Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany.
HITeC e.V., 22527 Hamburg, Germany.
Chem Res Toxicol. 2021 Feb 15;34(2):330-344. doi: 10.1021/acs.chemrestox.0c00253. Epub 2020 Dec 9.
Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctorII/.
皮肤致敏潜力或效力是新化学物质和新化学混合物安全性评估的一个重要终点。以前，动物实验（如局部淋巴结测定法）是主要的评估形式。然而，如今的重点在于开发非动物测试方法（即体外和化学分析）和计算模型。在这项工作中，我们根据公开的局部淋巴结测定法数据，研究了聚合的 Mondrian 一致性预测分类器区分非致敏和致敏化合物以及两种皮肤致敏潜力水平（弱至中度致敏剂和强至极度致敏剂）的能力。一致性预测框架相对于其他建模方法的优势在于，只有在对个别预测达到定义的置信度最低水平时，才会将化合物分配到活性类别中。这消除了对适用性域标准的需求，而适用性域标准在性质上通常是任意的，并且灵活性较差。我们的新二进制分类器命名为 Skin Doctor CP，与我们之前提出的相应非一致性预测工作流程相比，它能够以更高的可靠性-效率比区分非致敏剂和致敏剂。当在一组 257 种化合物上进行测试，在显著性水平为 0.10 和 0.30 时，该模型的效率分别为 0.49 和 0.92，准确率分别为 0.83 和 0.75。此外，我们开发了一个三元分类工作流程，以区分非致敏剂、弱至中度致敏剂和强至极度致敏剂。尽管该模型的整体性能令人满意（在显著性水平为 0.10 和 0.30 时，非致敏剂、弱至中度致敏剂和强至极度致敏剂的准确率分别为 0.90 和 0.73，效率分别为 0.42 和 0.90），但它并没有获得令人满意的类别结果（在显著性水平为 0.30 时，非致敏剂、弱至中度致敏剂和强至极度致敏剂的有效性分别为 0.70、0.58 和 0.63）。我们认为该模型因此无法可靠地识别强至极度致敏剂，并建议从当前可用的局部淋巴结测定法数据中衍生的其他三元模型可能存在相同的问题。Skin Doctor CP 可通过公共网络服务在 https://nerdd.zbh.uni-hamburg.de/skinDoctorII/ 上获得。
