A physiologically-based PK/PD model to capture population variability for diabetes research and automatic blood glucose control
S. Schaller (1) (2), S. Willmann (1), L. Schaupp (3), T. Pieber (3), A. Schuppert (1) (2), J. Lippert (1), T. Eissing (1)
(1) Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany, (2) Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany; (3) Department of Internal Medicine, Medical University of Graz, Graz, Austria
Objectives: Realistic in-silico models of the glucose metabolism can provide invaluable information to improve diabetes management and research and the development of automatic control strategies for diabetes. Existing in-silico models (reviewed in ) already provide a powerful tool, e.g.  approved by the FDA for pre-clinical testing, but do not provide the mechanistic and structural detail at molecular and organ levels necessary to integrate heterogeneous data that can drive fundamental research in diabetes. We here present the methods necessary to close this gap.
Methods: A coupled PBPK/PD model of glucose metabolism, including glucagon, was developed on a high level of mechanistic detail using PK-Sim® and MoBi® [3, 4]. Mechanistic models of both insulin receptor dynamics and subcutaneous insulin absorption were integrated to capture variability in insulin action on a molecular level and to accommodate for the application of commercial insulin analogs. A detailed description of the GI-Tract for oral absorption allows the simulation of meal and glucose absorption and can be extended for the study of oral anti-diabetic agents. The established model can be extrapolated to populations, be it adults, children or elderly  to capture the variability of glucose metabolism. The different modules as well as the multi-scale model were parameterized using literature, e.g. , and in-house data and will be further tested in clinical trials.
Results: The model is able to describe different standard scenarios including clamp studies, the response to intravenous and oral glucose tolerance tests as well as complete clinical trials, both, with healthy subjects and subjects with type 1 diabetes. It can be individualized based on physiological data and patient history (e.g. bodyweight or total daily dose of insulin). The high level of mechanistic and structural detail allows to capture some of the intra-individual variability generally compensated by a time-variant correction of insulin sensitivity. The modeling framework allows to generate virtual diabetic populations or individualized models for support in pharma R&D of diabetes and for the evaluation of automatic glucose control within integrated systems .
Conclusion: Overall, the PBPK/PD model provides a powerful basis to the medical scientific, pharmaceutical and device R&D community for the testing and validation of novel diabetes treatment strategies on virtual diabetes populations.
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