TY - JOUR
KW - 3Rs
KW - Advanced analytics
KW - Animal Testing Alternatives
KW - Drug development
KW - New approach methods (NAMs)
KW - Nonclinical
KW - Reduction
KW - Refinement
KW - replacement
KW - Safety assessment
KW - Toxicology
AU - Laura Lotfi
AU - T. William O'Neill
AU - Angela Wilcox
AU - Steven Bulera
AB - Animal models are crucial in biomedical research, particularly in pharmaceutical discovery and safety testing. Recent legislative updates and regulatory shifts, such as the FDA Modernization Act 2.0 and European Parliament Resolution 2021/2784 (RSP), have opened new avenues for innovative nonclinical research. Among these innovations is the use of Virtual Control Groups (VCGs), a method which can significantly reduce animal usage and improve experimental designs. VCGs utilize historical control data and machine learning algorithms to replicate traditional control groups' statistical power and address preanalytical and analytical variations. We applied VCG data retrospectively to 20 pilot studies. The VCG and concurrent control group (CCG) data were compared to assess statistical alignment as well as biological relevance. The findings highlight the effectiveness of VCGs in maintaining toxicological study integrity and underscore the importance of specific selection criteria in ensuring accurate toxicological outcomes. This work supports the potential of VCGs in advancing the 3Rs and improving nonclinical research methodologies.
BT - Regulatory Toxicology and Pharmacology
DA - 2026-06-01
DO - 10.1016/j.yrtph.2026.106077
N2 - Animal models are crucial in biomedical research, particularly in pharmaceutical discovery and safety testing. Recent legislative updates and regulatory shifts, such as the FDA Modernization Act 2.0 and European Parliament Resolution 2021/2784 (RSP), have opened new avenues for innovative nonclinical research. Among these innovations is the use of Virtual Control Groups (VCGs), a method which can significantly reduce animal usage and improve experimental designs. VCGs utilize historical control data and machine learning algorithms to replicate traditional control groups' statistical power and address preanalytical and analytical variations. We applied VCG data retrospectively to 20 pilot studies. The VCG and concurrent control group (CCG) data were compared to assess statistical alignment as well as biological relevance. The findings highlight the effectiveness of VCGs in maintaining toxicological study integrity and underscore the importance of specific selection criteria in ensuring accurate toxicological outcomes. This work supports the potential of VCGs in advancing the 3Rs and improving nonclinical research methodologies.
PY - 2026
EP - 106077
ST - Virtual control groups in nonclinical research
T2 - Regulatory Toxicology and Pharmacology
TI - Virtual control groups in nonclinical research: A Re-analysis of 20 studies using virtual control group data
UR - https://www.sciencedirect.com/science/article/pii/S0273230026000504
VL - 168
Y2 - 2026-03-03
SN - 0273-2300
ER -