Gary Herman (Merck) • Marc Pfister (BMS) • Joga Gobburu (FDA)

44. Questions

45. Modeling and Simulation in Drug Development