Virtual control groups (VCGs) are rapidly emerging as one of the most promising New Approach Methodologies (NAMs) in nonclinical drug development. Once viewed primarily as a theoretical application of historical control data, VCGs are now demonstrating their ability to support toxicology studies while maintaining the scientific rigor required for sound safety assessment and regulatory decision-making.
Recently published research from Charles River as well as from the IHI VICT3R consortium show that VCGs can reproduce the same safety conclusions obtained through traditional concurrent control groups across a range of toxicology study designs. As regulatory agencies continue to encourage innovative approaches, VCGs are gaining acceptance as a practical and scientifically grounded tool for the future of nonclinical research.
However, broader adoption will depend on more than promising study results. Clinical pathology and histopathology will be essential to ensuring that the historical data underpinning VCGs are sufficiently reliable, consistent, and comprehensive to support robust safety assessments.
VCG methodology
VCGs leverage high-quality historical control data, curated from past studies, and combine it with advanced statistical methods and machine learning to generate a matched virtual control group. These datasets are carefully constructed to align with the specific conditions of a given study based on established critical attributes such as species, strain, age, sex, dosing regimen, environmental conditions, vehicle, and analytical conditions and instruments.
This type of data curation requires a comprehensive and centralized database along with robust data infrastructure that allows curation of extensive and complex data sets. In addition, data science pipelines that include clustering, statistics, and expert validation along with machine learning algorithms are used to define and validate matching criteria.
VCG and clinical pathology
Reliable virtual control groups depend on rigorous data curation and standardization, particularly for clinical pathology endpoints. These measurements can vary with numerous factors, including animal age, sex, body weight, genetic background, fasting status, sample collection timing and handling, analytical instrumentation, reagent lots, and laboratory-specific practices. Even subtle differences in these variables can affect test results and their interpretation.
To ensure that virtual controls are suitable comparators, these factors must be consistently documented and considered during group selection. Robust data analysis pipelines are also needed to assess how individual parameters influence clinical pathology measurements and to refine the criteria used to match historical controls to the study conditions.
VCG and digital pathology
Unlike clinical pathology, which generates quantitative measurements, histopathology has traditionally relied on pathologists examining glass slides and assessing tissue changes, often using semi-quantitative scoring systems. This makes it more challenging to standardize assessments and build the large, searchable historical datasets needed for virtual control groups. The answer to these barriers lies in digital pathology and whole slide imaging (WSI).
WSI converts glass slides into high-resolution digital images that can be stored, reviewed remotely, analyzed, and integrated into searchable databases. These digital archives can be maintained and expanded over time, creating a resource that researchers can draw on across studies, institutions, and time periods. This enables the development of historical pathology repositories comparable to those already available for clinical pathology and other data.
AI-assisted image analysis could further strengthen these resources by helping quantify pathological features such as inflammation, degeneration, fibrosis, necrosis, and cellular proliferation. By reducing variability associated with subjective interpretation, these technologies can improve consistency across studies and institutions. This approach doesn’t replace the pathologist but rather serves as a tool to augment their analysis and interpretation.
Building the data infrastructure
Wider adoption of VCGs will depend as much on data infrastructure as on scientific methodology. For clinical pathology, this means harmonized metadata standards, robust historical control databases, and statistical frameworks capable of accounting for temporal drift caused by changing animal colonies, diets, procedures, and instrument platforms.
For anatomic pathology, key investments include validated slide scanners, WSI platforms, and scalable image storage systems. Validated digital pathology workflows and AI-assisted analysis tools will also be important for generating consistent, reliable data.
Regulatory agencies have signaled increasing openness toward alternative methodologies, but widespread acceptance of VCGs will require robust validation, transparency, and continued scientific collaboration.
Although regulatory agencies are showing increasing openness to alternative methodologies, broader acceptance of VCGs will depend on rigorous validation, transparent methods, and evidence that these approaches can support reliable safety assessments. Collaboration across industry will be essential to this process. Shared data standards, validation frameworks, and consortium-led initiatives could help build confidence in VCGs while promoting consistency across organizations.
Beyond animal reduction
Virtual control groups are most often linked to animal reduction, but that perspective captures only part of their significance.
Their broader value lies in transforming decades of accumulated scientific knowledge into an active decision-support system. By combining large-scale historical data, advanced analytics, clinical pathology expertise, and digital pathology innovation, VCGs can improve study interpretation, deepen understanding of biological variability, and enhance confidence in toxicology conclusions.
The future of VCGs will ultimately depend on the quality of the underlying data. Clinical pathology provides the quantitative foundation, while digital pathology provides the infrastructure needed to bring histopathology into the same data-driven network.
Together, they represent the critical next step in the evolution of nonclinical safety assessment — one that advances ethics, efficiency, and scientific rigor simultaneously. If implemented thoughtfully, virtual control groups may become not simply an alternative to traditional controls, but a foundation of the next generation of predictive, data-driven toxicology.
