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Toxics
Special Issues
Computational Toxicology: Exposure and Assessment
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Computational Toxicology: Exposure and Assessment

A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 890

Special Issue Editor

Dr. Alla P. Toropova

E-Mail Website
Guest Editor
Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
Interests: QSPR/QSAR; Monte Carlo method; nanoinformatics; toxicology; nanotoxicology; drug discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the need to develop models of physicochemical properties and biological activity is generally recognized. Toxicology is one of the main fields in need of such models. However, methods of obtaining them remain the subject of discussion, both in practical and epistemological terms. Questions about their applicability domain and mechanistic interpretation remain open. Estimating the representativeness of the support datasets to be used in developing such models is problem in and of itself. Fortunately, fresh proposals and novel developments for the above-mentioned list of practical and theoretical problems are being presented. Toxicity has a large number of varieties, and perhaps these varieties have only one thing in common: they are all dangerous to human health and the environment. All attempts thus far to systematize knowledge on toxicity remain incomplete. Moreover, molecular descriptors and advanced computational methods are the most widely used tools for modeling all kinds of toxicity. Almost all areas of mathematics are involved in toxicity modeling in one way or another. Graph theory, quantum chemistry, and molecular mechanics also find applications for the topic in question. Further, it should be noted that experimental contributions are no less important, as they are the basis for model development. This Special Issue aims to serve as a platform on which to showcase proposals and innovations from all of the above-mentioned areas of research.

Dr. Alla P. Toropova
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. Toxics is an international peer-reviewed open access monthly 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 2600 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

  • toxicology
  • mutagenicity
  • carcinogenicity
  • chronic toxicity
  • eco-toxicity
  • QSPR/QSAR
  • validation
  • risk assessment
  • new approach methodologies
  • artificial intelligence
  • Monte Carlo method
  • SMILES and quasi-SMILES
  • read across
  • mathematical toxicology
  • exposure

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Published Papers (2 papers)

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Research

18 pages, 3808 KiB  
Article
Physicochemical Exploration and Computational Analysis of Bone After Subchronic Exposure to Kalach 360 SL in Female Wistar Rats
by Latifa Hamdaoui, Hafedh El Feki, Marwa Ben Amor, Hassane Oudadesse, Riadh Badraoui, Naila Khalil, Faten Brahmi, Saoussen Jilani, Bandar Aloufi, Ibtissem Ben Amara and Tarek Rebai
Toxics 2025, 13(6), 456; https://doi.org/10.3390/toxics13060456 - 29 May 2025
Viewed by 237
Abstract
Glyphosate (N-phosphonomethylglycine) is a widely used organophosphorus herbicide that inhibits the shikimate pathway, a crucial metabolic route responsible for the synthesis of aromatic amino acids in plants and certain microorganisms. Due to its broad-spectrum activity, glyphosate serves as the main active ingredient in [...] Read more.
Glyphosate (N-phosphonomethylglycine) is a widely used organophosphorus herbicide that inhibits the shikimate pathway, a crucial metabolic route responsible for the synthesis of aromatic amino acids in plants and certain microorganisms. Due to its broad-spectrum activity, glyphosate serves as the main active ingredient in various commercial herbicide formulations, including Roundup and Kalach 360 SL (KL). It poses a health hazard to animals and humans due to its persistence in soil, water erosion, and crops. The aim of our study was to continue the previous research to explore the impact of KL on bone using physico-chemical parameters and in silico studies after exposing female wistar rats for 60 days. The in silico study concerned the assessment of binding affinity and molecular interactions using computational modeling approach. The rats were allocated into three experimental groups: group 1 (n = 6) served as controls, while groups 2 and 3 received low and high doses (Dose 1: 126 mg/Kg and Dose 2: 315 mg/Kg) of KL dissolved in water, respectively. All rats were sacrificed after 60 days of exposure. XRD and FTIR spectrum analysis of bone tissues in female rats showed significant histoarchitectural changes associated with bone mineralization disruption. Our results have demonstrated that sub-chronic exposure of adult female rats to KL causes bone rarefaction, as confirmed by a previous histological study. This physico-chemical study has further confirmed the harmful impact of KL on the crystalline fraction of bone tissue, composed of hydroxyapatite crystals. In addition, the computational analyses showed that glyphosate binds to 3 Glu form of osteocalcin (3 Glu-OCN) (4MZZ) and decarboxylated osteocalcin (8I75) with good affinities and strong molecular interactions, which justified and supported the in vivo findings. In conclusion, KL may interfere with hydroxyapatite and osteocalcin and, therefore, impair bone remodeling and metabolism. Full article
(This article belongs to the Special Issue Computational Toxicology: Exposure and Assessment)
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12 pages, 4160 KiB  
Article
Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions
by Benjamin Bajželj, Marjana Novič and Viktor Drgan
Toxics 2025, 13(5), 383; https://doi.org/10.3390/toxics13050383 - 9 May 2025
Viewed by 268
Abstract
Quantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches. Developing interpretable models for endpoint prediction is valuable as interpretable models may provide valuable insights into the relationship between molecular structure and [...] Read more.
Quantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches. Developing interpretable models for endpoint prediction is valuable as interpretable models may provide valuable insights into the relationship between molecular structure and observed biological or toxicological properties of compounds. In this study, we introduce a novel modification of counter-propagation artificial neural networks that aims to identify key molecular features responsible for classifying molecules into specific endpoint classes. The novel approach presented in this work dynamically adjusts molecular descriptor importance during model training, allowing different molecular descriptor importance values for structurally different molecules, which increases its adaptability to diverse sets of compounds. We applied the method to enzyme inhibition and hepatotoxicity classification datasets. Our findings show that the proposed approach improves the classification of molecules, reduces the number of neurons excited by molecules from different endpoint classes, and increases the number of acceptable models. The proposed approach may be useful in compound toxicity prediction and drug design studies. Full article
(This article belongs to the Special Issue Computational Toxicology: Exposure and Assessment)
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