Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database

Virtual Control Groups (VCGs) based on Historical Control Data (HCD) in preclinical toxicity testing have the potential to reduce animal usage. As a case study we retrospectively analyzed the impact of replacing Concurrent Control Groups (CCGs) with VCGs on the treatment-relatedness of 28 selected histopathological findings reported in either rat or dog in the eTOX database. We developed a novel methodology whereby statistical predictions of treatment-relatedness using either CCGs or VCGs of varying covariate similarity to CCGs were compared to designations from original toxicologist reports; and changes in agreement were used to quantify changes in study outcomes. Generally, the best agreement was achieved when CCGs were replaced with VCGs with the highest level of similarity; the same species, strain, sex, administration route, and vehicle. For example, balanced ac-curacies for rat findings were 0.704 (predictions based on CCGs) vs. 0.702 (predictions based on VCGs). More-over, we identified covariates which resulted in poorer identification of treatment-relatedness. This was related to an increasing incidence rate divergence in HCD relative to CCGs. Future databases which collect data at the individual animal level including study details such as animal age and testing facility are required to build adequate VCGs to accurately identify treatment-related effects.

Automated summary

Virtual Control Groups (VCGs) based on Historical Control Data (HCD) have the potential to reduce animal usage in preclinical toxicity testing. Replacing Concurrent Control Groups (CCGs) with VCGs can improve the consistency of study outcomes. However, some covariates may affect the identification of treatment-relatedness.