Quantitative clinical pharmacology applications of modeling and simulation in clinical development
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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

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

  • 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

  • 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

  • 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

  • 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


PK

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

  • 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

Capture

Prior knowledge

Model

Clinical trial

Simulate

Optimize


Case Example 1: Meta-Analysis of Statin Efficacy

  • 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

  • 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. 


Dose vs LDL lowering Response: Population, Mixed Effects Response Meta-Analysis:


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

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

  • 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

  • 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

Time Dependent Placebo Response, Emax Drug Response and Saturable Absorption

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


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


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

  • 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

PK

ACE activity

Fraction of patients achieving

target; horizontal lines denote

50 and 80%

24h

24h


Model Based Simulations of TID Regimens


Simulations at Steady State

  • 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


Fraction of Patients Achieving 90% ACE Inhibition at Trough


Case Example 4: Tipranavir (TPV) Approval and Label

  • 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

  • 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


Control+T20

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


Benefit: Viral Load Change From Baseline (log10)

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

  • “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

  • 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


Components of Drug Disease Models


Viral Dynamics

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


p

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

  • 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

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


Measured and Predicted HIV-1 RNA log10

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


Measured and Model Simulated HIV-1 RNA log10

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


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

-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

  • John Wagner (Merck)

  • Gary Herman (Merck)

  • Marc Pfister (BMS)

  • Joga Gobburu (FDA)


Questions


Modeling and Simulation in Drug Development


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