Virtual Control Groups and Bayesian Statistics in Preclinical Drug Safety Assessment

Music City Center 

Utilization of data from historical control animals to form virtual control groups (VCGs) is an innovative approach to embody the 3Rs (reduce, refine, and replace use of control animals) principle in research. However, there is no available systematic evaluation of statistical performance using VCGs in preclinical safety assessment. The optimal selection criteria and combination of VCGs and concurrent control group (CCG) also remain unclear. The VICT3R consortium on VCG controls sponsored by the Innovative Health Initiative (IHI) is currently working to fill in these gaps and refine the implementation of VCGs. This study evaluated the statistical ability as measured by sensitivity and specificity to detect test article effects for body weight and clinical pathology endpoints retrospectively in Pfizer's large animal toxicity studies using VCGs. In addition, exploratory analyses using Bayesian statistics (meta-analytic priors and hierarchical models) were conducted for Developmental and Reproductive Toxicology (DART) and Safety Pharmacology endpoints. Our results show that both VCGs and Bayesian statistics can preserve or improve the sensitivity of detecting toxicologically relevant effects.