Ecotoxicological Modeling and Environmental Risk Predictions Using In Silico Approaches

Dear Colleagues,

Industrial, commercial, and other chemicals for use in agricultural, domestic, and other sectors have an impact on living organisms in the environment, including humans. In cases of unintended exposure at a significant level of concentration, these effects may be hazardous for organisms of different trophic levels. This has led to an ever-growing concern about the ecotoxicological risk potential of chemicals as well as their indirect effects on human health. Pharmaceuticals, industrial and household chemicals, personal care products, pesticides, plasticizers, flame retardants, surfactants, manufactured nanomaterials, microplastics and their transformation products, etc., are chemicals of emerging concern, most of which have endocrine-disrupting effects, as well as possible genotoxicity, cytotoxicity, carcinogenicity, antibiotic resistance, etc. Despite their risk potential, the experimental data on the toxic effects of chemicals, pharmaceuticals, cosmetics, biocides, pesticides, dyes, solvents, and other pollutants on the ecosystem are very limited. Additionally, relevant data on the soil sorption, bioconcentration, biodegradation, biotransformation, etc., of commercial chemicals are critical for determining the level of their environmental threat. It is practically impossible to gather experimental data for a large number of chemicals on numerous ecotoxicity and environmental property endpoints. Due to data gaps, different regulatory bodies, such as the Organization for Economic Co-operation and Development (OECD), European Centre for the Validation of Alternative Methods (ECVAM), European Chemical Agency (ECHA), etc., and various regulations, such as the Registration, Evaluation and Authorization and Restriction of Chemicals (REACH) and Classification, Labelling and Packaging (CLP) regulations in the EU and Toxic Substances Control Act (TSCA) in the US, recognize the use of in silico models such as quantitative structure–activity relationships (QSARs) and read-across (RA) as supplements or even as replacements for experimental testing. These approaches support the “3Rs” (replacement, refinement, and reduction in animal use in research) principles and might help in designing “greener” alternatives replacing the original toxic chemicals.

This Special Issue will report the development, validation, and application of different in silico approaches, including (but not limited to) quantitative structure–activity relationships (QSARs), read-across (RA), and read-across structure–activity relationships (RASARs) for use in ecotoxicological risk assessment and environmental fate predictions such as:

• Ecotoxicological modeling of organic pollutants;
• Ecotoxicological modeling of pharmaceuticals, personal care products, and biocides;
• Ecotoxicological modeling of polymers;
• Ecotoxicological modeling of metal oxide nanoparticles;
• Ecotoxicological modeling of organic chemical mixtures;
• Ecotoxicological modeling of chemicals of emerging concern (CECs);
• Ecotoxicological modeling of ionic liquids;
• Environmental fate predictions;
• Air half-life predictions;
• Biodegradation half-life predictions;
• Bioconcentration modeling;
• Biomagnification modeling;
• Soil ecotoxicity predictions;
• Avian toxicity predictions;
• Interspecies ecotoxicity correlations.

Both original research and review articles are welcome on any of the topics listed above as well as those detailing applications of QSAR/RA/RASAR.

Prof. Dr. Kunal Roy
Guest Editor

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