A differential simulator using past clinical trial data to run simulated clinical trials

Individuals with type 1 Diabetes Mellitus (T1DM) must inject insulin to regulate blood glucose concentrations. The artificial pancreas project seeks to automate the delivery of insulin in response to continuous glucose monitor (sensor) signals. Most medical devices must go through extensive animal studies before human studies can be conducted, but regulatory authorities (FDA, in the United States) have allowed investigators to skip animal trials for the artificial pancreas project by conducting exhaustive simulation-based (in silico) clinical trials. Still, current simulators only provide a rough evaluation of prospective algorithms because they cannot accurately model all physiologic processes. In this paper we propose an alternative simulation approach that works directly from clinical data, reducing the number of required assumptions. This approach calculates changes to real data based on changes in the inputs rather than calculating the effect of the entire input. Here, we choose a simple, linear, insulin-glucose model based on published insulin-glucose test data. The simulator with the linear insulin glucose model is validated against an FDA-accepted simulator for various magnitudes of modified insulin dosing. The scenarios include a 0-200% step change to the patients' background insulin delivery rates (basal rates), and a 50-150% scaling of their meal insulin doses (boluses). These studies show that the differential simulator induces less error than patient variability when using other simulators. We also show two illustrative test cases for testing revision to artificial pancreas controllers.