Generating T1DM virtual patients for in silico clinical trials via AI-guided statistical model checking

The availability of a representative population of virtual patients, i.e., a population large enough to represent all relevant human patient behaviours, is a key enabler for the design of In Silico Clinical Trials (ISCTs), that is trials following simulation-based approaches for the safety and efficacy assessment of pharmacological treatments and biomedical devices. This involves the development of Virtual Physiological Human (VPH) models able to represent the whole phenotypes spectrum of the human physiology of interest. Usually, such models are open-loop models, i.e., their behaviour depends also on exogenous inputs (such as, e.g., pharmacological drugs). In this paper, we propose a methodology to convert an open-loop VPH model into a closed-loop model. As a case study, we apply our methodology to a state-of-the-art VPH model defining the human glucose regulation system of individuals with Type 1 Diabetes Mellitus (T1DM), and show how we generate a representative population of T1DM virtual patients.