Transcript PRESENTATION - FINAL - Critical Path to TB Drug Regimens

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
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
CPTR M&S
Projects
PBPK
• SIMCYP Grant
Application
(CPTR+U of F)
• Pulmosim tool
from Pfizer
Clinical trial
simulation
tools
• Developed TB
modeling inventory
• Develop drugdisease-trial model
for TB
• White papers
• FDA qualification
Preclinical
PKPD models
• Data
standards
• Data
sources
• Hollow
Fiber
model
• Database
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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
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PBPK
PulmoSim: Framework for inhaled drugs that can serve as a foundation for
orally administered antibiotics systemically distributed to the lungs
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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.
Trial Simulations Optimize Design Based on
Quantitative Principles
Test Multiple Replications of
Trial Design Assumptions
Drug/Disease Model
Trial Designs
•X possible doses
•Different N
•Sampling time
•Inclusion criteria
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Trial Simulations
Optimize Design
Based on
Quantitative
Principles
0
CFU
30
40
50
60
Range of Outcomes
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
N
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
Modify Design
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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
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
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Regulatory Review Process: What’s success?
Informal discussion with FDA/EMA.
Consultation
and
Advise Process
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.
Success!!!
Regulatory decision qualifying
or endorsing the submitted
tools
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Modeling and Simulation
beyond PK/PD
CPTR Workshop October 2 – 4, 2012
Pentagon 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
Concentration (mg/L)
1.5
1.0
0.5
0.0
0
6
12
18
24
30
Time (hours)
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42
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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. 41:26702679) 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
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
http://www.crystalinks.com/delphi.html
QUANTITATIVE:
Predict extent and values prior to the event
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.
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.
• 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
•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.
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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