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Modeling and Simulation beyond PK/PDCPTR Workshop October 2 – 4, 2012Pentagon City
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
CPTR M&S Projects
PBPKClinical trial simulation
toolsPreclinical
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
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• Hollow Fiber model
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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
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PBPK Integrates the Complex Process of Distribution
• Normal lung tissue
• Inflamed lung tissue
• Granulomatous tissue
• CPTR
6
PBPK
PulmoSim: Framework for inhaled drugs that can serve as a foundation for orally administered antibiotics systemically distributed to the lungs
Clinical Trial Simulation Tools
Integrate the disease with pharmacology modelsTakes into account design considerations
Gobburu JV, Lesko LJ. Annu Rev Pharmacol Toxicol. 2009;49:291-301.
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Trial Simulations Optimize Design Based on Quantitative Principles
Test Multiple Replications of Trial Design Assumptions
Modify Design
0.4 0.5 0.6 0.7 0.8
010
2030
4050
60
Effect of Dose and Number of Subjects on Power toEstimate Significant Effect of Drug vs Placebo
1 mg 2 mg 5 mg 10 mg 20 mg
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
errorerrorerrorDoseDose
Pharmaco-kinetics
Pharmaco-Pharmaco-kineticskinetics
ParametersN (m, cv%)
ParametersParametersN (m, cv%)N (m, cv%)
Drug Effect Drug Effect
Placebo Effect Placebo Effect Placebo Effect
Disease Disease Disease
PainPainScoreScore
RemedicationRemedicationTimeTime
Observed DrugObserved DrugConcentrationConcentration
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
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.
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
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
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
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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
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
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Regulatory decision qualifying or endorsing the submitted tools
Success!!!
Modeling and Simulation beyond PK/PDCPTR Workshop October 2 – 4, 2012Pentagon City
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
M. tuberculosis in the hollow fiber system
Gumbo T, et al. (2006) J Infect Dis 2006;195:194-201
HFS: Moxifloxacin Concentration-Time Profile
0.0
0.5
1.0
1.5
0 6 12 18 24 30 36 42 48
Time (hours)
Conce
ntrat
ion (m
g/L
)
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
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
Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36
ISONIAZID HFS: Monte Carlo Simulations
• 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.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
PK-PD PREDICTED vs OBSERVED EBA IN CLINICAL TRIALS
Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36
PREDICTIONPREDICT: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
ORACLES AND DEVINING THE FUTURE
http://www.crystalinks.com/delphi.html
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.
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.
• 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.
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.
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.
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
Thank you!
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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
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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
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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
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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
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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
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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
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