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并发症的负面影响，糖尿病已成为医疗健康领域的重大挑战。计算机模拟平台，特别是以UVA/Padova 1型糖尿病模拟器为代表，通过虚拟环境中模拟患者反应，已成为推进糖尿病治疗的有力工具。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，其虚拟受试者规模庞大，包含6062个个体。方法：本研究提出了基于分布的亚群选择（DSPS）方法，这是一种系统性地识别能够模拟真实临床试验中观察到的临床行为的虚拟亚群的方法。该方法将亚群选择任务转化为线性规划问题，从而能够识别出最具代表性的最大虚拟队列。该选择过程围绕糖尿病研究中的关键临床结局指标，如糖化血红蛋白（HbA1c）和空腹血浆葡萄糖（FPG），确保所选虚拟亚群的统计特性（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。该方法用于选择一个虚拟受试者亚群，该亚群在26周期间内的多个关键指标上，包括血糖疗效、胰岛素剂量和累积低血糖事件，均与临床试验数据高度吻合。结论：DSPS算法能够在UVLab中选择虚拟亚群，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人群模拟平台与既往已完成的临床试验之间的差距。