A Human Glucoregulatory Model for the Rational Design and Evaluation of Diabetes Therapeutics

Computational glucoregulatory models that incorporate biochemical signaling and mechanisms have shown utility for preclinical evaluation and even the rational design of such interventions, potentially reducing the need for animal experimentation. In this work, we expand upon our IM³PACT 2.0 computational framework by applying it to human clinical data and demonstrate its utility for translational prediction of glucose-responsive glucagon (GRG) therapeutic performance.

The model was parameterized separately for healthy and type 1 diabetes (T1D) humans using a data-fitting procedure applied to clinical insulin and glucagon injection datasets. The resulting healthy model reproduces the damped glucose recovery clinically observed following insulin administration, characteristic of coupled insulin–glucagon regulation. Sensitivity analysis was performed to identify parameters critical to describing glucose responses in the injection datasets, and a species comparison was performed that benchmarked human parameters against our previously validated rat model.