Jochen Zisowsky, Noelia Nebot, Guenter Heimann
Pharmacometrics Unit, Oncology Clinical Pharmacology, Novartis Pharma AG
Objectives: To develop a statistically sound approach for PK-QT analysis when jointly modeling the impact of two active compounds by developing confidence region for the expected effect at maximum concentration (Cmax) of the two compounds which excludes an effect of 10 msec or larger, corresponding to controlling the type I error for an appropriately defined hypothesis test.
Methods: PK and QT data were obtained from a single arm trial in patients. The primary endpoint was ddQTcF defined as time-matched change from baseline in QTcF. The lme() function in R 3.0.2 was used to develop a model which was linear in both compounds (without/with interaction) and included a fixed effect parameter for the diurnal effect, and a random patient effect. This model can be viewed as an extension of the model proposed by Hosmane et al.[1] for a single agent to the situation with two active drugs.
The effect of Cmax (either from the individual bivariate PK data or at the respective Tmax) was evaluated for each compound by plugging Cmax into the model equation. We used bootstrap to test the null hypothesis whether the expected effect at Cmax would be ≥10 msec. The null hypothesis was rejected if the proportion of bootstrap copies with an estimated effect >10 msec was
Results: PK profiles of the four compounds could be grouped into two pairs with similar PK behavior and the subsequent PK-QT analysis could be simplified to two compounds. The PK-QT analysis revealed competing effects of two compounds on QT. The estimated effect at Cmax was
None of the observed concentration pairs were above the 10 msec line and none of the 95% ellipsoids representing the joint two-dimensional distribution of the pairs of maximum concentrations crossed the 10 msec line.
Conclusions: The developed approach to analyse PK-QT data when two active compounds are present worked well in our data example. The reduction of QT interval by the parent compound and the increase of QT interval by the second compound were nicely reflected in our parameter estimates. This contradicting effect would have shown as hysteresis in a separate PK-QT analysis for each compound, leading to biased and not interpretable results. Our two-dimensional approach nicely overcomes this issue.
References:
[1] Hosmane B, Locke C, Chiu YL. Exposure-Response Modeling Approach for Assessing QT Effect in Thorough QT/QTc Studies. J Biopharm Stat (2010) 20(3): 624-640.
Reference: PAGE 25 (2016) Abstr 5732 [www.page-meeting.org/?abstract=5732]
Poster: Drug/Disease modeling - Safety