Chemical characterization of anemia-inducing aniline-related substances and their application to the construction of a decision tree-based anemia prediction model
Chemical characterization of anemia-inducing aniline-related substances and their application to the construction of a decision tree-based anemia prediction model
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引起贫血的苯胺相关物质的化学表征及其在构建基于决策树的贫血预测模型中的应用
DOI：
10.1016/j.fct.2021.112548
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发表时间：
2021
影响因子：
4.3
通讯作者：
and Kouichi Yoshinari
中科院分区：
文献类型：
--
作者：
Takaho Asai;Jun-ichi Takeshita;Yuki Shimizu;Yoshihiro Tochikubo;Ryota Shizu;Takuomi Hosaka;Yuichiro Kanno;and Kouichi Yoshinari
贫血是一种被广泛观察到的化学物质毒性，而苯胺是一种典型的致贫血物质。然而，目前还不清楚是否所有具有不同取代基的苯胺类物质都会导致贫血。因此，我们用决策树分析方法研究了引起贫血的物质的物理化学特性。训练和验证物质从公开可用的大鼠重复剂量毒性研究数据库中选择，并以分子描述符为解释变量，采用决策树和自举方法构建判别模型。为了提高判别的准确性，我们对解释变量进行了单独评估以对其进行修正，通过考虑新陈代谢，如偶氮还原和N-脱烷基，建立了在对物质进行决策树之前应用的“前提规则”，并引入了对具有多个苯胺类亚结构的物质进行“部分否定”评估的想法。最终得到的模型对训练数据集和验证数据集的准确率分别为79.2%和77.5%。此外，我们还发现了一些降低苯胺类物质贫血诱导性的化学性质，包括在苯胺上添加了磺酸盐或羧基和/或大体积的多环结构。综上所述，本研究结果将为理解化学性贫血的机制提供一个新的视角，并有助于开发一种预测系统。
Anemia is a well-observed toxicity of chemical substances, and aniline is a typical anemia-inducing substance. However, it remains unclear whether all aniline-like substances with various substituents could induce anemia. We thus investigated the physicochemical characteristics of anemia-inducing substances by decision tree analyses. Training and validation substances were selected from a publicly available database of rat repeated-dose toxicity studies, and discrimination models were constructed by decision tree and bootstrapping methods with molecular descriptors as explanatory variables. To improve the accuracy of discrimination, we individually evaluated the explanatory variables to modify them, established “prerules” that were applied before subjecting a substance to a decision tree by considering metabolism, such as azo reduction andN-dealkylation, and introduced the idea of “partly negative” evaluation for substances having multiple aniline-like substructures. The final model obtained showed 79.2% and 77.5% accuracy for the training and validation dataset, respectively. In addition, we identified some chemical properties that reduce the anemia inducibility of aniline-like substances, including the addition of a sulfonate or carboxy functional group and/or a bulky multiring structure to anilines. In conclusion, the present findings will provide a novel insight into the mechanistic understanding of chemically induced anemia and help to develop a prediction system.