A Computational Glucoregulatory Model of Liver and Glucagon for the Evaluation of Therapeutics

Computational models of the glucoregulatory system constitute a powerful tool for preclinical evaluation and mechanistic insight into therapeutics. However, in the case of diabetes, there is a dearth of physiological models capable of accurately describing the hormone glucagon, which is important for the study and design of new classes of therapeutics, such as glucose-responsive glucagon (GRG). In this work, we construct a physiological compartment model, IMPACT 2.0, which integrates a refined liver submodel and explicit whole-body glucagon kinetics. Key mechanistic enhancements include glucose transporter dynamics, receptor binding, and hepatic glycogen metabolism, allowing for the improved prediction of glucose excursions in response to both insulin- and glucagon-based therapeutics. Model validation against experimental data from healthy and diabetic rats demonstrated accurate glucose predictions following insulin and glucagon administration. Sensitivity analysis was used to evaluate our model's identifiability in the case of insulin or glucagon subcutaneous injections. By comparing diabetic and healthy model fits, we found that 16 of the 37 fitting parameters were significantly different between the health states. Additionally, we applied IMPACT 2.0 to evaluate a recently developed GRG based on controlled release via a microneedle patch, illustrating its utility in mechanistic drug design and bridging in vitro characterization with physiological outcomes. By offering a physiologically detailed and validated framework for glucagon and liver metabolism, IMPACT 2.0 is an improved pharmacokinetic and pharmacodynamic model that will be valuable for accelerating drug discovery, optimizing GRG formulations, and informing the design of closed-loop insulin and glucagon therapeutics.