Ecotoxicological Modeling of Organic Chemicals for Their Acute Toxicity in Algae Using Classification and Toxicophore-Based Approaches

Ecotoxicological Modeling of Organic Chemicals for Their Acute Toxicity in Algae Using Classification and Toxicophore-Based Approaches

Kabiruddin Khan, Probir Kumar Ojha
DOI: 10.4018/IJQSPR.2020040102
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Abstract

To study the relationship between toxicity of organic chemicals (OCs) with their structural and physicochemical features, the authors have developed a linear discriminant analysis (LDA) model for the classification of organic chemicals based on their acute observed toxicity in algae. This is done by employing a sufficiently large dataset of 352 chemicals following the strict Organization for Economic Cooperation and Development (OECD) guidelines for the quantitative structure-activity relationship (QSAR) validation. Additionally, 3D toxicophore models were generated to explore for the presence of common features contributing to the toxicity making chemicals a major concern for the future. Both of the models were rigorously validated following stringent validation criteria such as Wilks' λ statistic, canonical index (Rc), squared Mahalanobis distance, and chi-squared. Finally, a confusion matrix was employed to check for the quality of classification/prediction obtained for the LDA and pharmacophore models both in the training and the test sets.
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1. Introduction

The toxicity assessment of organic chemicals by the industries and regulatory bodies is an important step which precedes the release of a final product into the market. With the continuous globalization, there arises an enhanced demand for the organic chemicals in various spheres of life. Some of the major contributors in the total global production of organic chemicals come from the synthesis of pharmaceuticals, cosmetics, agrochemicals, biocides, chemical reagents, fertilizers, etc. These chemicals have varying degrees of exposure on humans as well as animals or plants based on the extent to which they are exposed. For examples, objects (any living species or plant) living in close proximity to the area which is engaged in manufacturing harmful chemicals are at greater risk of direct or indirect contact with these chemicals in contrast to people living in remote villages with full of natural biodiversity enjoying a healthier lifestyle. A lot of natural and synthetic chemicals are continuously exposed to flora and fauna but have never been evaluated by toxicity testing tools (Chuprina et al., 2010; Egeghy et al., 2012). Additionally, the number of reports for accumulation of these industrial chemicals have skyrocketed in the last few decades. Chemicals like pharmaceuticals and personal care products (PPCPs) have demonstrated the ability to infuse into the aquatic environment; the failure of conventional water treatment methods have rendered them re-enter into the water cycle. Thus, they can behave as potent persistent pollutants affecting the surrounding on a long term (Ellis, 2006). The increased loads of OCs in the aquatic environment (flora and fauna) pose the ecological risks for causing genotoxicity in aquatic species along with inducing gene resistance microbes (Dantas et al., 2008; Al-Bahry et al., 2009; Yang et al., 2011). It is expected that the extent of accumulation of OCs in water bodies mainly in rivers and oceans will increase due to shift of industries from developed to developing countries. One of the major problems of these chemicals is their ability to behave as potential PBT like entities. Within the REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) regulation, the assessment of PBT/vPvB (persistence, bioaccumulation and toxicity/very Persistent very Toxic) chemicals should be carried out following the definite criteria mentioned in Annex XIII (ECA, 2008). The lack of data against P, B and T possess a serious challenge in PBT screening of unknown/untested molecules (Arnot, & Gobas, 2006; Weisbrod et al., 2006; Arnot, & Mackay, 2008).

For several years we have relied on animals for the toxicity evaluation of harmful chemicals, however with recent objection on the use of animals due to ethical reasons have restricted scientists to rely on alternative strategies. Additionally, in the recent decade, there has been a push of regulatory agencies and scientists to replace extremely hazardous chemicals with safer alternatives (Schulte et al., 2013). The experimental identification of toxic chemicals is not only costly and time consuming but also contributing towards accumulation of these chemicals in the surroundings although marginally. From the ethical point of view, the use of animals for toxicity testing can be the cause of a debatable issue with animal ethical bodies. For these reasons, NIH (National Institute of Health) and EPA (Environment protection Agency) encourage the development of alternative in vitro and in silico approaches as seen in large-scale programs such as ToxCast project (Dix et al., 2007) and the Tox21 consortium (Tice et al., 2013). The REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals) legislation in European Union (Dearden, 2016; Khan et al., 2018; Khan et al., 2019b) has promoted the use of statistical quantitative structure-activity relationship (QSAR) models and structural alerts as major computational approaches to regulatory decision support and chemical safety assessment, since 2006.

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