Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures

Abstract

Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical’s toxicity. Thus, only a handful of works in the literature and methods are available for a mixture’s toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture’s toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture’s toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture’s toxicity are discussed.

In silico prediction of toxicity and its applications for chemicals at work

“…Chemicals are toxic by interacting with other substances, and risk assessments can overlook the side effects of the mixture. For example, although adding other metals together increases lead (Pb) toxicity and reports accumulative risk assessment, there is a lack of raw data on the toxicity of the mixture, due to chemical exposure types and complex interactions, and it is not possible to test all combinations of these elements [10].…”

Toward Sustainable Environmental Quality: Priority Research Questions for Asia

“…Diesel engine exhaust and oil spills are examples of extremely complex mixtures. Traditionally, mixture interaction was assumed to be represented in an "additive" way, that is, dose or response addition, if the components in the mixture have the same or similar modes of action or responses (Kar and Leszczynski 2019). The National Research Council of the United States recommended that cumulative risk assessment for mixtures be conducted for chemicals not only with the same modes of action but also with the same type of health outcomes (National Research Council 2008).…”

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

“…Quantitative structure-activity relationship, or QSAR (Figure 1), is an area of molecular modeling that studies relationships between structure and activity using mathematical statistics and machine learning methods. QSAR is efficiently used to predict toxicity of chemical substances [9][10][11][12][13]. Classical QSAR is a so-called Hansch analysis [14], which stands on the assumption that bioactivity of compounds is correlated with geometrical and physicochemical descriptors.…”