Computational data-driven investigation of chemical exposome and its links to human and ecosystem health [HBNI Th253]

Abstract

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.