Prieto Garcia L. (1,2), Lundahl A. (3), Ahlström C. (2), Vildhede A. (2), Lennernäs H. (1), Sjögren E. (1)
(1) Department of Pharmacy, Uppsala University, SE-75123, Uppsala, Sweden; (2) DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden; (3) CPQP, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
Objectives:The use of complex whole-body physiologically-based pharmacokinetic (PBPK) models in drug discovery and development is facilitated by the development of several software platforms such as SimCyp® (SimCyp, Sheffield, UK), PK-Sim® and MoBi® (Bayer Technology Services, Leverkusen, Germany) among others. These commercial PBPK modelling platforms provide a generic model structure for the physiology of predefined species and populations. However, understanding on platform differences and potential implications for the output and further application is still lacking. Platform differences may involve model structure and parametrization but also built-in data base information and technical functionalities. In practice this may not only affect the final model output and how model development and evaluations are performed but more importantly the outcome from the application and decisions made upon it. The aim of this work was:
- to assess how PBPK model development and model output may be affected by choice of PBPK platform.
- to provide a comprehensive PBPK model for simvastatin lactone and acid in PK-Sim® and SimCyp® platforms to predict the implications of enzyme- and transporter-mediated drug-drug interactions (DDI).
Methods: PK-Sim® and SimCyp® were used in this study as representatives of established PBPK platforms. The hypolipidemic agent simvastatin was chosen as the test compound. Simvastatin is suitable due to a large number of available clinical data and that the wide range of processes involved in simvastatin disposition allows for the assessment of several aspects in the platforms. The importance of some of these processes has been shown in clinical DDI and pharmacogenetics studies [1], such as 1) metabolism via CYP3A4 and CYP2C8; 2) transporter-mediated disposition in the gut and liver via BCRP and OATP1B1; 3) formation of an active metabolite (simvastatin acid) via esterase enzymes, which is a reversible process. The simvastatin model development in both software used the same starting knowledge and data (in vitro and in vivo) and aimed to describe these key processes of simvastatin.
Results: A simvastatin PBPK model was successfully developed in both platforms. There was good agreement between the predicted simvastatin (lactone and acid) exposure and the observed clinical data at the dose range 20-60 mg. The PK parameters, Cmax and area under the concentration-time curve (AUC), were predicted within 2-fold error and plasma-concentration profiles were recovered within the predicted 95% CI (confidence interval). The models were also validated against the clinical pharmacogenetic studies for BCRP and OATP1B transporters and were able to capture the observed difference in plasma profiles for the different genotypes, predicting the respective Cmax and AUC within 2-fold of the observed clinical data for each genotype. In addition, clinical DDIs with strong and moderate CYP3A4 inhibitors and the OATP1B1 inhibitor, gemfibrozil, were successfully predicted with 2-fold error and no bias. Collectively, the pharmacogenetic and DDI validations demonstrate the predictive capability of the models in both platforms when simvastatin (lactone and acid) is used as a probe substrate of CYP3A4, BCRP and OATP1B1. During model development, differences between the two platforms were identified on structure, processes, functionalities and flexibility which leaded us to adapt different modelling strategy in each platform. Therefore, the final input parameters differed, but the final model output was comparable. The strong and weak aspects of the two platforms were also assed.
Conclusions: This shows that in-depth knowledge is needed when using established PBPK platforms so to enable an assessment of what consequences application of the specific PBPK platform may bring. Specifically, this work provides insights on software differences and its implications to bridge the PBPK knowledge from the different platform users: Simcyp and PK-Sim. Finally, it offers a Simvastatin model in both platforms for potential future applications on simvastatin PD and safety assessment of metabolism- and transporter-mediated DDI risks.
References:
[1] Elsby et al. Clin Pharmacol Ther (2012) 92 (5) 584–598
Reference: PAGE () Abstr 9465 [www.page-meeting.org/?abstract=9465]
Poster: Drug/Disease Modelling - Absorption & PBPK