pharmacometrics pk/pd modeling - iasct home subramanian.pdf · non-linear mixed-effects modeling...

Pharmacometrics – PK/PD Modeling

Use of Qualitative Methods and Quantitative Pharmacology to Support Drug Development

Outline of the Presentation

Introduction

PK-PD modeling

Applications in various disease settings

– Oncology

– Cardiovascular: QT Prolongation

– Diabetes/Metabolic disorders

Summary and Conclusions

Proposition of the Usual Dose (Concentration)

Paracelsus

(1493-1541)

“The dose makes the poison”

DOSE PK/PD

VARIABILITY

Drug Discovery and Development

Non-teratogenic

Patentable

1 Drug Molecule

10,000 Drug Candidates

Targeted

Selective

Potent

Physically stable

Soluble

Permeable

Metabolically stable

Non-inducing

Reversible

Durable

Non-mutagenic

Manufacturable

Carcinogenicity studies

Competitive profile

1 Drug Launch

10 Drug Molecules

All discovery criteria met

Safe and active in lab and animal models

Formulation

Stability

Trial sites and investigators

Patient recruitment

Dosing range

Side effect profile

Efficacy

Long term safety

Cost-effective manufacturing

Regulatory filing

Approximately 10–15 years, $0.8 – 1.4B, from idea to marketable drug!

Confidential

Model Based Drug Development

High development costs and low success rates in bringing new

drugs to market is a “growing crisis” as described by the FDA (1)

Model-based drug development (MBDD) identified as an important

tool to ease this crisis

MBDD is a mathematical and statistical approach that constructs

validates and utilizes drug exposure-response models, and

pharmacometric models along with disease models, to facilitate

drug development (2)

1. US. Food and Drug Administration. Challenge and opportunity on the critical path to new medical products. http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html (2004).

2. D. R. Stanski. Model-based drug development: a critical path opportunity. http://www.fda.gov/oc/initiatives/criticalpath/stanski/stanski.html (2004).

http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html

http://www.fda.gov/oc/initiatives/criticalpath/stanski/stanski.html

Modeling and Simulation in Drug Discovery and Development

Discovery Preclinical

development PoC trials

Confirmatory trials

• Structure & ligand

based drug design

• Protein structure

modeling

• Docking

• QSAR

• Virtual screening

• In silico ADMET

modeling…

• Scaling models: Animal to

human

• Compartmental modeling

• Biomarker identification

• Exposure-response /

Biomarker / Tox models

• Identification of patient

population

Dosing selection

• Disease Progression

models

• Clinical trial simulations

Compound selection

Learn… & Confirm


Introduction

PK-PD modeling


– Oncology




Pharmacokinetics (PK)

Compartmental Analysis

A model is fit (for e.g., one-

compartmental, two-compartmental,…)

and PK parameters (Ka, CL, V, …)

estimated

Population analysis and influence of

subject covariates

Two-stage approach

Non-linear mixed-effects modeling

approach

Gain understanding of the PK of the

drug, understand influence of patient

covariates on PK, explore necessity for

dose adjustments, simulate clinical

trials for different scenarios

Pharmacokinetic Data Analysis

Non-compartmental Analysis

Derived from plasma PK

concentration data without using

any modeling techniques

Subject wise estimates Cmax,

tmax, AUCs, t1/2 can be obtained

Gain understanding of the PK of

the drug

Pharmacodynamic Models

Phamacodynamics (PD)

Linear model: E = A * Conc + B; Log-linear model: E = A * log(Conc) + B; Logistic model: ln(p/1-p) = A * Conc + B; (p: probability of an effect E) Emax/Sigmoid Emax model:

PK/PD Models

Direct Response Models

Indirect Response Models

Observed effect determined

by effect site concentration

without (Direct link) or with

(Indirect link) a time lag

Mechanism based – observed effect

may be secondary to a previous

time consuming synthesis or

degradation of an endogenous

substance

Derendorf H., Meibohm B. Modeling of PK/PD Relationships. Pharmaceutical Research (1999), 16:176-184.


Introduction

PK-PD models


– Oncology




MBDD: Oncology

Approach

Problem: Drug-Drug interaction potential

Targeted anti-cancer agent

Population PK modeling of data pooled

from clinical studies with and without

chemotherapy

Influence of covariates including chemo

administration on PK parameters

Comparison with pre-clinical results

First population pharmacokinetic modeling

study for a molecule discovered and being

developed in India

Subramanian J, Damre A, Rohatagi S. Submitted to Indian J. Cancer 2012

Targeted agent in oncology

Pharmacokinetic (PK) analysis of data combined from

Phase I and Phase I/II trials

Two compartmental model

Effect of covariates on PK parameters

No statistically significant influence of

combination agents on CL and V1

detected up to a 0.01 significance level

MBDD: Oncology


Central compartment (Volume of distribution: V1)

Peripheral compartment (Volume of distribution V2)

Clearance: CL Inter-compartmental clearance: Q

Drug

Drug-Drug Interactions (DDI)

One-way ANOVA to compare the effect of

combination agents on dose normalized Cmax

also not statistically significant (p > 0.05)

Results supported by findings from pre-clinical

DDI experiments

Model Evaluation using bootstrap and predictive check

Parameter estimates stable

Variability in the original data well

reproduced by the model

Model useful for simulations of later

phase trials of the targeted agent


MBDD: Oncology


Introduction

PK-PD models


– Oncology




MBDD: Cardiovascular (QT Prolongation)

Approach Problem: Is a thorough QTc (TQT) study necessary?

TQT studies expected as part of a NDA

Concentration-QT (C-QT) modeling using

data pooled from single and multiple

ascending-dose (SAD/MAD) studies

Results of predicted QTc prolongation

compared to available thorough QTc (TQT)

study results, to see whether the C-QT

model could establish the QTc prolongation

relationship without the TQT results.

Rohatagi et. al. J Clin Pharm 2010


Process for pooled concentration – response modeling

Rohatagi et. al J Clin Pharm 2010

Equations in the Model



Comparison of correction methods using baseline data



Negative slope predicted and observed



Allows earlier and more in-depth understanding of the C-QT relationship

Serves as a guide for further developmental decisions for the drug

Should we conduct a TQT study?

When to conduct the TQT study?

Is the drug candidate worth pursuing?


Advantages of a pooled C-QT analyses of data from early clinical studies


QT prolongation studies in oncology

Many oncologic drugs including reported to prolong the QT interval

A thorough QT (TQT) study may not be feasible in early oncologic trials because of

lack of positive control or placebo in these trials

In a clinical setting, an oncologic compound considered likely to have a QT effect if

any of the following criteria are met1:

≥ 10ms change from baseline in QTcF by central tendency analysis or by estimated Cmax

analysis using an exposure-response model

> 60ms change from baseline QTcF in >15% of patients;

New absolute QTcF > 500ms in > 5% of patients

1 Morganroth J, Shah RR and Scott JW. Evaluation and Management of Cardiac Safety Using the Electrocardiogram in

Oncology Clinical Trials: Focus on Cardiac Repolarization (QTc Interval). Clin Pharmacol Ther. 2010 87(2):166-74.

Development of an exposure-response (C-QT) model very important to

understand the QT prolongation effect of anti-cancer compounds!


Introduction

PK-PD models


– Oncology




MBDD: Diabetes/Metabolic Disorders

Problem: Dose selection and optimization of clinical trial design

Approach

Healthy volunteers and Diabetic patients data from Phase I and II

Biomarker models to determine max phase II dose

Two-compartmental PK model

Indirect Response Models for PD end-points

Effect of subject characteristics Model Based Development of a PPARy Agonist, Rivoglitazone, to Aid Dose Selection and Optimize Clinical Trial Designs. . S.Rohatagi et al. Journal of Clinical Pharmacology 48: 1420-1429 2008.

Phase I

Study in HV, adiponectin increase was

saturable at 5 mg QD x 2wks

Phase II

Adiponectin response correlated with PD

response providing further qualification

for its use as biomarker

Adiponectin, a protein hormone, modulates glucose and lipid metabolism, biomarker for PPARg activity

0 2 4 6 8 10

0

2

4

6

8

10

0 2 4 6 8 10

Dose (mg)

0

2

4

6

8

10

Fold

Incre

ase o

ver

Baselin

e

E0 = 0.86

Emax = 6.2

ED50 = 1.9

Response was flat after 5 mg dose; Hence 5 mg was the max dose chosen for Phase IIa study


Model Based Development of a PPARy Agonist, Rivoglitazone, to Aid Dose Selection and Optimize Clinical Trial Designs. S.Rohatagi et al. Journal of Clinical Pharmacology 48: 1420-1429 2008.

Rivoglitazone PK modeled using two-compartmental model

Semi-mechanistic models described the effects of predicted Rivoglitazone

concentrations (Cp) on FPG, HbA1c, and plasma volume (1/Hb)

• PK Model: Two-compartmental model. CL significantly affected by gender, body weight, patient/healthy volunteer status and renal function

• Cp-FPG Model: Changes in FPG concentrations modeled as a function of Cp via an indirect-effect model. Cp reduces FPG by increasing the plasma glucose removal rate. [Benincosa-Jusko model for PPARγ agonists]

• FPG-HbA1c: Changes in HbA1c modeled as secondary to changes in FPG in a first order process

• Cp-Plasma Volume: Rivoglitazone Cp increase plasma volume by decreasing the plasma volume removal rate. A linear relationship with Cp found for inhibition

Model Based Development of a PPARy Agonist, Rivoglitazone, to Aid Dose Selection and Optimize Clinical Trial Designs. S.Rohatagi et al, Journal of Clinical Pharmacology 48: 1420-1429 2008.


Incidence of edema was modeled as a function of hemodilution

Subjects with larger drops in hemoglobin concentrations (i.e., hemodilution)

had an increased risk of edema

Model Based Development of a PPARy Agonist, Rivoglitazone, to Aid Dose Selection and Optimize Clinical Trial Designs. S.Rohatagi et alJournal of Clinical Pharmacology 48: 1420-1429 2008.


Most importantly…


Quantitative understanding of dose-exposure-biomarker relationships and

impact of subject characteristics on these relationships

Need for dose adjustment, impact of trial designs

Results of all analyses communicated regularly at all phases of the

program, enabling continuous improvements of models and testing a

variety of “what-if” scenarios with respect to study populations and designs

Model Based Development of a PPARy Agonist, Rivoglitazone, to Aid Dose Selection and Optimize Clinical Trial Designs. S.Rohatagi et al, Journal of Clinical Pharmacology 48: 1420-1429 2008.


Introduction

PK-PD models


– Oncology





What should we do?

Make modeling the basis of developing drugs

When to start?

Start early and transfer PK-PD knowledge from discovery to development; refine

model as more data become available

What types of models?

PK/PD

Disease models

Animal/human correlations

Basically integration of knowledge

Optimal use of PK-PD modeling and simulation => fewer failed compounds,

fewer study failures and smaller number of studies needed for registration

Save time and money!

Model Based Drug Development

Thank You

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