Circadian quantitative system pharmacology model to inform clinical study design and translation to human
Francois Gaudreault, Cheng Chang and Arthur Simen.
Pfizer Word-Wide Research and Development, Cambridge, MA, USA.
Objectives: A system pharmacology model of the circadian clock based on data from rodents has been developed [1] to enable predictions of the effects of light and pharmacological manipulation (dose and dosing time) on circadian rhythms using clock gene expression as a biomarker. Here, we compare prospective projections from the model with observed human clinical data to illustrate the application of system modelling as a translational tool informing early clinical trial design and pharmacology of a compound with a novel mechanism of action.
Methods: Clock gene data were obtained from a Phase 1, double blind, placebo and active comparator controlled multiple ascending dose safety study involving approximately 96 healthy volunteers dosed either in the morning (AM) or evening (PM). Daily variations (Days 0, 7, 14) in peripheral blood mRNA levels [0, 4, 8 (AM), 12, 16, 20 (PM) and 24 h] of periodic genes were fitted to a 24-h cosinor model using non-linear mixed-effects modelling implemented in R V3.0.1 [2]. Periodicity was assessed using a Lomb-Scargle analysis [3] using a p-value
Results: The 24-h cosinor model with treatment effects on both phase and amplitude was shown to adequately describe the human data. Among the periodic genes (CLOCK, CRY1/2, NR1D1 and PER3), PER3 showed the largest drug effect at steady-state (by Day 7), with dose-responsive phase delays of approximately 7.45 +/ 0.86 h (AM) and 15.37 +/- 1.21 h (PM) at the top dose. These results are consistent with the system model predictions [1], suggesting that the compound pharmacologically induced phase delay, with a more robust response when administered in the evening.
Conclusions: Quantitative system pharmacology is a valuable approach to comprehensively elucidate, validate and test new pharmacological concepts for the development of novel drugs. This case study illustrates how integrated knowledge of both pre-clinical and clinical data informed optimal trial design while improving confidence in the targeted pharmacology.
References:
[1] Kim JK et al. Modeling and validating chronic pharmacological manicpulation of circadian rhythms. CPT Pharmacometrics and Syst Pharmacol (2013) Jul 17; 2:e57.
[2] R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
[3] Ruf, T. (1999) The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series. Biol. Rhythm Res. 30: 178-201