Exposure to poly- and perfluoroalkyl substances (PFASs), an emerging class of endocrine disrupting halogenated pollutants, has been linked to thyroid toxicity in human populations across the globe. The PFASs can compete with thyroxine (T4) for binding to the human thyroid hormone transport protein transthyretin (TTR) which may lead to reduce thyroid hormone levels leading to endocrine disrupting activity. In this background, twenty-four PFASs, together with structurally similar natural fatty acids binding capacity in radioligand-binding assay values were modeled with classification- and regression-based quantitative structure-toxicity relationship (QSTR) to identify the responsible structural features and fragments of these diverse classes of PFASs. Additionally, docking study performed employing complex of TTR with bound 2,6-difluorobiphenyl-4-carboxylic acid (PDB: 2F7I) to constitute the receptor model for human TTR provided corroborating evidence for these binding interactions and indicated multiple high-affinity modes of binding. Further, developmental toxicity data on zebrafish (<i>Danio rerio</i>) embryos of single PFAS and tertiary mixtures were modeled employing QSTR tool. The computed models, as well as consensus model, are then employed for toxicity prediction of binary and tertiary mixtures which have no experimental data to check mixtures mode of toxicity and their possible threshold of toxicity for future risk assessment (Figure 1). Further, for virtual screening, we have employed twenty-four PFASs from our previous work to build a huge external dataset consists of single (24), binary (276) and tertiary (2024) mixtures (equal ratio mixtures) by permutation of the studied chemicals. Then, all developed models are employed for predicting this external dataset for interpretation of toxicity threats for individuals and mixtures along with identification of diverse range and combination of toxicity thresholds (Figure 2). We found that chemicals within these mixtures displayed concentration addition suggesting a similar mode of toxic action. Importantly, next-generation chemicals were less acutely toxic singly and in mixtures than their first generation counterpart. Not only that, mixtures of PFASs showed less toxicity than single PFAS. The developed <i>in silico</i> models, therefore, provide an understanding of important structural attributes of these chemicals and may serve as an efficient query tool for screening of large database.

Investigating ligand and structure-based modeling followed by mixture toxicity prediction of per-and polyfluoroalkyl substances: A virtual screening approach

255th National Meeting (2018)

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