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Quantitative Clinical Pharmacology: Applications of Modeling and Simulation in Clinical Development. Rajesh Krishna, PhD, FCP October 9, 2006 Program in Integrative Information, Computer and Application Sciences (PICASso), Princeton University. Prior Knowledge -Analogues -Disease

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Quantitative clinical pharmacology applications of modeling and simulation in clinical development

Quantitative Clinical Pharmacology:Applications of Modeling and Simulation in Clinical Development

Rajesh Krishna, PhD, FCP

October 9, 2006

Program in Integrative Information, Computer and Application Sciences (PICASso), Princeton University


Prior Knowledge

-Analogues -Disease

-Competitors -Patients

-Discovery/Pre-clinical

Drug Product

-indication

-patients

-formulation

-dose

-safety/ efficacy

Drug Molecule

Clinical Development Plan

Trial 1

Trial 2

Trial …

Trial N

Learn

Learn / Confirm

Confirm

Efficacy

Tox

PK/PD

Mechanistic

Therapeutic Benefit

PPK/PD

Clinical Endpoint

MTD, Efficacy D-R

PK/PD, (P)PK/PD

Biomarker, Surrogate

Typical Drug Development Program


Reality of present day new drug development
Reality of Present Day New Drug Development

  • High NME attrition

    • High failure rate before IND

    • NME IND = NDA <20% of time

    • Reported >50% failure rate in Phase 3 (Carl Peck, CDDS)

    • Decreased NME NDAs despite increased INDs

    • Cost per NME approved estimated at >$800M (Tufts)

Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004 (3):711-715


Probability of Success for New Mechanisms ~11%

Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004 (3):711-715


New Mechanisms Often Fail Because of Lack of Efficacy or Demonstrated Benefit

Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004 (3):711-715


Drivers For Change Demonstrated Benefit

  • Escalating R&D costs despite flat growth

    • R&D expenditure not proportional to number and quality of NMEs

    • Need to  productivity (do more with same amount of investment)

  • Low POS of NME’s entering Phase I - ~11% (Kola and Landis, 2004)

  • Phase II as stage for decision making often the default today

    • High attrition given low POS for new mechanisms without evidence of pharmacological benefit in Phase I

    • R&D costs required to support decision-making for new mechanisms this late, given POS, prohibitive

    • Lack of resources to create tools to facilitate early decision-making

  • Knowledge management and integration

    • New tools to link outcomes, predict hazards, reduce uncertainty in risk/benefit

  • Quick win-quick kill paradigm


FDA Critical Path Initiative Demonstrated Benefit

  • Goals

  • Develop new predictive “tools”

  • Improve the productivity and success of drug development

  • Speed approval of innovative products

Adapted from: http://www.fda.gov/oc/initiatives/criticalpath/


Areas for Change Demonstrated Benefit

  • Key objective:

    • Need to dramatically improve predictions of efficacy and safety in clinical development

  • Enablers:

    • Biomarkers ~ target validation

      • Biomarker qualification, qualifying disease specific biomarkers

    • M&S ~ effective knowledge management leveraging bioinformatics

      • Drug disease models, clinical trial simulation

    • Clinical trials ~ better decision making, improving efficiency

      • Adaptive trial designs, seamless trials


Pulse check terminology

PK Demonstrated Benefit

Mixed Effects Modeling

0.20

0.15

Drug Concentration

0.10

0.05

Safety

Efficacy

0.0

0

1

2

3

4

5

6

Time (h)

Pulse Check - Terminology


Model based drug development
Model Based Drug Development Demonstrated Benefit

  • Hypothesis based drug development emphasizing integrating information and improving the quality of decision making in drug development

    • Preclinical and clinical biomarkers

    • Dose-response and/or PK-PD relationships

    • Mechanistic or empirical disease models

    • Novel clinical trial designs

    • Clinical trial simulations and probabilities of success

    • Baseline-, placebo- and dropout-modified models

    • Outcome models


Roadmap for model based drug development
Roadmap for Model Based Drug Development Demonstrated Benefit

Capture

Prior knowledge

Model

Clinical trial

Simulate

Optimize


Case example 1 meta analysis of statin efficacy
Case Example 1: Meta-Analysis of Statin Efficacy Demonstrated Benefit

  • Accumulated data from 25 trials (~9500 patients)

    • 5 Pfizer sponsored trials for Lipitor

    • 7 AstraZeneca summary basis trials for Crestor

    • 9 Merck summary basis trials for Zetia

    • 4 Pfizer sponsored trials for an investigational non-statin

  • Epidemiology Trials

    • Wilson et al, Framingham risk equations,

    • (Prediction of Coronary Heart Disease Using Risk Factor Analysis, Circulation, 1998, 97:1837-1847)

    • Riker et al, C-Reactive Protein and LDL

    • (Comparison of C-Reactive Protein and Low-Density Lipoprotein Cholesterol Levels in the Prediction of First Cardiovascular Events, NEJM 2002, 347:1557-1565)

Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene

a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 


Pharmacodynamic model development
Pharmacodynamic Model Development Demonstrated Benefit

  • Trials looked at the following alone or in combination with Ezetamibe or gemcabine:

    • Atorvastatin

    • Rosuvastatin

    • Simvastatin

    • Lovastatin

    • Pravastatin

Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene

a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 



Statin dose response relationship absence e 0 and presence of 10 mg ezetimibe e 10
Statin Dose-Response Relationship: Absence (E 0) and Presence of 10 mg Ezetimibe (E 10)

Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene

a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 


Dose response relationship for non statin without and with statin
Dose-Response Relationship for Non-Statin Without and With Statin

With Atorvastatin

Alone

Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene

a New Lipid-Altering Agent. AAPS Journal.  2005; 7(3): E513-E522. 


Prediction of Simvastatin Risk Reduction vs Dose Using a Model Based Approach

Adapted from D. Stanski


Case example 2 gabapentin approval and label
Case Example 2: Gabapentin Approval and Label Model Based Approach

  • Gabapentin was approved by FDA for post-herpetic neuralgia

  • Approved label states under clinical studies: “Pharmacokinetic-pharmacodynamic modeling provided confirmatory evidence of efficacy across all doses”

  • Model and Data Provided with Submission

    • FDA reviewers used model to test various scenarios

    • Supported doses and conclusions of Pfizer

    • Provided confidence to eliminate need for replicate doses

    • FDA proposed language in the label on PK-PD modeling and clinical trials

Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005


Gabapentin PHN Study Designs Model Based Approach

  • Used all daily pain scores

  • Exposure-Response analysis utilized titration data for within-subject dose response

Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005


Gabapentin PHN Data Fits Model Based Approach

Time Dependent Placebo Response, Emax Drug Response and Saturable Absorption

Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005



Case example 3 optimizing dose selection for an ace inhibitor
Case Example 3: Optimizing Dose Selection for an ACE Inhibitor

  • A 2-compartment PK model with first order absorption and first order output

  • Daytime variation of ACE is described with a cosine function with time period tp, amplitude A and shift

    ACE(t)=ACEo+ A cos(2π (t+S)/tp)

  • An Emax model and a sigmoidal Emax model are tested to describe the relationship between concentrations and plasma ACE activity

Adapted from: Pfister. Dose selection of M100240. J Clin Pharmacol 2004 Jun;44(6):621-31


Simulation scenarios
Simulation Scenarios Inhibitor

  • Target:

    • > 90% inhibition of plasma ACE activity in at least 50% of patients

  • Simulations at steady state:

    • For comparison of oral daily doses ranging from 25 to 150 mg

    • PK and plasma ACE activity profiles (n=500) under these dosage regimens are simulated with parameters drawn from the population PK and PD distribution


Model based simulations of bid regimens
Model Based Simulations of BID Regimens Inhibitor

PK

ACE activity

Fraction of patients achieving

target; horizontal lines denote

50 and 80%

24h

24h



Simulations at steady state
Simulations at Steady State Inhibitor

  • Simulations are used to evaluate candidate QD and BID dose regimen to achieve >90% plasma ACE inhibition at 24 hours

  • For comparison of oral daily doses ranging from 25 to 250 mg, PK and plasma ACE activity profiles (n=500) under these dosage regimens are simulated with parameters drawn from the population PK and PD distribution



Case example 4 tipranavir tpv approval and label
Case Example 4: Tipranavir (TPV) Approval and Label Inhibitor

  • Protease inhibitor for experienced patients or patients with viral resistance to other PIs

  • Plasma TPV levels are a major driver of efficacy and toxicity, boosted with ritonavir (RTV)

  • HIV-1 protease mutations represent a major driver of resistance and decreased efficacy

  • 500/200 TPV/RTV dose employed in Phase III

    • Plasma TPV levels > IC50 to suppress viral load and avoid development of resistance

Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005


Inhibitory Quotient (IQ) As A Predictor of Efficacy Inhibitor

  • Protein Binding Correction Factor (PBCF = 3.75x)

    • TPV is highly bound in plasma (99.96 - 99.98%)

    • Cell culture media only contains 6% fetal bovine serum (99.88%)

    • PBCF estimated using 2 methods:

      • Method 1: Equilibrium Dialysis: 0.120% free / 0.034% free = 3.5x

      • Method 2: Addition of 75% human plasma to antiviral assay resulted in a 4x shift

  • IQ = Cmin / (IC50 fold WT ● mean WT HIV IC50● 3.75)

standardized TPV susceptibility

PBCF

susceptibility in patient isolate

PK

Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005


Tpv c min ic 50 t20 parameters and viral response

Control+T20 Inhibitor

Control

TPV Cmin, IC50, T20 Parameters and Viral Response

For IQ ≥ 100, 54% responded to TPV and 73% responded to TPV+T20For IQ < 100, 21% responded to TPV and 52% responded to TPV+T20

Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005


Risk vs benefit impact of iq on 24 week viral load response and c min on liver toxicity

Benefit: Viral Load Change From Baseline (log Inhibitor10)

Risk: Grade 3-4 ALT, AST or Bilirubin

Risk vs. Benefit: Impact of IQ on 24-Week Viral Load Response and Cmin on Liver Toxicity

Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005


Tpv label statements
TPV Label Statements Inhibitor

  • “Among the 206 patients receiving APTIVUS-ritonavir without enfuvirtide…..the response rate was 23% in those with an IQ value < 75 and 55% with an IQ value > 75.”

  • “Among the 95 patients receiving APTIVUS-ritonavir with enfuvirtide, the response rate in patients with an IQ < 75 vs. those with IQ > 75 was 43% and 84% respectively.”

Adapted from: TPV Label, under “Pharmacodynamics”.


Case example 5 drug disease model
Case Example 5: Drug Disease Model Inhibitor

  • Mechanistic disease model for HIV/AIDS

  • Pharmacodynamic model incorporating dose, concentration, HIV viral load time course

  • Biomarkers of efficacy – viral RNA time course

  • Biomarkers of safety – GIT events time course

  • Dose response relationships or PK/PD model

  • Outcome analysis



Viral dynamics
Viral Dynamics Inhibitor

Adapted from: Bonhoeffer (1997) Proc. Natl. Acad. Sci. USA 94, 6971-6976


Drug disease models

p Inhibitor

d2

PI

Active

Infected

l

fAbVT

CD4+ Cells

(N)NRTI

Virus

a

+

fLbVT

Latent

Infected

(N)NRTI

d1

c

d3

Drug Disease Models

l:production rate

of target cell

d1: dying rate of

target cell

c: dying rate of virus

b: infection rate

constant

d2: dying rate of

active cells

d3: dying rate of latent

cells

p: production rate of

virus

Adapted from: J Acquir Immun Defic Syndr 26:397, 2001, FDA EOP2A Slides


Case example 5 applying drug hiv disease model
Case Example 5: Applying Drug HIV Disease Model Inhibitor

  • Maraviroc (MVC;UK-427,857)

  • Novel CCR5 antagonist in development for the treatment of HIV

  • Blocks the CCR5 receptor, which is used by HIV to enter CD4+ cells

  • Simulate decline of HIV-1 RNA plasma levels for 400 patients per treatment arm

  • Dosing regimens simulated were as follows: 150 mg twice daily fed, 150 mg twice daily fasted, and 300 mg once daily fasted

  • HIV-1 RNA measurements were performed daily for 40 days after the start of treatment


Hiv 1 rna log10 time course
HIV-1 RNA Log10 Time Course Inhibitor

Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191


Measured and predicted hiv 1 rna log10
Measured and Predicted HIV-1 RNA log10 Inhibitor

Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191


Measured and model simulated hiv 1 rna log10
Measured and Model Simulated HIV-1 RNA log10 Inhibitor

Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191


Predicted inhibition fraction in function of mean viral load drop
Predicted Inhibition Fraction in Function of Mean Viral Load Drop

Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191


Prior Knowledge Drop

-Analogues -Disease

-Competitors -Patients

-Discovery/Pre-clinical

Drug Molecule

Clinical Development Plan

Trial 1

Trial 2

Trial …

Trial N

Summary: QCP Enabled Drug Development Program

Drug Product

-indication

-patients

-formulation

-dose

-safety/ efficacy

QCP Enablers: Reducing Uncertainty in Risk/Benefit

Drug and Disease Modeling

Dose Response, PK-PD and Dosing

Targeted Label Information Optimal Use

Adaptive Trial Design

“The best way to predict the future is to create it”

– Peter F. Drucker


Acknowledgements
Acknowledgements Drop

  • John Wagner (Merck)

  • Gary Herman (Merck)

  • Marc Pfister (BMS)

  • Joga Gobburu (FDA)


Questions
Questions Drop



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