Abstract：
Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.
摘要：
局部和透皮治疗最近已显著增长,并且在这些给药途径的药物发现和开发过程中考虑皮肤致敏是至关重要的。各种测试，包括动物和非动物方法，已经被设计来评估皮肤致敏的可能性。此外，已经创建了许多计算机模型，为体内等传统方法提供快速且具有成本效益的替代方法，在体外，以及对化合物进行分类的化学方法。在这项研究中,使用创新的分层支持向量回归（HSVR）方案开发了定量结构-活性关系（QSAR）模型。目的是通过分析直接肽反应性测定(DPRA)中的半胱氨酸消耗百分比来定量预测皮肤致敏的可能性。结果证明是准确的，一致,和训练集中的强大预测，测试集,和离群值集合。因此,该模型可用于评估新化合物或虚拟化合物的皮肤致敏潜能。
