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Modeling and Simulation Modeling and Simulation

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Modeling and Simulation - PPT Presentation

beyond PKPD CPTR Workshop October 2 4 2012 Pentagon City MampSWG Objective For Preclinical Phases Deliver Quantitative PKPD models to help sponsors select therapeutic combinations ID: 340125

model drug effect amp drug model amp effect modeling clinical simulation trial variability quantitative patients design disease models rates

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Slide1

Modeling and Simulation beyond PK/PD

CPTR Workshop October 2 – 4, 2012

Pentagon CitySlide2

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 GoalsSlide3

SIMCYP Grant Application (CPTR+U of F)

Pulmosim

tool from PfizerDeveloped TB modeling inventoryDevelop drug-disease-trial model for TB White papersFDA qualificationData standards

Data sources

Database

3

Hollow Fiber modelSlide4

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 timeIts parameters represent blood flows, pulmonary ventilation rate, organ volumes etc., for which information is reliable known[Enter Presentation Title in Insert Tab > Header & Footer4Slide5

PBPK Integrates the

Complex

Process of DistributionNormal lung tissue[Enter Presentation Title in Insert Tab > Header & Footer5

Inflamed lung tissue

Granulomatous tissue

CPTRSlide6

PBPK

6

PulmoSim: Framework for inhaled drugs that can serve as a foundation for orally administered antibiotics systemically distributed to the lungsSlide7

Clinical Trial Simulation Tools

Integrate the disease with pharmacology models

Takes into account design considerationsGobburu JV, Lesko LJ. Annu Rev Pharmacol Toxicol. 2009;49:291-301.Slide8

8

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

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 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

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 PrinciplesSlide9

For Predictions

the Top-Down Approach is

Too LimitingDescribes existing data, lacks mechanistic insights, limited to explore new scenarios.Davies GR, et al. Antimicrob Agents Chemother. 2006;50(9):3154-6.Slide10

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;166:1951-67Slide11

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. 2011:280:50-62Slide12

Leverage can be Obtained F

rom

Other AreasPredator-Prey models in viral infections such as with HCV may provide useful insights for TB modeling and simulationGuedj 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;267:330-40Slide13

The Path F

orward

to a Successful M&S Platform in TBObtain the right datasets to model the dynamics of CFU as a function of drug exposure/dose and disease progression in a mechanistically-inspired settingLongitudinal dataDifferent combination therapiesDrug susceptible, MDR and XDR strain dataDevelop model that is predictive of CFU and linked to outcome taking into account appropriate other factors as co-therapy, demographics etcTest and validate the model(s) with regulatory buy-inDevelop 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 & Footer13Slide14

Regulatory Review Process: What’s success?

Consultation and

Advise Process

14

Regulatory decision qualifying or endorsing the submitted tools

Success!!!Slide15

Modeling and Simulation beyond PK/PD

CPTR Workshop October 2 – 4, 2012

Pentagon CitySlide16

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 MODELINGSlide17

M. tuberculosis

in the hollow fiber system

Gumbo T

, et al. (2006)

J Infect Dis

2006;195:194-201

Slide18

HFS:

Moxifloxacin

Concentration-Time ProfileSlide19

HFS, Simulations

and

Predictions Later on “Validated with CLINICAL Data” Efflux pump & cessation of effect of antibioticsThe rapid emergence of quinolone resistanceThe potency & ADR of Cipro/Orflox versus MoxiThe “biphasic” effect of quinolonesThe exact dose of Rifampin associated with optimal effectThe population PK variability hypothesis, and the rates of ADR arising during DOTSThe role of higher doses of pyrazinamide The “breakpoints” that define drug resistanceSlide20

The HFS in Quantitative

P

redictionHFS 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 performanceSlide21

Gumbo T, et al.

Antimicrob

. Agents. Chemotherapy. 2007: 51:2329-36

Slide22

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, ChennaiM. tuberculosis MICs in clinical isolatesPopulation PK data from (Antimicrob.Agents Chemother. 41:2670-2679) input into the subroutine PRIOR of the ADAPT II9,999 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

Slide23

PK-PD PREDICTED vs

OBSERVED EBA IN CLINICAL TRIALS

Gumbo T, et al. Antimicrob. Agents. Chemotherapy. 2007: 51:2329-36 Slide24

PREDICTION

PREDICT:Etymology

 via 

Latin

:

præ

-, "before"

dicere, "to say".“PREDICT” to say BEFORE

QUALITATIVE: Predict an event in terms of whether it occursQUANTITATIVE: Predict

extent and values prior to the event

ORACLES AND DEVINING THE FUTURE

http://www.crystalinks.com/delphi.htmlSlide25

If MDR-TB

Does

N

ot

A

rise

F

rom

P

oor

C

ompliance, W

hy

D

oes

I

t

?

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

.

Slide26

Sputum conversion rate predicted = 56% of patients

Sputum conversion rate from prospective clinical studies in WC= 51-63%

External

Validation

of

Model

:

S

putum

C

onversion

R

ates

in

10,000

Patients

Srivastava S, et al. J. Infect. Dis. 2011; 204:1951-9

.

Slide27

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

.

Slide28

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.Slide29

What

W

as 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.Slide30

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 drugSlide31

Thank you!

[Enter Presentation Title in Insert Tab > Header & Footer

31Slide32

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.pdf32Slide33

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.pdf33Slide34

Identifying sources of variability

Levels of resistance may explain a drug’s varying IC

50 magnitudeshttp://www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf34Slide35

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.pdf35Slide36

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.pdf36Slide37

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 useData-driven modeling and simulation tools: data standards and databases from available and relevant studiesSpearheading regulatory review pathways with FDA and EMA, to facilitate the applicability of those drug development toolsAligning and cross-fertilizing with other work groups to increase efficiency[Enter Presentation Title in Insert Tab > Header & Footer37