The road to virtual control groups and the importance of proper body weight selection
Main Article Content
Abstract
Virtual control groups (VCGs) created from historical control data (HCD) can reduce the number of concurrent control group animals needed in regulatory toxicity studies by up to 25%. This study investigates the performance of VCGs on statistical outcomes of body weight development between treatment and control groups in legacy studies. The objective is to reproduce the statistical outcomes of 28-day sub-chronic studies (legacy studies) after replacing the concurrent control group with virtual ones. In rodent toxicity studies initial body weight is used as surrogate for the age of animals. For the assessment of VCG-sampling methods, three different approaches were explored: (i) sampling VCGs from the entire HCD, ignoring initial body weight information of the legacy study, (ii) sampling from HCD by matching the legacy study’s initial body weights, and (iii) sampling from HCD with assigned statistical weights derived from legacy study initial body weight information. The ability to reproduce statistical outcomes using virtual controls was determined by the congruence between the legacy study and the HCD weight distribution: regardless of the chosen approach, the ability to reproduce statistical outcomes was high for VCGs when the legacy study’s initial body weight distribution was similar to the HCD’s. When the initial body weight range of the legacy study was at the extreme ends of the HCD’s distribution, the weighted sampling approach was superior. This article demonstrates the importance of proper HCD matching by the legacy study’s initial body weight and discusses conditions to accurately reproduce body weight development.
Plain language summary
Animal control data from past studies performed in a standardized manner can be used to create virtual control groups (VCGs) to use in new studies instead of control animals. This approach can reduce the number of study animals needed by up to 25%. This study assessed the performance of VCGs selected by body weight in rat studies. The objective was to reproduce the original study results as closely as possible after replacing the original control group values with VCGs from a pool of historical control values. Several methods for selecting control animal data to create VCGs were compared. Among these, assigning statistical weights to the sampling pool yielded the best performance. Ideally, the body weight distributions on day 1 of the study should be similar between the VCG and the original study animals. This article shows that proper selection of VCGs can yield reliable study data with fewer animals.
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Dunnett, C. W. (1955). A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 50, 1096-1121. doi:10.1080/01621459.1955.10501294
Du Sert, N. P., Ahluwalia, A., Alam, S. et al. (2020). Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol 18, e3000411. doi:10.1371/journal.pbio.3000411
Golden, E., Allen, D., Amberg, A. et al. (2024). Toward implementing virtual control groups in nonclinical safety studies: Workshop report and roadmap to implementation. ALTEX 41, 282-301. doi:10.14573/altex.2310041
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
Gurjanov, A. (2024) VCG Initial Body Weight [Software]. GitHub. https://github.com/bayer-group/vcg-initbw
Gurjanov, A., Vieira-Vieira, C., Vienenkoetter, J. et al. (2024). Replacing concurrent controls with virtual control groups in rat toxicity studies. Regul Toxicol Pharmacol 148, 105592. doi:10.1016/j.yrtph.2024.105592
Hamada, C. (2018). Statistical analysis for toxicity studies. J Toxicol Pathol 31, 15-22. doi:10.1293/tox.2017-0050
Hoffman, W. P., Ness, D. K., van Lier, R. B. L. (2002). Analysis of rodent growth data in toxicology studies. Toxicol Sci 66, 313-319. doi:10.1093/toxsci/66.2.313
Hothorn, L. A. (2016). The two-step approach – a significant ANOVA F-test before Dunnett’s comparisons against a control – is not recommended. Commun Stat Theory Methods 45, 3332-3343. doi:10.1080/03610926.2014.902225
Howard, B. R. (2002). Control of variability. ILAR J 43, 194-201. doi:10.1093/ilar.43.4.194
ICH (2009). Guidance on nonclinical safety studies for the conduct of human clinical trials and marketing authorization for pharmaceuticals M3 (R2). International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. https://bit.ly/4dmQujM
Jacob Filho, W., Lima, C. C., Paunksnis, M. R. R. et al. (2018). Reference database of hematological parameters for growing and aging rats. Aging Male 21, 145-148. doi:10.1080/13685538.2017.1350156
Kluxen, F. M., Weber, K., Strupp, C. et al. (2021). Using historical control data in bioassays for regulatory toxicology. Regul Toxicol Pharmacol 125, 105024. doi:10.1016/j.yrtph.2021.105024
McCutcheon, J. E. and Marinelli, M. (2009). Age matters. Eur J Neurosci 29, 997-1014. doi:10.1111/j.1460-9568.2009.06648.x
Namdari, R., Jones, K., Chuang, S. S. et al. (2021). Species selection for nonclinical safety assessment of drug candidates: Examples of current industry practice. Regul Toxicol Pharmacol 126, 105029. doi:10.1016/j.yrtph.2021.105029
OECD (2008). Test No. 407: Repeated Dose 28-Day Oral Toxicity Study in Rodents. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10.1787/9789264070684-en
OECD (2018). Test No. 408: Repeated Dose 90-Day Oral Toxicity Study in Rodents. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10.1787/9789264070707-en
Russell, W. M. S. and Burch, R. L. (1959). The Principles of Humane Experimental Technique. Methuen. https://caat.jhsph.edu/the-principles-of-humane-experimental-technique-2/
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 01926233231208987. doi:10.1177/01926233231208987
Talbot, S. R., Biernot, S., Bleich, A. et al. (2020). Defining body-weight reduction as a humane endpoint: A critical appraisal. Lab Anim 54, 99-110. doi:10.1177/0023677219883319
Wolford, S., Schroer, R., Gallo, P. et al. (1987). Age-related changes in serum chemistry and hematology values in normal Sprague-Dawley rats. Fundam Appl Toxicol 8, 80-88. doi:10.1016/0272-0590(87)90102-3
Wright, P. S., Smith, G. F., Briggs, K. A. et al. (2023). Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database. Regul Toxicol Pharmacol 138, 105309. doi:10.1016/j.yrtph.2022.105309