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

Ph.D HBNI 2024

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.