For decades, concurrent control groups have served as a foundational element of nonclinical drug development. Yet the model is under pressure as scientific, regulatory and ethical expectations evolve. In this conversation, Laura Lotfi of Charles River makes the case for virtual control groups (VCGs) as a next-generation approach—one that replaces newly generated control data with rigorously matched historical datasets.
The shift is not theoretical. Advances in data infrastructure, machine learning and standardized datasets have made VCGs increasingly viable. Validation studies, including retrospective analyses of completed trials, show strong alignment in outcomes when VCGs are applied in the right contexts. At the same time, the model addresses long-standing inefficiencies: reducing the need to generate redundant data, lowering resource demands and helping mitigate ethical concerns tied to animal use.
Regulatory signals are reinforcing this momentum. Policies such as the FDA Modernization Act 2.0, global 3Rs initiatives and the European Medicines Agency’s draft qualification opinion on virtual control groups are encouraging scientifically justified alternatives, even as formal guidance continues to evolve. For forward-looking organizations, the imperative is shifting from exploration to execution—building data readiness, piloting targeted use cases and engaging regulators early. As Lotfi suggests, the opportunity is not just adoption, but leadership in defining how VCGs become part of the industry standard.