Virtual Control Groups: one step forward into the future of animal testing in toxicology

Historical control databases are established by many companies (i) enabling contextualization of results from single studies against previous studies performed under similar conditions, (ii) to properly design studies and/or (iii) to come up with quality control instruments. Although the use of historical control data in supporting inferences varies across different assays; one major opportunity is the derivation of so-called Virtual Control Groups (VCGs) for replacement of animals in the control groups and thus enabling a 3R strategy for future animal testing approaches.

Our goal is to reduce the number of animals used in experiments

Starting with an exceptional dataset provided by members of the eTRANSAFE consortium we start the journey into the future of animal testing via derivation and incorporation of VCGs in animal testing approaches and thus enabling a 3R strategy. 

A multi-disciplinary cross-industry and cross-academic team of statisticians, toxicologists and data-scientists from Merck, Roche, Novartis, Bayer und Sanofi has been established and coordinates the data collection process among the eTRANSAFE partners. Bayer especially supports this activity by providing resources from its Research and Pre-Clinical Statistics Group and has decided to strengthen the action by hiring the PhD student Alexander Gurjanov. In close collaboration with the statistician Lea A.I. Vaas, a stepwise approach is planned:

  • Thorough data-quality check:
    A thorough quality control will be performed to identify, extract and purify all the datapoints which will then be used for the model building (see Steger-Hartmann et. al (2020)).
  • Benchmarking:
    The model itself will be benchmarked and evaluated with concurrent data to determine its reliability and applicability domain.
  • Start Dialog:
    Approach regulatory authorities in order to discuss VCGs as an acceptable method to replace control-group-animals in toxicity studies.


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