GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.

Minimizing the use of animals as concurrent controls in in vivo studies directly supports the 3Rs principles of Replacement, Reduction, and Refinement. Current virtual control group (VCG) approaches primarily rely on historical control data, but their utility may be limited by cross-study variability from differences in study design, laboratory practices, and data heterogeneity. We propose GanCtrl, a generative AI approach to infer study-specific control data directly from time-matched treatment data. By generating synthetic controls analogous to concurrent controls, GanCtrl aims to mitigate biological, temporal, and technical biases inherent in VCG approaches based on historical control data. GanCtrl was applied to rat repeat-dose toxicity studies to simulate 38 clinical pathology endpoints under control conditions using corresponding treatment data. Synthetic controls closely approximated real concurrent controls, with differences smaller than intra- and inter-laboratory baseline variation and comparable to biological replicate variability, while also preserving the typical magnitude and distribution of control responses across studies. Importantly, synthetic controls enabled detection of drug-induced clinical pathology signals and maintained biologically relevant endpoint relationships, such as ALT-AST. For practical utility, toxicity assessments using GanCtrl-derived synthetic controls were compared with those using real concurrent controls and benchmarked against VCGs constructed from single-laboratory or combined multi-laboratory historical data. Although both approaches performed comparably in the single-laboratory setting, GanCtrl outperformed VCGs when data from multiple laboratories were combined. These findings suggest that GanCtrl offers a potential approach for generating VCGs that may reduce the use of concurrent control animals and advance the 3Rs.