Computational Toxicology of Environmental Microplastics: In-Silico Prediction of Emerging Hepatotoxic Risks through Molecular Docking and ADMET Profiling

Abstract:In light of the widespread presence of environmental microplastics (MPs; <5 mm) in ecosystems, humans are constantly exposed through ingestion, inhalation, and trophic transmission. The liver is a crucial target organ for microplastic-induced toxicity, as evidenced by growing biomonitoring data showing micro- and nanoplastics in human blood and hepatic tissues. The liver is especially susceptible to physicochemical interactions between microplastics, their additives, and sorbed environmental pollutants because it is the primary site of xenobiotic metabolism. Nevertheless, experimental limitations continue to restrict the ability to predict human hepatotoxic risk.

Through an emphasis on molecular docking, quantitative structure–activity relationships (QSAR), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, this review critically summarizes developments in computational toxicology methods for forecasting microplastic-associated hepatotoxicity. We assess the effects of surface charge, environmental aging, polymer type, and particle size (micro- to nanoscale) on molecular reactivity and hepatic bioavailability. To clarify possible mechanisms underlying metabolic disruption, cholestasis, oxidative stress, and inflammatory liver injury, docking-based analyses focusing on important hepatic proteins—such as cytochrome P450 enzymes, bile acid transporters, nuclear receptors, and oxidative stress–inflammatory signalling regulators—are discussed. To estimate hepatotoxic endpoints, biliary clearance, bioaccumulation, and metabolic interference, complementary ADMET predictions are evaluated.

This study illustrates the usefulness of in-silico models for mechanistic risk assessment of microplastic polymers and related compounds by combining molecular interaction data with systems toxicology and adverse outcome pathway frameworks. In order to improve predictive hepatotoxic risk assessment of environmental microplastics, important methodological constraints and future directions—such as artificial intelligence-assisted modeling and regulatory integration—are underlined.

Keywords:environmental microplastics; nanoplastics; hepatotoxicity; computational toxicology; molecular docking; ADMET profiling; cytochrome P450; adverse outcome pathways; bioaccumulation; human health risk assessment