Background and Objective: Diabetes presents a significant challenge to healthcare due to the negative impact of poor blood sugar control on health and associated complications. Computer simulation platforms, notably exemplified by the UVA/Padova Type 1 Diabetes simulator, has emerged as a promising tool for advancing diabetes treatments by simulating patient responses in a virtual environment. The UVA Virtual Lab (UVLab) is a new simulation platform to mimic the metabolic behavior of people with Type 2 diabetes (T2D) with a large population of 6062 virtual subjects. Methods: The work introduces the Distribution-Based Population Selection (DSPS) method, a systematic approach to identifying virtual subsets that mimic the clinical behavior observed in real trials. The method transforms the sub-population selection task into a Linear Programing problem, enabling the identification of the largest representative virtual cohort. This selection process centers on key clinical outcomes in diabetes research, such as HbA1c and Fasting plasma Glucose (FPG), ensuring that the statistical properties (moments) of the selected virtual sub-population closely resemble those observed in real-word clinical trial. Results: DSPS method was applied to the insulin degludec (IDeg) arm of a phase 3 clinical trial. This method was used to select a sub-population of virtual subjects that closely mirrored the clinical trial data across multiple key metrics, including glycemic efficacy, insulin dosages, and cumulative hypoglycemia events over a 26-week period. Conclusion: The DSPS algorithm is able to select virtual sub-population within UVLab to reproduce and predict the outcomes of a clinical trial. This statistical method can bridge the gap between large population simulation platforms and previously conducted clinical trials.

翻译：背景与目的：由于血糖控制不佳对健康及其相关并发症的负面影响，糖尿病对医疗保健构成了重大挑战。计算机模拟平台，特别是以UVA/Padova 1型糖尿病模拟器为代表，已成为通过在虚拟环境中模拟患者反应来推进糖尿病治疗的有前景的工具。UVA虚拟实验室（UVLab）是一个新的模拟平台，旨在模拟2型糖尿病（T2D）患者的代谢行为，其虚拟受试者群体规模庞大，包含6062个个体。方法：本研究引入了基于分布的群体选择（DSPS）方法，这是一种系统性的方法，用于识别能够模拟真实临床试验中观察到的临床行为的虚拟子集。该方法将子群体选择任务转化为一个线性规划问题，从而能够识别出最大规模的具有代表性的虚拟队列。该选择过程围绕糖尿病研究中的关键临床结局指标展开，例如糖化血红蛋白（HbA1c）和空腹血浆葡萄糖（FPG），确保所选虚拟子群体的统计特性（矩）与真实世界临床试验中观察到的特性高度相似。结果：将DSPS方法应用于一项3期临床试验的德谷胰岛素（IDeg）组。使用该方法选择了一个虚拟受试者子群体，该子群体在多个关键指标上（包括26周内的血糖疗效、胰岛素剂量和累积低血糖事件）与临床试验数据高度吻合。结论：DSPS算法能够在UVLab中选择虚拟子群体，以复现和预测临床试验的结果。这种统计方法可以弥合大规模群体模拟平台与既往已完成的临床试验之间的差距。