1. Modeling and Simulation beyond PK/PD CPTR Workshop October 2 – 4, 2012 Pentagon City

2. M&S-WG Objective: For Preclinical Phases: Deliver Quantitative PKPD models to help sponsors select therapeutic combinations For Phase I: Deliver PBPK models to help sponsors predict first-in-human results for combination regimens (Pulmosim/SIMCYP) For Phase II & III: Deliver clinical trial simulation tools (based on quantitative drug-disease-trial models) to be used to help design TB drug regimen development studies Here a more in-depth look at the clinical setting Mission and Goals

3. CPTR M&S Projects PBPK Clinical trial simulation tools Preclinical PKPD models SIMCYP Grant Application (CPTR+U of F) Pulmosim tool from Pfizer Developed TB modeling inventory Develop drug- disease-trial model for TB White papers FDA qualification Data standards Data sources Database 3 Hollow Fiber model

4. PBPK Complex ADME processes: PBPK models account for anatomical, physiological, physical, and chemical mechanisms. Multi-compartment approach to account for organs or tissues, with interconnections corresponding to blood, lymph flows and even diffusions. Develops a system of differential equations for drug concentration on each compartment as a function of time Its parameters represent blood flows, pulmonary ventilation rate, organ volumes etc., for which information is reliable known [Enter Presentation Title in Insert Tab > Header & Footer4

5. PBPK Integrates the Complex Process of Distribution Normal lung tissue [Enter Presentation Title in Insert Tab > Header & Footer5 Inflamed lung tissue Granulomatous tissue CPTR

6. PBPK 6 PulmoSim: Framework for inhaled drugs that can serve as a foundation for orally administered antibiotics systemically distributed to the lungs

7. Clinical Trial Simulation Tools Integrate the disease with pharmacology models Takes into account design considerations Gobburu JV, Lesko LJ. Annu Rev Pharmacol Toxicol. 2009;49:291-301.

8. 8 Trial Simulations Optimize Design Based on Quantitative Principles Test Multiple Replications of Trial Design Assumptions Modify Design 0.40.50.60.70.8 0 10 20 30 40 50 60 Effect of Dose and Number of Subjects on Power to Estimate Significant Effect of Drug vs Placebo 1 mg2 mg5 mg10 mg20 mg 304.56.51848.573.5 401329768791 5027.55285 9599 6040.5629097100 7055.5 71 9499100 N Drug/Disease Model Trial Designs X possible doses Different N Sampling time Inclusion criteria Range of Outcomes Analytics/Statistics CFU Trial Simulations Optimize Design Based on Quantitative Principles

9. For Predictions the Top-Down Approach is Too Limiting Describes existing data, lacks mechanistic insights, limited to explore new scenarios. Davies GR, et al. Antimicrob Agents Chemother. 2006;50(9):3154-6.

10. But the Bottom-up Approach is too expansive Requires detailed mechanistic understanding, makes models more “portable”, limited by unverifiable assumptions. Wigginton JE, et al. A model to predict cell-mediated immune regulatory mechanisms during human infection with Mt. J Immunol. 2001;166:1951-67

11. Intermediate Approach: Mechanistically-Inspired Retains key mechanistic verifiable components, allows for parameter estimations and is fit for simulation purposes Marino S et al. A hybrid multicompartment model for granuloma formation and T-cell priming in TB. J of Theor Bio. 2011:280:50-62

12. Leverage can be Obtained From Other Areas Predator-Prey models in viral infections such as with HCV may provide useful insights for TB modeling and simulation Guedj J. et al. Understanding HCV dynamics with direct-acting antiviral agents due to interplay between intracellular replication and cellular infection dynamics. J Theor Bio 2010;267:330-40

13. The Path Forward to a Successful M&S Platform in TB Obtain the right datasets to model the dynamics of CFU as a function of drug exposure/dose and disease progression in a mechanistically-inspired setting – Longitudinal data – Different combination therapies – Drug susceptible, MDR and XDR strain data Develop model that is predictive of CFU and linked to outcome taking into account appropriate other factors as co-therapy, demographics etc Test and validate the model(s) with regulatory buy-in Develop tool that can interrogate the model to aid in trial design of compounds under investigation or in development [Enter Presentation Title in Insert Tab > Header & Footer13

14. Regulatory Review Process: What’s success? Informal discussion with FDA/EMA. Sponsor submits a letter of intent requesting formal qualification. FDA/EMA Review Team formed. Sponsor submits briefing document. F2F meeting between sponsor and FDA/EMA Review Team. Review Team may request additional information. Sponsor submits full data package. Review process within FDA/EMA begins. Consultation and Advise Process 14 Regulatory decision qualifying or endorsing the submitted tools Success!!!

15. Modeling and Simulation beyond PK/PD CPTR Workshop October 2 – 4, 2012 Pentagon City

16. WHAT PREDICTIVE MODELING SHOULD DO A DISEASE MODEL AND A MATHEMATICAL MODEL SHOULD GIVE A QUANTITATIVE PREDICTION: HOW MUCH RESPONSE? WITH WHAT DOSE? ACCURACY SHOULD BE JUDGED BASED ON CLINICAL EVENT RATES and NOT another model or CONSESUSS ACCURACY SHOULD BE BASED ON HOW ACCURATE CLINICAL PREDICTIONS ARE, NOT ON LACK OF COMPLEXITY OF THE MODELING

17. M. tuberculosis in the hollow fiber system Gumbo T, et al. (2006) J Infect Dis 2006;195:194-201

18. HFS: Moxifloxacin Concentration-Time Profile

19. HFS, Simulations and Predictions Later on “Validated with CLINICAL Data” Efflux pump & cessation of effect of antibiotics The rapid emergence of quinolone resistance The potency & ADR of Cipro/Orflox versus Moxi The “biphasic” effect of quinolones The exact dose of Rifampin associated with optimal effect The population PK variability hypothesis, and the rates of ADR arising during DOTS The role of higher doses of pyrazinamide The “breakpoints” that define drug resistance

20. The HFS in Quantitative Prediction HFS quantitative output on the relationship between changing concentration and microbial effect Human pharmacokinetics and their variability MODELING & SIMULATIONS Predictive outcome: dose, breakpoints, microbial effect, resistance emergence, regimen performance

21. Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36

22. ISONIAZID HFS: Monte Carlo Simulations INH inhibitory sigmoid E max based on hollow fiber studies % patients with nat-2 SNPs associated with fast acetylation versus slow acetylation in different ethnic groups: Cape Town, Hong Kong, Chennai M. tuberculosis MICs in clinical isolates Population PK data from (Antimicrob.Agents Chemother. 41:2670- 2679) input into the subroutine PRIOR of the ADAPT II 9,999 Monte Carlo simulation for different ethnic groups to sample distributions for SCL→AUC→AUC/MIC→EBA Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36

23. PK-PD PREDICTED vs OBSERVED EBA IN CLINICAL TRIALS Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36

24. PREDICTION PREDICT: EtymologyEtymology via Latin:Latin præ-, "before" dicere, "to say". “PREDICT” to say BEFORE QUALITATIVE: Predict an event in terms of whether it occurs QUANTITATIVE: Predict extent and values prior to the event ORACLES AND DEVINING THE FUTURE http://www.crystalinks.com/delphi.html

25. If MDR-TB Does Not Arise From Poor Compliance, Why Does It? Hypothesis: Perhaps the PK system (i.e., patient’s xenobiotic metabolism) is to blame HFS output: kill rates, sterilizing effect rates (i.e., log10 CFU/ml/day) Known clinical kill rates, sterilizing effect rates (i.e., log10 CFU/ml/day) Performed MCS in 10,000 Western Cape Patients on the FULL REGIMEN Srivastava S, et al. J. Infect. Dis. 2011; 204:1951-9.

26. Sputum conversion rate predicted = 56% of patients Sputum conversion rate from prospective clinical studies in WC= 51-63% External Validation of Model: Sputum Conversion Rates in 10,000 Patients Srivastava S, et al. J. Infect. Dis. 2011; 204:1951-9.

27. Many (simulated) patients had 1-2 of the 3 drugs at very low concentration throughout, leading to monotherapy of the remaining drug Drug resistance predicted to arise in 0.68% of all pts on therapy in first 2 months despite 100% adherence Srivastava S, et al. J. Infect. Dis. 2011; 204:1951-9.

28. Prospective study of 142 patients in the Western Cape province of South Africa Jotam Pasipanodya, Helen McIlleron*, André Burger, Peter A. Wash, Peter Smith, Tawanda Gumbo Pasipanodya J, et al. Submitted.

29. What Was Done All patients hospitalized first 2 months All had 100% adherence first 2 months Drug concentrations measured at 8 time points over 24hrs in month 2 Followed for 2 years, 6% non-adherence Pasipanodya J, et al. Submitted.

30. CART ANALYSIS: Top 3 predictors of Long term outcomes Pasipanodya J, et al. Submitted. 0.7% patients developed ADR in 2 months versus 0.68% we predicted IN THE PAST from modeling and simulations : All ADR had low concentrations of at least one drug

31. Thank you! [Enter Presentation Title in Insert Tab > Header & Footer31

32. Identifying sources of variability Individual variability in blood/air flow with body positions may affect drug distribution and elimination in different parts of the lung http://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf 32

33. Identifying sources of variability Dormant and active bacterial populations may exhibit different effect sizes, even at saturation concentrations http://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf 33

34. Identifying sources of variability Levels of resistance may explain a drug’s varying IC 50 magnitudes http://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf 34

35. Identifying sources of variability Additional factors that induce variability in a defined population? http://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf 35

36. Identifying sources of variability Deeper mechanistic understanding of the disease processes http://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf 36

37. The new CPTR modeling and simulation work group Integrating quantitative systems pharmacology, spanning different stages of the combination drug development process for TB Leveraging previous work to advance existing drug development tools and develop new ones for specific contexts of use Data-driven modeling and simulation tools: data standards and databases from available and relevant studies Spearheading regulatory review pathways with FDA and EMA, to facilitate the applicability of those drug development tools Aligning and cross-fertilizing with other work groups to increase efficiency [Enter Presentation Title in Insert Tab > Header & Footer37