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Modeling and Simulation beyond PK/PD

Modeling and Simulation beyond PK/PD. CPTR Workshop October 2 – 4, 2012 Pentagon City. Mission and Goals. M&S-WG Objective : For Preclinical Phases: Deliver Quantitative PKPD models to help sponsors select therapeutic combinations

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Modeling and Simulation beyond PK/PD

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

  2. Mission and Goals 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

  3. Developed TB modeling inventory • Develop drug-disease-trial model for TB • White papers • FDA qualification • Data standards • Data sources • Database • Hollow Fiber model • SIMCYP Grant Application (CPTR+U of F) • Pulmosim tool from Pfizer

  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 & Footer

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

  6. PBPK 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 PharmacolToxicol. 2009;49:291-301.

  8. Trial Simulations Optimize Design Based on Quantitative Principles Test Multiple Replications of Trial Design Assumptions Drug/Disease Model Range of Outcomes 60 50 40 Trial Simulations Optimize Design Based on Quantitative Principles 30 20 10 CFU 0 0.4 0.5 0.6 0.7 0.8 Analytics/Statistics Effect of Dose and Number of Subjects on Power to Estimate Significant Effect of Drug vs Placebo • Trial Designs • X possible doses • Different N • Sampling time • Inclusion criteria 1 mg 2 mg 5 mg 10 mg 20 mg N 30 4.5 6.5 18 48.5 73.5 40 13 29 76 87 91 50 27.5 52 85 95 99 60 40.5 62 90 97 100 70 55.5 71 94 99 100 Modify Design

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

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

  11. Intermediate Approach: Mechanistically-Inspired 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 Retains key mechanistic verifiable components, allows for parameter estimations and is fit for simulation purposes

  12. Leverage can be Obtained From Other Areas 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 Predator-Prey models in viral infections such as with HCV may provide useful insights for TB modeling and simulation

  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 & Footer

  14. Regulatory Review Process: What’s success? Consultation and Advise Process • 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 Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36 INH inhibitory sigmoid Emax 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.AgentsChemother. 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

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

  24. ORACLES AND DEVINING THE FUTURE • PREDICTION • PREDICT: • Etymology via 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 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. External Validation of Model: Sputum Conversion Rates in 10,000 Patients Sputum conversion rate predicted = 56% of patients Sputum conversion rate from prospective clinical studies in WC= 51-63% 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 JotamPasipanodya, 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 • 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 Pasipanodya J, et al. Submitted.

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

  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

  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

  34. Identifying sources of variability Levels of resistance may explain a drug’s varying IC50 magnitudes http://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf

  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

  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

  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 & Footer

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