Liverpool John Moores University, Liverpool, United Kingdom.
Imperial College London, London, United Kingdom.
Food Chem Toxicol. 2024 Dec;194:115070. doi: 10.1016/j.fct.2024.115070. Epub 2024 Oct 22.
The Cramer classification scheme has emerged as one of the most extensively-adopted predictive toxicology tools, owing in part to its employment for chemical categorisation within threshold of toxicological concern evaluation. The characteristics of several of its rules have contributed to inconsistencies with respect to degree of hazard attributed to common (particularly food-relevant) substances. This investigation examines these discrepancies, and their origins, raising awareness of such issues amongst users seeking to apply and/or adapt the rule-set. A dataset of over 3000 compounds was assembled, each with Cramer class assignments issued by up to four groups of industry and academic experts. These were complemented by corresponding outputs from in silico implementations of the scheme present within Toxtree and OECD QSAR Toolbox software, including a working of a "Revised Cramer Decision Tree". Consistency between judgments was assessed, revealing that although the extent of inter-expert agreement was very high (≥97%), general concordance between expert and in silico calls was more modest (∼70%). In particular, 22 chemical groupings were identified to serve as prominent sources of disagreement, the origins of which could be attributed either to differences in subjective interpretation, to software coding anomalies, or to reforms introduced by authors of the revised rules.
克莱默分类方案已成为应用最广泛的预测毒理学工具之一，部分原因是它可用于毒理学关注阈值评估中的化学分类。其部分规则的特点导致其对常见（特别是与食物相关的）物质的危害程度的归属存在不一致性。本研究探讨了这些差异及其来源，旨在提高希望应用和/或改编规则集的用户对这些问题的认识。我们收集了超过 3000 种化合物的数据集，每个化合物都有克莱默分类的分配，由多达四个行业和学术专家小组给出。这些数据集还补充了来自 Toxtree 和 OECD QSAR Toolbox 软件中该方案的计算机实现的相应输出，包括“修订克莱默决策树”的工作。我们评估了判断之间的一致性，结果表明，尽管专家之间的一致性非常高（≥97%），但专家和计算机判断之间的总体一致性更为适度（约 70%）。特别是，我们确定了 22 个化学分组作为分歧的主要来源，其来源可归因于主观解释的差异、软件编码异常或修订规则作者引入的改革。
