2026-10-14T03:25:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33922717 2026-09-18T07:24:54Z category_4 category_13 category_19 category_873 category_734 category_931 category_106 portal_63 item_type_3 month_year_09_2026

A Human Glucoregulatory Model for the Rational Design and Evaluation of Diabetes Therapeutics Marco Machado (650693) Sungyun Yang (11591620) Ali A. Alizadehmojarad (5895983) Michael S. Strano (1278936) Biochemistry Genetics Pharmacology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy type 1 diabetes simulated test injections novel diabetes therapeutics incorporate biochemical signaling including doses outside identify parameters critical fitting procedure applied computational glucoregulatory models based composite microneedle 0 computational framework benchmarked human parameters parameterized framework constitutes loading design space responsive glucagon therapeutics translational therapeutic effect induced t1d rats 3 </ sup human glucoregulatory model describing glucose responses glucagon injection datasets human clinical data responsive glucagon injection datasets human model translational prediction therapeutic performance parameterized separately clinical insulin rational design subsequently developed species comparison silico platform sensitivity analysis previously evaluated potentially reducing pact 2 morbidity worldwide model performance leading cause fitted dataset expand upon evaluated using animal experimentation Diabetes mellitus remains a leading cause of morbidity worldwide, motivating the development of novel 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. Model performance was evaluated using a series of simulated test injections, including doses outside the fitted dataset. This human model was then used to simulate the translational therapeutic effect of a hydrogel-based composite microneedle (cMN) patch GRG, previously evaluated in streptozotocin-induced T1D rats. A generalized parametric GRG design framework was subsequently developed, mapping the kinetic and drug-loading design space that yields effective therapeutic performance in T1D humans. The parameterized framework constitutes a validated in silico platform for the preclinical evaluation and rational design of glucose-responsive glucagon therapeutics in humans. 2026-09-18T00:00:00Z Dataset Dataset 10.1021/acsptsci.6c00356.s001 https://figshare.com/articles/dataset/A_Human_Glucoregulatory_Model_for_the_Rational_Design_and_Evaluation_of_Diabetes_Therapeutics/33922717 CC BY-NC 4.0