IHI VICT3R: Developing and Implementing Virtual Control Groups to Reduce Animal Use in Toxicology Research.

Area of Science:

• Pharmacology and Toxicology
• Computational Biology
• Regulatory Science

Background:

• The virtual control group (VCG) concept repurposes historical animal toxicity data to reduce concurrent controls in studies.
• Previous work established the feasibility of VCGs, but significant collaborative efforts are needed for implementation.
• Regulatory acceptance of VCGs remains a key challenge.

Purpose of the Study:

• To address challenges in VCG implementation and gain regulatory acceptance.
• To build a high-quality, standardized database of control animal data from toxicity studies.
• To develop workflows and computational tools for generating robust VCGs.

Main Methods:

• Compiling and standardizing historical and future control animal data into a comprehensive database.
• Developing statistical and artificial intelligence (AI) approaches for generating virtual control animals.
• Assessing VCG validity, reproducibility, and robustness by comparing them against real control groups.

Main Results:

• The VICT3R project is establishing a robust database for VCG generation.
• Workflows and AI tools are being developed to create statistically sound VCGs.
• Validation studies are underway to compare VCG performance against traditional control groups.

Conclusions:

• The VICT3R project is advancing the VCG concept towards practical application and regulatory acceptance.
• This initiative aims to significantly reduce animal use in toxicity testing.
• The project will provide validated tools and data for generating reliable virtual control groups.