RESUMO
Virtual control groups (VCGs) created from historical control data (HCD) can reduce the number of concurrent control group animals needed in regulatory toxicity studies by up to 25%. This study investigates the performance of VCGs on statistical outcomes of body weight development between treatment and control groups in legacy studies. The objective is to reproduce the statistical outcomes of 28-day sub-chronic studies (legacy studies) after replacing the concurrent control group with virtual ones. In rodent toxicity studies initial body weight is used as surrogate for the age of animals. For the assessment of VCG-sampling methods three different approaches are explored: (i) sampling VCGs from the entire HCD ignoring initial body weight information of the legacy study, (ii) sampling from HCD matching the legacy study's initial body weights, and (iii) sampling from HCD with assigned statistical weights derived from legacy study initial body weight information. It is shown that the ability to reproduce statistical outcomes by virtual controls is mainly determined by the congruence between the legacy study and the HCD weight distribution: regardless of the chosen approach, the ability to reproduce statistical outcomes was well for VCGs when the legacy study's initial-body-weight distribution was similar to the HCD's. When the initial body weight range of the legacy study was at the extreme ends of the HCD's distribution, the weighted-sampling approach was superior. This article highlights the importance of proper HCD-matching by the legacy study's initial body weight and discusses required conditions to accurately reproduce body weight development.
Animal control data from past studies performed in a standardized manner can be used to create virtual control groups (VCGs) to use in new studies instead of control animals. This approach can reduce the number of study animals by up to 25%. This study assesses the performance of VCGs selected by body weight in rat studies. The objective was to reproduce the original study results as closely as possible after replacing the original control group values with VCGs from a pool of historical control values. Several methods for selecting control animal data to create VCGs were compared. Among these, assigning statistical weights to the sampling pool yielded the best performance. Ideally the body weight distributions on day 1 of the study should be similar between the VCG and the original study animals. This article shows that proper selection VCGs can yield reliable study data with fewer animals.
RESUMO
Virtual control groups (VCGs) in nonclinical toxicity represent the concept of using appropriate historical control data for replacing concurrent control group animals. Historical control data collected from standardized studies can serve as base for constructing VCGs and legacy study reports can be used as a benchmark to evaluate the VCG performance. Replacing concurrent controls of legacy studies with VCGs should ideally reproduce the results of these studies. Based on three four-week rat oral toxicity legacy studies with varying degrees of toxicity findings we developed a concept to evaluate VCG performance on different levels: the ability of VCGs to (i) reproduce statistically significant deviations from the concurrent control, (ii) reproduce test substance-related effects, and (iii) reproduce the conclusion of the toxicity study in terms of threshold dose, target organs, toxicological biomarkers (clinical pathology) and reversibility. Although VCGs have shown a low to moderate ability to reproduce statistical results, the general study conclusions remained unchanged. Our results provide a first indication that carefully selected historical control data can be used to replace concurrent control without impairing the general study conclusion. Additionally, the developed procedures and workflows lay the foundation for the future validation of virtual controls for a use in regulatory toxicology.