Diabetes presents a significant challenge to healthcare due to the short- and long-term complications associated with poor blood sugar control. Computer simulation platforms have emerged as promising tools for advancing diabetes therapy by simulating patient responses to treatments in a virtual environment. The University of Virginia Virtual Lab (UVLab) is a new simulation platform engineered to mimic the metabolic behavior of individuals with type 2 diabetes (T2D) using a mathematical model of glucose homeostasis in T2D and a large population of 6062 virtual subjects. This work proposes a statistical method - the Distribution-based sub-population selection (DSPS) method - for selecting subsets of virtual subjects from this large initial pool, ensuring that the selected group possesses the desired characteristics necessary to reproduce and predict the outcomes of a clinical trial. DSPS formulates the sub-population selection as a linear programming problem, identifying the largest virtual cohort to closely resemble the statistical properties (moments) of key outcomes from real-world clinical trials. The method was applied to the insulin degludec arm of a 26-week phase 3 clinical trial, evaluating the efficacy and safety of insulin degludec and liraglutide combination therapy. DSPS selected a sub-population that mirrored clinical trial data across key metrics, including glycemic efficacy, insulin dosages, and cumulative hypoglycemia events, with a relative sum of square errors of 0.33 and a percentage error of 1.07 %. This approach bridges the gap between large population simulation platforms and clinical trials, enabling the selection of virtual sub-populations with specific properties required for targeted studies.