PK-PD Modeling with the QTc: Is it possible to avoid a TQT Study?

Paul A. Frohna, MD, PhD, PharmD

Biotechnology ConsultantFrohna Biotech Consulting

www.frohnabiotechconsulting.com

Part 1. Understanding the FDA’s Perspective

BiomarkerBiomarker

Drug

keo

kin

kout

H

H

CP CE

Pharmacokinetics Pharmacodynamics

Pharmacokinetics and Pharmacodynamics: Combining drug levels with biomarkers

(DDQTc)

Biomarker (DQTc)

This is an inexact science since not all drugs that cause an increase in QTc lead to Torsades de Pointe and sudden cardiac death, which is the real

reason to care about QTc in drug development.

R

Response(TdP)

FDA View on Model-based Drug Development and QTc Assessment

“Model-based drug development is a priority for the Critical Path Initiative. I believe it is the future of drug development”

“We need to move from empirical evaluations to model-based, learn-confirm cycles to enhance the predictive capacity of the drug development process”

Janet Woodcock, M.D.Director, Center for Drug Evaluation and Research

“Regulatory review of QT study is not complete without an assessment of concentration-QTc relationship”

Norman Stockbridge, M.D., Ph.D.Director, Cardio-Renal Drug Products

Head, Interdisciplinary Review Team for QT

Generating the Right QTc Data Package for Your Drug

FDA Interdisciplinary Review Team (IRT) for QT Studies

Provide standardization forum for study designs

Quantitative Outcomes and Values– Concentration-Response required in all TQT studies– High rate of false positives when utilizing only dose-

response data– CR is an important tool with additional statistical

power to characterize QTc effects of a drug when you’re unable to conduct a TQT study

• Anti-cancer compounds—too toxic for healthy subjects and at supratherapeutic doses, plus don’t want to use placebo in cancer patients

FDA Analysis of Sponsor’s TQT Study Data (Florian et al, JCP, 2011;51:1152-1162)

FDA Hypothesis: A better understanding of how study design elements are likely to affect drug concentrations and the corresponding concentration–QTc relationship can be useful in designing future TQT studies.

Objective: Determine what study design variables and patient covariates affect the Conc-QTc relationship of Moxi (positive control)

Data: Several (20) TQT studies submitted to the FDA using Moxi with plasma concentration data and ΔΔQTcF

Methods: Pooling and analyzing several (20) TQT studies to build pop PK model and conc-QTc model

ΔΔQTcF vs Time Plots for the 20 Pooled TQT Studies by Sex and Race

Sex: Male (dotted) and Female (solid)

Race: Caucasian (solid), Black (dotted) and Asian (dot-dash)

Florian et al., JCP, 2011;51:1152-1162

FDA’s Current Thinking…

As more concentration-response QTc data are collected and submitted to the FDA, along with sophisticated PK-PD modeling, the FDA is mulling over the possibility that thorough QTc studies may not be required in the future.

When is that future…not sure, but the future IS coming!