Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure

文献类型： 外文期刊

第一作者： Chung, Elena

作者： Chung, Elena;Russo, Daniel P.;Zhu, Hao;Ciallella, Heather L.;Wang, Yu-Tang;Wu, Min;Aleksunes, Lauren M.

作者机构：

关键词： quantitative structure-activity relationships; models; carcinogens; big data; data mining; machine learning

期刊名称：ENVIRONMENTAL SCIENCE & TECHNOLOGY （ 影响因子：11.4； 五年影响因子：12.0 ）

ISSN： 0013-936X

年卷期： 2023 年 57 卷 16 期

页码：

收录情况： SCI

摘要： Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicityrelated models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency's Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds' carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.

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