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.

Efficacy and cytotoxicity in cell culture of novel α-hydroxytropolone inhibitors of hepatitis B virus ribonuclease H

“…Physicochemical properties of the compounds can both influence their toxicological profile (Blagg, 2006; Raies and Bajic, 2016). Many of these chemical parameters can be calculated from the compounds’ structures, so we determined if there were quantitative correlations between calculated physicochemical properties of tropolones and anti-HBV efficacy and cytotoxicity.…”

Current data regarding the structure-toxicity relationship of boron-containing compounds

“…In fact, the theoretical approach for biological assessment of BCCs is only weakly related to experimental results, and the former has shown a bias to exaggerating the toxic effect of BCCs (Ciani, 2012). The poor quality of these results is probably related to erroneously choosing the method (Raies, 2016) or to the poor data that is available on BCC toxicology in databases.…”

“…Also, in silico analysis of BCCs has yielded limited results when using Lipinski's rule or predictors based on pharmacokinetics and pharmacodynamics (Bakri, 2014). With innovative strategies, some researchers are trying to build a multiclass model to predict several categories of toxicity (e.g., acute toxicity, mutagenicity, tumorigenicity, skin and eye irritation, reproductive effects and multiple dose effects) by using databases instead of only one or two toxicity endpoints (Chen, 2013;Raies, 2016).…”

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“…There are various methods for generating models to predict toxicity endpoints, including structural alerts (SAs) and rule-based models; read-across (RA), dose–response (DR), and time–response (TR) models; pharmacokinetic (PK) and pharmacodynamic (PD) models; uncertainty factors (UFs) models; and the quantitative structure–activity relationship (QSAR) model. 93–95 …”