Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews最新文献

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox models have been developed with a 10 × 5-fold cross-validation schema on the training portion of the CERAPP ToxCast dataset, formed by 1677 chemicals, each described by 777 molecular features. On the CERAPP "All Literature" evaluation set (agonist: 6319 compounds; antagonist 6539; binding 7283), ML4Tox significantly improved sensitivity over published results on all three tasks, with agonist: 0.78 vs 0.56; antagonist: 0.69 vs 0.11; binding: 0.66 vs 0.26.

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.

Phthalates are widely used in polymer materials as a plasticizer. These compounds possess potent toxic variations depending on their chemical structures. However, a growing body of evidence indicates that phthalate compounds are undoubtedly discovered in secondary metabolites of organisms, including plants, animals and microorganisms. This review firstly summarizes biological sources of various phthalates and their bioactivities reported during the past few decades as well as their environmental toxicities and public health risks. It suggests that these organisms are one of important sources of natural phthalates with diverse profiles of bioactivity and toxicity.

Molecular imaging has been widely applied in preclinical research. Among these new molecular imaging modalities, microPET imaging can be utilized as a very powerful tool that can obtain the measurements of multiple biological processes in various organs repeatedly in a same subject. This review discusses how this new approach provides noninvasive biomarker for neurotoxicology research and summarizes microPET findings with multiple radiotracers on the variety of neurotoxicity induced by toxic agents in both the rodent and the nonhuman primate brain.

Aluminum (Al) toxicity is a major factor limiting plant productivity. The objective of the present study was to develop the mechanisms of boron (B) alleviating aluminum toxicity in citrus. The results showed that aluminum toxicity severely hampered root elongation. Interestingly, under aluminum exposure, boron supply improved superoxide dismutase activity while reducing peroxidase, catalase and polyphenol oxidase activities. Likewise, the contents of H₂O₂, lipid peroxidation, protein and proline in roots were markedly decreased by boron application under aluminum exposure. Our results demonstrated that boron could alleviate aluminum toxicity by regulating antioxidant enzyme activities in the roots.