Sylvain Fouliard and Marylore Chenel
Clinical Pharmacokinetics and Pharmacometrics Department, Institut de Recherches Internationales Servier, France
Introduction: Drug-related QT interval prolongation assessment is essential in a drug development program. In the specific context of oncology, QT studies are harder to perform, and an accurate description of the PKPD relationship between drug concentration and corrected QT (QTc) may be performed by analysing electrocardiogram (ECG) data collected in early clinical trials [1]. However, the constraints of phase I/II studies induce limitations in the flexibility of administration and measurement schedules.
Objectives: This work aims at proposing a PKPD model-based ECG sampling schedule strategy for QTc prolongation detection in early clinical studies and to assess a potential effect of an anticancer drug on QTc.
Methods: First a putative PKPD model was built for study design evaluation, combining a preliminary population PK model of drug S together with a population PD model describing the circadian variation of Fridericia-corrected QTc developed using placebo data from two former QT studies and a linear drug effect on QTc, assuming these models would be predictive of the outcome of the PKPD relationship. A large range of values of the unknown drug effect on QTc was investigated as an input in optimal design software POPDES [2] in order to evaluate the ability to assess a PKPD relationship using a given study design [3]. After the completion of the study, ECG data were analysed using a sequential population PKPD approach. Model evaluation was performed through goodness-of-fit plots and NPDEs.
Results: The proposed ECG sampling design was expected to lead to a good precision of estimation of the model parameters, with expected relative standard errors (RSEs) being less than 30%. Drug S concentration-time profiles were described with a 2-compartment model with a first order absorption and first order elimination from central compartment. The circadian variation of QTc was modelled as the sum of three sine and cosine terms and drug effect was proportional to concentration. RSEs of fixed effect parameters was not higher than 30% and parameter values were close to the anticipated ones.
Conclusions: This work proposes a modelling and simulation based strategy in order to assess QTc prolongation risk in the context of clinical trials in oncology. Although the early assumptions made on PKPD relationship are not negligible and are to be confirmed, we show in this example an accurate quantification of a QTc prolongation with a feasible ECG recording design in oncology patients.
References:
[1] Piotrovsky, V. “Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation”. AAPS J 7.3 (2005): E609-E624.
[2] Ogungbenro K, Dokoumetzidis A, Aarons L. “Application of optimal design methodologies in clinical pharmacology experiments”. Pharm Stat. (2009) Jul-Sep;8(3):239-52.
[3] Fouliard S. and Chenel M. Optimal design and QT-prolongation detection in oncology studies. PAGE 19 (2010) Abstr 1697 [www.page-meeting.org/?abstract=1697].
Reference: PAGE 23 (2014) Abstr 3052 [www.page-meeting.org/?abstract=3052]
Poster: Drug/Disease modeling - Oncology