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Molecules
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Ecotoxicological Modeling and Environmental Risk Predictions Using In Silico Approaches
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Ecotoxicological Modeling and Environmental Risk Predictions Using In Silico Approaches

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6365

Special Issue Editor

Prof. Dr. Kunal Roy

E-Mail Website
Guest Editor
Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
Interests: QSAR and molecular modeling; cheminformatics; drug design; ecotoxicological modeling

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ecotoxicological modeling
  • quantitative structure–activity relationships (QSARs)
  • read-across (RA)
  • read-across structure–activity relationships (RASAR)
  • risk assessment
  • environmental fate
  • biodegradation
  • biomagnification, chemometrics
  • cheminformatics

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Published Papers (2 papers)

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Research

13 pages, 2813 KiB  
Article
Computational Modeling of Human Serum Albumin Binding of Per- and Polyfluoroalkyl Substances Employing QSAR, Read-Across, and Docking
by Andrea Gallagher, Supratik Kar and Maria S. Sepúlveda
Molecules 2023, 28(14), 5375; https://doi.org/10.3390/molecules28145375 - 13 Jul 2023
Cited by 8 | Viewed by 3369
Abstract
Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals in widespread use that have been shown to be toxic to wildlife and humans. Human serum albumin (HSA) is a known transport protein that binds PFAS at various sites, leading to bioaccumulation and long-term toxicity. [...] Read more.
Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals in widespread use that have been shown to be toxic to wildlife and humans. Human serum albumin (HSA) is a known transport protein that binds PFAS at various sites, leading to bioaccumulation and long-term toxicity. In silico tools like quantitative structure-activity relationship (QSAR), read-across, and quantitative read-across structure-property relationship (q-RASPR) are proven techniques for modeling chemical toxicity based on experimental data which can be used to predict the toxicity of untested and new chemicals, while at the same time, help to identify the major features responsible for toxicity. Classification-based and regression-based QSAR models are employed in the present study to predict the binding affinities of 24 PFAS to HSA. Regression-based QSAR models revealed that the packing density index (PDI) and quantitative estimation of drug-likeness (QED) descriptors were both positively correlated with higher binding affinity, while the classification-based QSAR model showed the average connectivity index of order 4 (X4A) descriptor was inversely correlated with binding affinity. Whereas molecular docking studies suggested that PFAS with the highest binding affinity to HSA create hydrogen bonds with Arg348 and salt bridges with Arg348 and Arg485, PFAS with lower binding affinity either showed no interactions with either amino acid or only interactions with Arg348. Among the studied PFAS, perfluoroalkyl acids (PFAA) with large carbon chain length (>C10) have one of the lowest binding affinities, compared to PFAA with carbon chain length ranging from 7 to 9, which showed the highest affinity to HSA. Generalized Read-Across (GenRA) was used to predict toxicity outcomes for the top five highest binding affinity PFAS based on 10 structural analogs for each and found that all are predicted as being chronic to sub-chronically toxic to HSA. The developed in silico models presented in this work can provide a framework for designing PFAS alternatives, screening compounds currently in use, and for the study of PFAS mixture toxicity, which is an area of intense research. Full article
(This article belongs to the Special Issue Ecotoxicological Modeling and Environmental Risk Predictions Using In Silico Approaches)
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12 pages, 2152 KiB  
Article
Predictive Models of Gas/Particulate Partition Coefficients (KP) for Polycyclic Aromatic Hydrocarbons and Their Oxygen/Nitrogen Derivatives
by Qiang Wu, Siqi Cao, Zhenyi Chen, Xiaoxuan Wei, Guangcai Ma and Haiying Yu
Molecules 2022, 27(21), 7608; https://doi.org/10.3390/molecules27217608 - 6 Nov 2022
Cited by 1 | Viewed by 1901
Abstract
Polycyclic aromatic hydrocarbons (PAHs) and their oxygen/nitrogen derivatives released into the atmosphere can alternate between a gas phase and a particulate phase, further affecting their environmental behavior and fate. The gas/particulate partition coefficient (KP) is generally used to characterize such [...] Read more.
Polycyclic aromatic hydrocarbons (PAHs) and their oxygen/nitrogen derivatives released into the atmosphere can alternate between a gas phase and a particulate phase, further affecting their environmental behavior and fate. The gas/particulate partition coefficient (KP) is generally used to characterize such partitioning equilibrium. In this study, the correlation between log KP of fifty PAH derivatives and their n-octanol/air partition coefficient (log KOA) was first analyzed, yielding a strong linear correlation (R2 = 0.801). Then, Gaussian 09 software was used to calculate quantum chemical descriptors of all chemicals at M062X/6-311+G (d,p) level. Both stepwise multiple linear regression (MLR) and support vector machine (SVM) methods were used to develop the quantitative structure-property relationship (QSPR) prediction models of log KP. They yield better statistical performance (R2 > 0.847, RMSE < 0.584) than the log KOA model. Simulation external validation and cross validation were further used to characterize the fitting performance, predictive ability, and robustness of the models. The mechanism analysis shows intermolecular dispersion interaction and hydrogen bonding as the main factors to dominate the distribution of PAH derivatives between the gas phase and particulate phase. The developed models can be used to predict log KP values of other PAH derivatives in the application domain, providing basic data for their ecological risk assessment. Full article
(This article belongs to the Special Issue Ecotoxicological Modeling and Environmental Risk Predictions Using In Silico Approaches)
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