虚拟控制组的道路以及正确的体重选择的重要性
Alexander Gurjanov1, Lea A I Vaas2, Thomas Steger-Hartmann1
1Bayer Research & Development, Pharmaceuticals, Investigative Toxicology, Berlin, Germany.
ALTEX
|May 29, 2024
概括
从历史控制数据 (HCD) 获得的虚拟控制组 (VCG) 可以减少在毒性研究中使用动物. 当历史研究和传统研究的体重相匹配时,VCGs准确地复制了统计结果,在极端情况下,加权样本优异.
科学领域:
- 毒理学 毒理学 毒理学
- 药理学 药理学是指药理学的学科.
- 生物统计学 生物统计学
背景情况:
- 虚拟控制组 (VCGs) 使用历史控制数据 (HCD) 在监管毒性研究中潜在地减少并发控制动物数量.
- VCGs可以减少对并发对照动物的需求高达25%.
研究的目的:
- 评估VCGs在复制遗留动物毒性研究中体重发展的统计结果中的有效性.
- 评估不同的VCG采样方法是否能够重现28天次慢性研究的结果.
主要方法:
- 测试了三种VCG采样方法: (i) 从HCD随机采样, (ii) 由初始体重匹配的HCD采样,以及 (iii) 基于初始体重的HCD加权采样.
- 在动物研究中,初始体重作为动物年龄的替代品.
主要成果:
- 当遗留研究的初始体重分布与HCD分布保持一致时,VCGs准确地重现了统计结果.
- 权重抽样方法在遗留研究的体量在HCD分布的极端下降时表现优异.
- 传统和HCD重量分布之间的高度一致性确保了可靠的统计结果的复制.
结论:
- 该研究强调了将HCD与传统研究的初始体重相匹配对于准确的VCG性能至关重要.
- 适当的HCD匹配对于VCG在监管毒性测试中的成功实施至关重要.
- 这些发现提供了条件,在这些条件下,可以使用虚拟控制器准确地复制身体体重的发展.
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