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Environmental Toxicology
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Environmental Toxicology

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Cross-Field Chemistry".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 15776

Special Issue Editor

Prof. Dr. Emilio Benfenati

E-Mail Website
Guest Editor
Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
Interests: In silico models; read across; regulatory application; computational toxicology; prioritization; weight of evidence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The adverse effects of chemical substances on the environment are a multifaceted problem, not only from an ecotoxicological point of view but also from multiple other perspectives, involving human populations. This Special Issue will collect contributions on state-of-the-art research, with particular attention to studies modeling ecotoxicological properties, environmental fate and behavior, propagation of the effects of contaminants towards human beings, integration of models for hazard and exposure, and the mixture effect. The challenge to minimize the impact of contaminants may benefit from in silico modeling, and your ideas will contribute.

Prof. Dr. Emilio Benfenati
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

  • Modeling ecotoxicological endpoints
  • Modeling environmental properties
  • Prioritization of contaminants
  • Proposing greener substances
  • Mixture effects
  • Integrated modeling for risk assessment

Published Papers (6 papers)

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Research

19 pages, 15957 KiB  
Article
Pesticides Burden in Neotropical Rivers: Costa Rica as a Case Study
by Silvia Echeverría-Sáenz, Manuel Spínola-Parallada and Ana Cristina Soto
Molecules 2021, 26(23), 7235; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26237235 - 29 Nov 2021
Cited by 8 | Viewed by 2427
Abstract
Neotropical ecosystems are highly biodiverse; however, the excessive use of pesticides has polluted freshwaters, with deleterious effects on aquatic biota. This study aims to analyze concentrations of active ingredients (a.i) of pesticides and the risks posed to freshwater Neotropical ecosystems. We compiled information [...] Read more.
Neotropical ecosystems are highly biodiverse; however, the excessive use of pesticides has polluted freshwaters, with deleterious effects on aquatic biota. This study aims to analyze concentrations of active ingredients (a.i) of pesticides and the risks posed to freshwater Neotropical ecosystems. We compiled information from 1036 superficial water samples taken in Costa Rica between 2009 and 2019. We calculated the detection frequency for 85 a.i. and compared the concentrations with international regulations. The most frequently detected pesticides were diuron, ametryn, pyrimethanil, flutolanil, diazinon, azoxystrobin, buprofezin, and epoxiconazole, with presence in >20% of the samples. We observed 32 pesticides with concentrations that exceeded international regulations, and the ecological risk to aquatic biota (assessed using the multi-substance potentially affected fraction model (msPAF)) revealed that 5% and 13% of the samples from Costa Rica pose a high or moderate acute risk, especially to primary producers and arthropods. Other Neotropical countries are experiencing the same trend with high loads of pesticides and consequent high risk to aquatic ecosystems. This information is highly valuable for authorities dealing with prospective and retrospective risk assessments for regulatory decisions in tropical countries. At the same time, this study highlights the need for systematic pesticide residue monitoring of fresh waters in the Neotropical region. Full article
(This article belongs to the Special Issue Environmental Toxicology)
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12 pages, 666 KiB  
Article
New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments
by Cosimo Toma, Claudia I. Cappelli, Alberto Manganaro, Anna Lombardo, Jürgen Arning and Emilio Benfenati
Molecules 2021, 26(22), 6983; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26226983 - 19 Nov 2021
Cited by 14 | Viewed by 2706
Abstract
To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need [...] Read more.
To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software. Full article
(This article belongs to the Special Issue Environmental Toxicology)
9 pages, 640 KiB  
Article
Nano-SAR Modeling for Predicting the Cytotoxicity of Metal Oxide Nanoparticles to PaCa2
by Haihua Shi, Yong Pan, Fan Yang, Jiakai Cao, Xinlong Tan, Beilei Yuan and Juncheng Jiang
Molecules 2021, 26(8), 2188; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26082188 - 10 Apr 2021
Cited by 7 | Viewed by 1935
Abstract
Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding [...] Read more.
Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials. Full article
(This article belongs to the Special Issue Environmental Toxicology)
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17 pages, 1306 KiB  
Article
Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools
by Diego Baderna, Roberta Faoro, Gianluca Selvestrel, Adrien Troise, Davide Luciani, Sandrine Andres and Emilio Benfenati
Molecules 2021, 26(7), 1928; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26071928 - 30 Mar 2021
Cited by 1 | Viewed by 2222
Abstract
Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A [...] Read more.
Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a “One Health” perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed. Full article
(This article belongs to the Special Issue Environmental Toxicology)
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12 pages, 975 KiB  
Article
QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors
by Cosimo Toma, Alberto Manganaro, Giuseppa Raitano, Marco Marzo, Domenico Gadaleta, Diego Baderna, Alessandra Roncaglioni, Nynke Kramer and Emilio Benfenati
Molecules 2021, 26(1), 127; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26010127 - 29 Dec 2020
Cited by 12 | Viewed by 3457
Abstract
Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure–activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification [...] Read more.
Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure–activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online. Full article
(This article belongs to the Special Issue Environmental Toxicology)
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23 pages, 2354 KiB  
Article
Activity and Diversity of Microorganisms in Root Zone of Plant Species Spontaneously Inhabiting Smelter Waste Piles
by Sylwia Siebielec, Grzegorz Siebielec, Piotr Sugier, Małgorzata Woźniak, Jarosław Grządziel, Anna Gałązka and Tomasz Stuczyński
Molecules 2020, 25(23), 5638; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25235638 - 30 Nov 2020
Cited by 17 | Viewed by 2130
Abstract
The aim was to assess plant driven changes in the activity and diversity of microorganisms in the top layer of the zinc and lead smelter waste piles. The study sites comprised two types (flotation waste—FW and slag waste—SW) of smelter waste deposits in [...] Read more.
The aim was to assess plant driven changes in the activity and diversity of microorganisms in the top layer of the zinc and lead smelter waste piles. The study sites comprised two types (flotation waste—FW and slag waste—SW) of smelter waste deposits in Piekary Slaskie, Poland. Cadmium, zinc, lead, and arsenic contents in these technosols were extremely high. The root zone of 8 spontaneous plant species (FW—Thymus serpyllum, Silene vulgaris, Solidago virgaurea, Echium vulgare, and Rumex acetosa; and SW—Verbascum thapsus; Solidago gigantea, Eupatorium cannabinum) and barren areas of each waste deposit were sampled. We observed a significant difference in microbial characteristics attributed to different plant species. The enzymatic activity was mostly driven by plant-microbial interactions and it was significantly greater in soil affected by plants than in bulk soil. Furthermore, as it was revealed by BIOLOG Ecoplate analysis, microorganisms inhabiting barren areas of the waste piles rely on significantly different sources of carbon than those found in the zone affected by spontaneous plants. Among phyla, Actinobacteriota were the most abundant, contributing to at least 25% of the total abundance. Bacteria belonging to Blastococcus genera were the most abundant with the substantial contribution of Nocardioides and Pseudonocardia, especially in the root zone. The contribution of unclassified bacteria was high—up to 38% of the total abundance. This demonstrates the unique character of bacterial communities in the smelter waste. Full article
(This article belongs to the Special Issue Environmental Toxicology)
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