Alladi Ramakrishnan Hall

Computational data-driven investigation of chemical exposome and its links to human and ecosystem health

Ajaya Kumar Sahoo

IMSc Chennai

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PhD Thesis Defence
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Abstract of PhD Thesis
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Humans and ecosystems are frequently exposed to myriad of chemicals, including those found in consumer products, industrial pollutants, and pesticides, which collectively con- stitute the chemical exposome. These chemicals can persist in the environment and bioaccumulate, leading to detrimental effects on humans and other organisms, as well as long-term ecological impacts. Therefore, it is imperative to characterize the chemical exposome and assess its impact on human and ecosystem health. To this end, traditional toxicity testing often relies on animal models which can be low-throughput, expensive and time consuming, and therefore, computational approaches have emerged as effective alternatives to expedite the characterization of the ever-expanding chemical exposome. In this thesis, we employ various computational approaches to characterize the structure- activity landscape and structure-mechanism relationship among environmental chemicals within the chemical exposome. Further, we investigate chemical-induced health effects on humans and ecosystems through the adverse outcome pathway (AOP) framework.

For the characterization of structure-activity landscape of endocrine disruptors among environmental chemicals, we focus on two distinct chemical spaces, namely, androgen re- ceptor (AR) binding chemicals and thyroid stimulating hormone receptor (TSHR) binding chemicals. In both cases, we employ several computational approaches to analyze hetero- geneity in the structure-activity landscape of these chemical spaces and identify activity cliffs, i.e., structurally similar chemicals exhibiting large differences in their activities against a target receptor. Further we classify the identified activity cliffs based on their structural features. Additionally, we analyze the structure-mechanism relationships of the TSHR binding chemicals and identify structurally similar chemicals differing in their mechanism of actions. In sum, the inferences from these computational analyses will aid in development of improved toxicity predictors for characterization of the chemical exposome.

Next, we investigate the adverse health effects induced by environmental chemicals, by focusing on certain classes of chemicals namely, heavy metal - cadmium, plastic additives and petroleum hydrocarbons (PHs), through AOP framework. In each case, we curate a list of chemicals by relying on published reports and existing resources. We then integrate biological endpoint data from various toxicological resources to identify associa- tions between the chemicals with the high quality and complete AOPs within AOP-Wiki. Thereafter, we utilized these chemical-AOP associations to construct chemical-specific AOP networks and analyze toxicity pathways to understand the mechanisms underlying chemical-induced adverse effects in both humans and ecological species. Further, we assess the toxicities of the PHs across diverse ecological species using network-based approaches and perform ecological risk assessment. In conclusion, this thesis presents a systematic computational approach that integrates heterogeneous toxicological data to investigate environmental chemicals and their adverse effects on humans and ecosystems, offering a holistic overview of the chemical exposome and its health implications from a One Health perspective.