Bayer AG, Investigational Toxicology, Berlin, Germany.
KALOS Technologies, Philadelphia, Pennsylvania, USA.
Toxicol Pathol. 2023 Aug;51(6):361-362. doi: 10.1177/01926233231208987. Epub 2023 Oct 31.
The availability of large amounts of high-quality control data from tightly controlled regulated animal safety data has created the idea to re-use these data beyond its classical applications of quality control, identification of treatment-related effects and assessing effect-size relevance for building virtual control groups (VCGs). While the ethical and cost-saving aspects of such a concept are immediately evident, the potential challenges need to be carefully considered to avoid any effect which could lower the sensitivity of an animal study to detect adverse events, safety thresholds, target organs, or biomarkers. In our brief communication, we summarize the current discussion regarding VCGs and propose a path forward how the replacement of concurrent control with VCGs resulting from historical data could be systematically assessed and to come to conclusions regarding the scientific value of the concept.
大量高质量的控制数据可从严格控制的监管动物安全数据中获得，这一数据可用性产生了除了质量控制、识别与处理相关的影响以及评估构建虚拟对照组 (VCG) 的效果大小相关性等经典应用之外，重新使用这些数据的想法。虽然这种概念在伦理和节省成本方面的优势是显而易见的，但需要仔细考虑潜在的挑战，以避免任何可能降低动物研究检测不良事件、安全阈值、靶器官或生物标志物敏感性的影响。在我们的简要交流中，我们总结了目前关于 VCG 的讨论，并提出了一个前进的方向，即如何系统地评估用历史数据产生的 VCG 替代同期对照组，并就该概念的科学价值得出结论。
