ABSTRACT

As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for classification and labeling of chemicals for eye irritation category [eye damage (Category 1), irritating to eye (Category 2) and nonirritating (No category)] of liquid chemicals with Integrated approaches to testing and assessment (IATA)-like two-stage random forest approach. Liquid chemicals (n = 219) with 34 physicochemical descriptors and quality in vivo data were collected with no missing values. Seven machine learning algorithms (Naive Bayes, Logistic Regression, First Large Margin, Neural Net, Random Forest (RF), Gradient Boosted Tree, and Support Vector Machine) were examined for the ternary categorization of eye irritation potential at a single run through 10-fold cross-validation. RF, which performed best, was further improved by applying the ‘Bottom-up approach’ concept of IATA, namely, separating No category first, and discriminating Category 1 from 2, thereafter. The best performing training dataset achieved an overall accuracy of 73% and the correct prediction for Category 1, 2, and No category was 80%, 50%, and 77%, respectively for the test dataset. This prediction model was further validated with an external dataset of 28 chemicals, for which an overall accuracy of 71% was achieved.

摘要

作为体内Draize 兔眼刺激试验的替代方法，本研究旨在构建一个计算机模型来预测完整的联合国 (UN) 全球协调制度 (GHS)，用于对眼刺激类别 [眼损伤]类别 1)、对眼睛有刺激性（类别 2）和无刺激性（无类别）] 的液体化学品，采用综合测试和评估方法 (IATA)，类似于两阶段随机森林方法。液体化学品 (n = 219) 具有 34 个物理化学描述符和体内质量收集的数据没有缺失值。检查了七种机器学习算法（朴素贝叶斯、逻辑回归、第一大边距、神经网络、随机森林 (RF)、梯度提升树和支持向量机）在一次运行中对眼睛刺激潜力的三元分类 10-折叠交叉验证。RF 表现最好，通过应用 IATA 的“自下而上方法”概念得到进一步改进，即首先分离 No 类别，然后区分类别 1 和类别 2。表现最好的训练数据集的总体准确率达到了 73%，而测试数据集对类别 1、2 和无类别的正确预测分别为 80%、50% 和 77%。该预测模型通过包含 28 种化学品的外部数据集进一步验证，总体准确度达到 71%。