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Alternative methods to the use of animals in pharmaceutical testing are becoming more used and accepted. Laura Lotfi of Charles River breaks down the basics of virtual control groups in drug development.
Guest Host: Siegfried Schmitt, PhD, vice president, Technical at Parexel
Guest Speaker: Laura Lotfi, Associate Director, Product Management, Digital Projects at Charles River
At the end of March 2026, the European Medicines Agency (EMA) issued a draft qualification opinion1 for a preclinical research methodology to reduce the use of animals in specific dose-range finding studies.2 The new approach method (NAM) would replace standard animal control groups with virtual groups, and once qualified, would allow EMA to accept evidence generated from virtual control groups in medicine approval applications.
In the United States, FDA has been making similar moves to limit the use of animals in drug development. In April 2025, the agency published its Roadmap to Reducing Animal Testing in to modernize drug development through human-relevant science.3 The core of the initiative is the advancement of NAMs, which include advanced in vitro systems (such as organ-on-a-chip), computational modeling, and artificial intelligence (AI). In the first year of the initiative, the FDA draft guidance to reduce or eliminate 6-month primate studies for monoclonal antibodies. The agency also qualified its first AI-based drug development tool, AIM-NASH, and updated guidance to support a transition away from horseshoe crab-derived testing, a move that could spare over one million animals annually.4 To provide regulatory certainty, the FDA launched a searchable database documenting exactly where alternative methods are currently acceptable. Furthermore, the agency has prioritized international alignment, collaborating with global regulators to ensure these new strategies are accepted worldwide.
Virtual control groups are a NAM that can be used to support initiatives to reduce the use of animals in drug development and regulation. According to EMA, characterized control data identify appropriate virtual comparator animals to create virtual control groups that are then used to conduct testing that would be traditional performed on animals.2
Laura Lotfi, associate director, Product Management, Digital Projects at Charles River, says that in addition to replacing, reducing, and refining the use of animals, virtual control groups may reduce study timelines and bring more “statistical power”. However, there are limitations to their use, including the reliability of the data.
“The data are highly dependent on the quality, the completeness of the underlying infrastructure that may contain sometimes biases or gaps that can affect the reliability of those results.
They may also be less suitable for certain scenarios,” Lotfi explains. “For example, there are some studies that involve new biological mechanism or rare endpoints, maybe some species that are not very well understood, where the limited historical data are not, quietly there yet, so there is a lack of sufficient historical data that are available. In those cases, traditional approaches may still be necessary.”
In the video above, Lotfi provides an explanation of how virtual control groups are used in drug development, their limitations, and the cost associated with their implementation.
Laura Lotfi, associate director, Product Management, Digital Projects at Charles River. Laura is a toxicologist that specializes in digital transformation within drug discovery and safety. She focuses on integrating advanced data platforms and innovative methodologies, such as virtual control groups.
Siegfried Schmitt, PhD, is vice president, Technical at Parexel and a member of PharmTech’s Editorial Advisory Board.
EMA Opinion Paper
FDA Guidance Documents and Websites
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