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Toxics
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Advances in Computational Toxicology and Their Exposure
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Advances in Computational Toxicology and Their Exposure

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 2595

Special Issue Editors

Dr. Andrey A. Toropov

E-Mail Website
Guest Editor
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
Interests: quantitative structure-property/activity relationships (QSPR/QSAR); analysis of nano-materials; drug discovery; applications of QSPR/QSAR in toxicology, ecology
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,

In anticipation of the future, many wish to influence both the date of its arrival and its content. This Special Issue aims to be a tool with which to influence the future of computational toxicology. Reports on any new significant aspects of computational toxicology are welcome. These may be models of different endpoints related to toxicology. Endpoints related to ecotoxicology are also subjects of study and discussion in this Special Issue.

The search for new ways of systematization and architecture of databases under construction, as well as attempts to find standards for development corresponding software for the implementation of the indicated tasks, will be critically considered with an emphasis on their practical implementation or their distribution and wide use. Comprehensive analyses of how one can approach the protection of public health, including consideration of both human and ecological risks, with the help of artificial-intelligence-provided machine monitoring for ecotoxicological events are welcome.

The development of systems for recording the toxicity and ecotoxicity of nanomaterials can be a a particularly forward-looking development of the results collected in this Special Issue.

Dr. Andrey A. Toropov
Dr. Alla P. Toropova
Guest Editors

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

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18 pages, 313 KiB  
Article
Semi-Correlations for Building Up a Simulation of Eye Irritation
by Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni and Emilio Benfenati
Toxics 2023, 11(12), 993; https://doi.org/10.3390/toxics11120993 - 6 Dec 2023
Viewed by 1337
Abstract
The OECD recognizes that data on a compound’s ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, [...] Read more.
The OECD recognizes that data on a compound’s ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecule. The regression line is based on the descriptor values which serve to distribute the data into four classes: true positive, true negative, false positive, and false negative. These values are applied to calculate the corresponding statistical criterion for assessing the predictive potential of the categorical model. In our model, the descriptor is the sum of what are termed correlation weights. These are defined by optimization using the Monte Carlo method. The target function of the optimization is related to the determination coefficient and the mean absolute error for the training set. Our model gives results that are better than those previously reported for the same endpoint. Full article
(This article belongs to the Special Issue Advances in Computational Toxicology and Their Exposure)

Review

Jump to: Research

20 pages, 3030 KiB  
Review
Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy
by Ahrum Son, Jongham Park, Woojin Kim, Yoonki Yoon, Sangwoon Lee, Jaeho Ji and Hyunsoo Kim
Toxics 2024, 12(11), 822; https://doi.org/10.3390/toxics12110822 - 16 Nov 2024
Viewed by 452
Abstract
It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics [...] Read more.
It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure–activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer. Full article
(This article belongs to the Special Issue Advances in Computational Toxicology and Their Exposure)
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