Introducing the concept of virtual control groups into preclinical toxicology testing

构造(python库) 数据共享 控制(管理) 集合(抽象数据类型) 计算机科学 治疗组和对照组 数据科学 风险分析(工程) 医学 病理 人工智能 替代医学 程序设计语言
作者
Thomas Steger‐Hartmann
出处
期刊:Alternatives to animal experimentation [Springer Nature]
卷期号:37 (3): 343-349 被引量:46
标识
DOI:10.14573/altex.2001311
摘要

Sharing legacy data from in vivo toxicity studies offers the opportunity to analyze the variability of control groups stratified for strain, age, duration of study, vehicle and other experimental conditions. Historical animal control group data may lead to a repository, which could be used to construct virtual control groups (VCGs) for toxicity studies. VCGs are an established concept in clinical trials, but the idea of replacing living beings with virtual data sets has so far not been introduced into the design of regulatory animal studies. The use of VCGs has the potential of a 25% reduction in animal use by replacing the control group animals with existing randomized data sets. Prerequisites for such an approach are the availability of large and well-structured control data sets as well as thorough statistical evaluations. the foundation of data sharing has been laid within the Innovative Medicines Initiatives projects eTOX and eTRANSAFE. For a proof of principle participating companies have started to collect control group data for subacute (4-week) GLP studies with Wistar rats (the strain preferentially used in Europe) and are characterizing these data for its variability. In a second step, the control group data will be shared among the companies and cross-company variability will be investigated. In a third step, a set of studies will be analyzed to assess whether the use of VCG data would have influenced the outcome of the study compared to the real control group.
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