Circadian rhythm in pharmacodynamics and its influence on the identification of treatment effects
Teun Post(1), Tomoo Funaki(2), Hiromi Maune(2), Henk-Jan Drenth(1)
1 LAP&P Consultants BV, Leiden, The Netherlands, 2 Otsuka Pharmaceutical Co., Ltd., Japan
Objectives: Compound X is a aquaretic compound, being developed for the treatment of a specific kidney disease. Population PK-PD models were developed on phase II data to support dose selection for upcoming phase III studies. The treatment effects were evaluated based on the paramaters urine osmolarity (OSM) and urine volume. For the derived pharmacodynamic models, inclusion of a circadian rhythm was required to describe a non-stationary baseline with substantial variability. The objective is to demonstrate the quantification of treatment effects on pharmacodynamic parameters under influence of a circadian rhythm.
Methods: Phase II study data were available for 18 subjects, which were randomised to two treatment arms, comprising of three periods each. In the first period all subjects received a single dose of 15 mg and in the second period a single dose of 30 mg. In the third period, subjects in one arm received a 15 mg bid and in the other arm 30 mg qd dosing for 5 days. Full PK profiles were available for the first and second period and for days 1 and 5 in the third period. Urine osmolarity and urine volume were measured before, during and after treatment for all periods. As the cumulative urine volume (CUV) can be derived by integreating the urinary production rate (UPR) over time, a model was developed which was able to describe the effect on both UPR and CUV in one comprehensive model with a circadian rhythm implemented on the UPR. The concentration-response relationship of compound X on OSM and UPR-CUV was modelled using turnover models with a circadian rhythm on baseline.
Results: The population PK model adequately described the observed PK, as shown by a visual predictive check and a bootstrap analysis. Inclusion of circadian processes in the PK-PD model for OSM resulted in the identification of a clear Emax exposure-response relationship. In contrast, the EC50 could not be precisely identified for the UPR-CUV model due to remaining residual variability not explained by a circadian rhythm. Nevertheless, both models showed an adequate perfomance in a visual predictive check.
Conclusions: Implementation of circadian rhythm was needed to distinguish the treatment effect from the non-stationary baseline response for OSM. This shows the importance of addressing circadian rhythm when quantifying treatment effects. The developed PK-PD model for OSM was used to support selection of appropriate dosage regimens in upcoming phase III studies.