Dr. Paul Frohna, a biotech consultant with expertise in translational medicine and clinical pharmacology, presents an overview of the FDA's evolving perspectives on the QTc issue and the stand alone thorough QTc study.

1. PK-PD Modeling with the QTc:
Is it possible to avoid a TQT Study?
Part 1. Understanding the FDA’s Perspective
Paul A. Frohna, MD, PhD, PharmD
Biotechnology Consultant
Frohna Biotech Consulting
www.frohnabiotechconsulting.com

2. Pharmacokinetics and Pharmacodynamics:
Combining drug levels with biomarkers (DDQTc)
Drug
kin
H
CP keo CE Biomarker
H Biomarker
kout
(DQTc) Response
(TdP)
Pharmacokinetics Pharmacodynamics
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.

3. 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

4. Generating the Right QTc Data
Package for Your Drug

5. 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

6. 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

7. 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

8. 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!