Toward implementing virtual control groups in nonclinical safety studies Workshop report and roadmap to implementation

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Emily Golden, David Allen, Alexander Amberg, Lennart T. Anger, Elizabeth Baker, Szczepan W. Baran, Frank Bringezu, Matthew Clark, Guillemette Duchateau-Nguyen, Sylvia E. Escher, Varun Giri, Armelle Grevot, Thomas Hartung, Dingzhou Li, Laura Lotfi, Wolfgang Muster, Kevin Snyder, Ronald Wange, Thomas Steger-Hartmann
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Historical data from control groups in animal toxicity studies are currently mainly used for comparative purposes to assess validity and robustness of study results. Due to the highly controlled environment in which the studies are performed and the homogeneity of the animal collectives it has been proposed to use the historical data to build so-called virtual control groups, which could partly or entirely replace the concurrent control group. This would constitute a substantial contribution to the reduction of animal use in safety studies. Before the concept can be implemented, the prerequisites regarding data collection, curation, and statistical evaluation together with a validation strategy need to be identified to avoid any impairment of the study outcome and subsequent consequences for human risk assessment. To further assess and develop the concept of virtual control groups, the transatlantic think tank for toxicology (t4) sponsored a workshop with stakeholders from the phar­maceutical and chemical industry, academia, FDA, contract research organizations (CROs), and non-governmental organizations in Washington, which took place in March 2023. This report sum­marizes the current efforts of a European initiative to share, collect, and curate animal control data in a centralized database and the first approaches to identify optimal matching criteria between virtual controls and the treatment arms of a study as well as first reflections about strategies for a qualifi­cation procedure and potential pitfalls of the concept.

Plain language summary
Animal safety studies are usually performed with three test groups of animals where increasing amounts of the test chemical are given to the animals and one control group where the animals do not receive the test chemical. The design of such studies, the characteristics of the animals, and the measured parameters are often very similar from study to study. Therefore, it has been suggested that measurement data from the control groups could be reused from study to study to lower the total number of animals per study. This could reduce animal use by up to 25% for such standardized studies. A workshop was held to discuss the pros and cons of such a concept and what would have to be done to implement it without threatening the reliability of the study outcome or the resulting human risk assessment.

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Golden, E. (2024) “Toward implementing virtual control groups in nonclinical safety studies: Workshop report and roadmap to implementation”, ALTEX - Alternatives to animal experimentation, 41(2), pp. 282–301. doi: 10.14573/altex.2310041.

Ackley, D., Birkebak, J., Blumel, J. et al. (2023). FDA and industry collaboration: Identifying opportunities to further reduce reliance on nonhuman primates for nonclinical safety evaluations. Regul Toxicol Pharm 138, 105327. doi:10.1016/j.yrtph.2022.105327

Adams, M. A. and Conway, T. L. (2014). Eta squared. In A. C. Michalos (ed.), Encyclopedia of Quality of Life and Well-Being Research. Dordrecht, The Netherlands: Springer. doi:10.1007/978-94-007-0753-5_918

Bate, S. and Karp, N. A. (2014). A common control group – Optimising the experiment design to maximise sensitivity. PLoS One 9, e114872. doi:10.1371/journal.pone.0114872

Berry, D. A., Elashoff, M., Blotner, S. et al. (2017). Creating a synthetic control arm from previous clinical trials: Application to establishing early end points as indicators of overall survival in acute myeloid leukemia (AML). J Clin Oncol 35, 7021-7021. doi:10.1200/Jco.2017.35.15_suppl.7021

Bliss-Moreau, E., Amara, R. R., Buffalo, E. A. et al. (2021). Improving rigor and reproducibility in nonhuman primate research. Am J Primatol 83, e23331. doi:10.1002/ajp.23331

Briggs, K., Cases, M., Heard, D. J. et al. (2012). Inroads to predict in vivo toxicology – An introduction to the eTOX project. Int J Mol Sci 13, 3820-3846. doi:10.3390/ijms13033820

Carfagna, M. A., Bjerregaard, T. G., Fukushima, T. et al. (2020). Send harmonization & cross-study analysis: A proposal to better harvest the value from send data. Regul Toxicol Pharm 111, 104542. doi:10.1016/j.yrtph.2019.104542

CBO – Congressional Budget Office (2021). Research and development in the pharmaceutical industry.

Clark, M. and Steger-Hartmann, T. (2018). A big data approach to the concordance of the toxicity of pharmaceuticals in animals and humans. Regul Toxicol Pharm 96, 94-105. doi:10.1016/j.yrtph.2018.04.018

DHHS – Department of Health and Human Services (2023). Justification of estimates for appropriations committees. Food and Drug Administration.

Duchateau-Nguyen, G. (2022). Virtual control groups in animal toxicology studies. Non-Clinical Statistics Conference, Louvain-la-Neuve, Belgium.

Everds, N. E. (2015). Evaluation of clinical pathology data: Correlating changes with other study data. Toxicol Pathol 43, 90-97. doi:10.1177/0192623314555340

Gurjanov, A., Kreuchwig, A., Steger-Hartmann, T. et al. (2023). Hurdles and signposts on the road to virtual control groups – A case study illustrating the influence of anesthesia protocols on electrolyte levels in rats. Front Pharmacol 14, 1142534. doi:10.3389/fphar.2023.1142534

Iacus, S. M., King, G. and Porro, G. (2008). Matching for causal inference without balance checking. SSRN.

Jones, S. R., Carley, S. and Harrison, M. (2003). An introduction to power and sample size estimation. Emerg Med J 20, 453-458. doi:10.1136/emj.20.5.453

Makurvet, F. D. (2021). Biologics vs. small molecules: Drug costs and patient access. Med Drug Discov 9, 100075. doi:10.1016/j.medidd.2020.100075

Mecklenburg, L., Lenz, S. and Hempel, G. (2023). How important are concurrent vehicle control groups in (sub)chronic non-human primate toxicity studies conducted in pharmaceutical development? An opportunity to reduce animal numbers. PLoS One 18, e0282404. doi:10.1371/journal.pone.0282404

NASEM – National Academies of Science, Engineering, and Medicine (2023). Nonhuman primate models in biomedical research: State of the science and future needs. doi:10.17226/26857

NIH – National Institutes of Health (NIH) (2022). ACD working group on catalyzing the development and use of novel alternative methods to advance biomedical research.

Phillips, K. A., Bales, K. L., Capitanio, J. P. et al. (2014). Why primate models matter. Am J Primatol 76, 801-827. doi:10.1002/ajp.22281

Pognan, F., Steger-Hartmann, T., Díaz, C. et al. (2021). The eTRANSAFE project on translational safety assessment through integrative knowledge management: Achievements and perspectives. Pharmaceuticals 14, 237. doi:10.3390/ph14030237

Sanz, F., Pognan, F., Steger-Hartmann, T. et al. (2023). eTRANSAFE: Data science to empower translational safety assessment. Nat Rev Drug Discov 22, 605-606. doi:10.1038/d41573-023-00099-5

Sharp, P. E. , La Regina, M. C. , Suckow, M. A. (1998). The Laboratory Rat. Boca Raton: CRC Press.Steger-Hartmann, T., Kreuchwig, A., Vaas, L. et al. (2020). Introducing the concept of virtual control groups into preclinical toxicology testing. ALTEX 37, 343-349. doi:10.14573/altex.2001311

Steger-Hartmann, T. and Clark, M. (2023). Can historical control group data be used to replace concurrent controls in animal studies? Toxicol Pathol, online ahead of print. doi:10.1177/01926233231208987

Steger-Hartmann, T., Kreuchwig, A., Wang, K. et al. (2023). Perspectives of data science in preclinical safety assessment. Drug Discov Today 28, 103642. doi:10.1016/j.drudis.2023.103642

Strayhorn, J. M., Jr. (2021). Virtual controls as an alternative to randomized controlled trials for assessing efficacy of interventions. BMC Med Res Methodol 21, 3. doi:10.1186/s12874-020-01191-9

Sun, D., Gao, W., Hu, H. et al. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B 12, 3049-3062. doi:10.1016/j.apsb.2022.02.002

US FDA (2022). S.5002. FDA Modernization Act 2.0. 117th congress (2021-2022)., X. and Monticello, T. M. (2006). Modern imaging technologies in toxicologic pathology: An overview. Toxicol Pathol 34, 815-826. doi:10.1080/01926230600918983

Ying, X. and Monticello, T. M. (2006). Modern imaging technologies in toxicologic pathology: An overview. Toxicol Pathol 34, 815-826. doi:10.1080/01926230600918983

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