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Virtual Control Groups: A Data-Driven Novel Method
VCGs can change the way safety assessment studies are designed
Animal candidates are selected from curated historical data to create a Virtual Control Group (VCG) based on study parameters to partially reduce the use of Concurrent Control Group (CCG) animals when feasible. Data science models and domain expertise are leveraged to statistically and biologically qualify and analyze VCG data when used on a CCG-targeted study.
Leading the Pursuit of Alternative Methods Innovation
While VCGs are an established concept in clinical trials, using virtual data sets in place of animals aren’t in nonclinical regulatory studies. VCGs provide unique opportunities for scientifically responsible drug development through new designs in nonclinical studies and provide immense value to drug developers looking to reduce animal use while maintaining study quality.
With the depth and breadth our client base we are uniquely positioned to fuse the science of toxicology with machine learning for a powerful alternative.
Journey of Exploration and Learning with Virtual Control Groups
Although the concept of VCGs is in its early stages, significant progress has been made in this area implementing such an innovative method will require thorough data collection, curation, and statistical evaluation paired with a validation strategy to maintain safety and scientific study outcome reliability.
Continued efforts to further develop and qualify VCGs will be crucial for the advancement of animal welfare and the conduct of nonclinical studies.
Driving Innovation Through VCGs
Join Charles River’s Laura Lotfi and Guillemette Duchateau-Nguyen from Roche for a discussion on the replacement of mouse model control groups with a virtual equivalent – and what that would mean for drug development in terms of cost, time, and animal model reduction.
Listen to the Podcast
Contact one of our experts to learn more about how you can implement VCGs in your nonclinical studies.