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In silico toxicology: computational methods for the prediction of chemical toxicity.

ABSTRACT: Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

SUBMITTER: Raies AB

PROVIDER: S-EPMC4785608 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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In silico toxicology: computational methods for the prediction of chemical toxicity.

Raies Arwa B AB Bajic Vladimir B VB

Wiley interdisciplinary reviews. Computational molecular science 20160106 2

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessme ...[more]

PMID: 27066112

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Project description:Zearalenone (ZEA), α-zearalenol (α-ZEL) and β-zearalenol (β-ZEL) (ZEA's metabolites) are co/present in cereals, fruits or their products. All three with other compounds, constitute a cocktail-mixture that consumers (and also animals) are exposed and never entirely evaluated, nor in vitro nor in vivo. Effect of ZEA has been correlated to endocrine disruptor alterations as well as its metabolites (α-ZEL and β-ZEL); however, toxic effects associated to metabolites generated once ingested are unknown and difficult to study. The present study defines the metabolomics profile of all three mycotoxins (ZEA, α-ZEL and β-ZEL) and explores the prediction of their toxic effects proposing an in silico workflow by using three programs of predictions: MetaTox, SwissADME and PASS online. Metabolomic profile was also defined and toxic effect evaluated for all metabolite products from Phase I and II reaction (a total of 15 compounds). Results revealed that products describing metabolomics profile were: from O-glucuronidation (1z and 2z for ZEA and 1 ab, 2 ab and 3 ab for ZEA's metabolites), S-sulfation (3z and 4z for ZEA and 4 ab, 5 ab and 6 ab for ZEA's metabolites) and hydrolysis (5z and 7 ab for ZEA's metabolites, respectively). Lipinsky's rule-of-five was followed by all compounds except those coming from O-glucuronidation (HBA>10). Metabolite products had better properties to reach blood brain barrier than initial mycotoxins. According to Pa values (probability of activation) order of toxic effects studied was carcinogenicity > nephrotoxic > hepatotoxic > endocrine disruptor > mutagenic (AMES TEST) > genotoxic. Prediction of inhibition, induction and substrate function on different isoforms of Cytochrome P450 (CYP1A1, CYP1A2, CYP2C9 and CYP3A4) varied for each compounds analyzed; similarly, for activation of caspases 3 and 8. Relying to our findings, the metabolomics profile of ZEA, α-ZEL and β-ZEL analyzed by in silico programs predicts alteration of systems/pathways/mechanisms that ends up causing several toxic effects, giving an excellent sight and direct studies before starting in vitro or in vivo assays contributing to 3Rs principle; however, confirmation can be only demonstrated by performing those assays.

Dataset Information

In silico toxicology: computational methods for the prediction of chemical toxicity.

Publications

In silico toxicology: computational methods for the prediction of chemical toxicity.

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