O. Lillin, W. Comisar, J. Stone, D. Tatosian, R. de Greef
MSD
Objectives: Population C-QTc analysis is useful tool to leverage QTc data from Phase I studies. We present the results of three Ph1 C-QTc analyses and discuss 1) the impact on the drug development trajectories of each of these compounds and 2) what type of ECG data are required for a reliable estimate of the drug effect.
Methods: Mixed effects models with circadian rhythm as covariate on the intercept QTc and direct linear drug effect[4] (Drug A and B) and a linear mixed effects model (Drug C) were developed based on concentrations and QTc data from Phase 1 studies. Three examples of novel compound candidates are included.
Results:
All compounds were weak to moderate hERG inhibitors.
Drug A: establish safety margin of therapeutic window, predict TQT, negotiate ECG strategy in Ph2/3
- – Ph1 C-QTc modeling predicted high likelihood of <10 ms QTc prolongation at doses ≤ 3.5-fold the therapeutic dose
- – The predicted QTc prolongation (mean and upper CI) at supra-therapeutic dose was 10 (14) ms and the TQT was predicted to be positive; the largest dQTc in the TQT was 12 (14) ms
- – PK-QTc modeling informed early the necessity to plan for appropriate ECG monitoring in Phase 2/3 and helped negotiate less stringent monitoring
Drug B: aid in design of TQT, negotiate ECG strategy for Ph3
- – Ph1 C-QTc predicted <2 msec QTc prolongation at Ph III doses, with a predicted QTc change of <10 ms occurring at a 4.5 to 5.5-fold margin over clinical exposures
- – The predicted QTc prolongation at supra-therapeutic dose was 4.5 (6.8) ms and the TQT is predicted to be negative (ongoing).
- – Modeling permitted earlier Ph3 start prior to conduct of TQT study without the need for extensive ECG measurements in Ph3.
Drug C: de-risking TQT
- – A Ph1 C-QTc analysis predicted no drug effect on QTc changes (drug effect slope was not significantly different from zero), which enabled more rapid development with deferral of TQT to be in parallel to Phase IIB.
The TQT was negative.
Conclusions: Population C-QTc models were successfully fitted to the data of Phase 1 trials for three compounds. The Ph1 C-QTc analyses were predictive of the TQT for Drugs A and C; study is ongoing for B. These early results were useful not only for internal decision making but also in the dialog with regulators.
References:
[1] J. Z. Peng et Al, Model-based QTc interval risk assessment in First-in-Man (FIM) studies for rapid decision making: A case example with a novel compound candidate, AAPs AM (2010), Abstr. T3441
[2] S Rohatagi et al, Is a Thorough QTc study Necessary? The Role of Modeling and Simulation in Evaluating the QTc Prolongation Potential of Drugs, J. Clin. Pharmacol. 2009; 49;1284
[3] C Garnett et al, Concentration-QT Relationships Play a Key Role in the Evaluation of Proarrhythmic Risk During Regulatory Review, J. Clin. Pharmacol. 2008; 48; 13
[4] V Piotrovsky et al, Pharmacokinetic-Pharmacodynamic Modeling in the Data Analysis and Interpretation of Drug-induced QT/QTc Prolongation, The AAPS Journal 2005; 7 (3) Article 63
[5] A Chain et al, Can First-Time-In-Human Trials Replace Thorough QT Studies?, PAGE 20 (2011), Abstr. 2172
Reference: PAGE 22 (2013) Abstr 2961 [www.page-meeting.org/?abstract=2961]
Poster: Safety (e.g. QT prolongation)