Evaluation of the Global Performance of Eight In Silico Skin Sensitization Models Using Human Data
Evaluation of the Global Performance of Eight In Silico Skin Sensitization Models Using Human Data
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DOI：
10.14573/altex.1911261
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发表时间：
2021-01-01
影响因子：
5.6
通讯作者：
Maertens, Alexandra
中科院分区：
文献类型：
--
作者：
Golden, Emily;Macmillan, Donna S.;Maertens, Alexandra
变应性接触性皮炎，或皮肤致敏的临床表现，是一种主要的职业危害。存在几种测试方法来评估皮肤致敏性，但由于其高速度和低成本的结果，计算机模型可能是最有利的。存在许多计算机皮肤致敏模型，尽管许多模型仅针对动物研究的结果进行了测试（例如，LLNA）;这在筛选和监管背景下都造成了人类皮肤致敏性评估的不确定性。该项目的目的是针对两个人类数据集评估八个计算机皮肤致敏模型的准确性：一个是高度策划的（Basketter等人，2014）和一个筛选级别（HSDB）。将两个数据集中每种化学品的二元皮肤致敏状态与8种计算机皮肤致敏工具（Toxtree、PredSkin、OECD的QSAR数据库、UL的REACHAcross（TM）、丹麦QSAR数据库、TIMES-SS和Lhasa Limited的Derek Nexus）的预测值进行比较。评估模型的覆盖率、准确性、灵敏度和特异性，以及优化特征（例如，准确性概率、适用域等），如果可以的话。虽然存在广泛的灵敏度和特异性，但模型在预测人类皮肤致敏状态（即，准确度约为70-80%）。此外，这些模型没有错误预测相同的化合物，这表明组合模型可能具有优势。计算机模拟皮肤致敏模型在筛选背景下提供了准确和有用的见解;然而，需要进一步改进，以便这些模型可以被认为是完全可靠的监管应用。
Allergic contact dermatitis, or the clinical manifestation of skin sensitization, is a leading occupational hazard. Several testing approaches exist to assess skin sensitization, but in silico models are perhaps the most advantageous due to their high speed and low-cost results. Many in silico skin sensitization models exist, though many have only been tested against results from animal studies (e.g., LLNA); this creates uncertainty in human skin sensitization assessments in both a screening and regulatory context. This project's aim was to evaluate the accuracy of eight in silico skin sensitization models against two human data sets: one highly curated (Basketter et al., 2014) and one screening level (HSDB). The binary skin sensitization status of each chemical in each of the two data sets was compared to the prediction from eight in silico skin sensitization tools (Toxtree, PredSkin, OECD's QSAR Toolbox, UL's REACHAcross (TM), Danish QSAR Database, TIMES-SS, and Lhasa Limited's Derek Nexus). Models were assessed for coverage, accuracy, sensitivity, and specificity, as well as optimization features (e.g., probability of accuracy, applicability domain, etc.), if available. While there was a wide range of sensitivity and specificity, the models generally performed comparably to the LLNA in predicting human skin sensitization status (i.e., approximately 70-80% accuracy). Additionally, the models did not mispredict the same compounds, suggesting there might be an advantage in combining models. In silico skin sensitization models offer accurate and useful insights in a screening context; however, further improvements are necessary so these models may be considered fully reliable for regulatory applications.