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Toxics
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Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining...
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Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 9141

Special Issue Editors

Dr. Emilio Benfenati

E-Mail Website
Guest Editor
Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche “Mario Negri", 19 Via La Masa, I-20156 Milan, Italy
Interests: toxicity evaluation; in silico models; QSAR; prioritization
Special Issues, Collections and Topics in MDPI journals
Dr. Noel Aquilina

E-Mail Website
Guest Editor
Faculty of Science, University of Malta, 2080 Msida, Malta
Interests: measurement and modelling of personal exposure; machine learning algorithms applied to exposure; polycyclic aromatic hydrocarbons; tobacco specific nitrosamines; biomarkers

Special Issue Information

Dear Colleagues,

The needs of our society to cope with safety issues when exposed to a wide range of toxics are enormous. As more data are available on two fronts, namely, toxicity of more compounds and real-time/high frequency data, Artificial Intelligence (AI) can improve our understanding of how toxic compounds create harm and improve ways to provide solutions. Although more data are available today, the complex properties and the dispersion of toxics make it very difficult to deal and address them without suitable computer tools. AI and Data Mining (DM) represent not only a methodological approach, but also a way to define new strategies to address toxicology and safety. While experimental studies proceed in sequential steps, also following parsimony criteria, DM and AI tools are able to elucidate a better vision of the complex, toxicological problem in an unprecedented way.

We solicit manuscripts addressing the use of AI and DM dealing with toxicity and safety within the Special Issue on this topic. Human toxicology, ecotoxicology and environmental aspects are within the target of this Special Issue. Both manuscripts on the methodological aspects and on specific applications are welcome. We solicit manuscripts from research institutes, academia, but also industry, to describe the point of view and perspectives from different users. Public authorities are also welcome to contribute, since the novel approach is introducing advanced, alternative pathways, which contribute to the scientific topic but may require debate regarding their acceptance for regulatory purposes.

Dr. Emilio Benfenati
Dr. Noel Aquilina
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

  • data mining
  • artificial intelligence
  • real-time data
  • toxicology
  • toxicity
  • environment
  • exposure
  • risk assessment
  • safety

Published Papers (6 papers)

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Research

21 pages, 3448 KiB  
Article
Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks
by Edoardo Luca Viganò, Davide Ballabio and Alessandra Roncaglioni
Toxics 2024, 12(1), 87; https://doi.org/10.3390/toxics12010087 - 19 Jan 2024
Viewed by 1415
Abstract
Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health. The same chemical can induce cardiotoxicity in different ways, following various [...] Read more.
Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health. The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today’s advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure–Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint. We then increased the complexity of encoders, incorporating graph-based methods, auto-encoders, and character embeddings employed in neural language processing. We also developed a multimodal neural network architecture, capable of considering the complementary nature of different chemical representations simultaneously. The potential of this approach, compared to more conventional architectures designed to handle a single encoder, becomes apparent when the amount of data increases. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences)
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25 pages, 12588 KiB  
Article
Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity
by Alexander D. Kalian, Emilio Benfenati, Olivia J. Osborne, David Gott, Claire Potter, Jean-Lou C. M. Dorne, Miao Guo and Christer Hogstrand
Toxics 2023, 11(7), 572; https://doi.org/10.3390/toxics11070572 - 30 Jun 2023
Cited by 1 | Viewed by 1653
Abstract
Dimensionality reduction techniques are crucial for enabling deep learning driven quantitative structure-activity relationship (QSAR) models to navigate higher dimensional toxicological spaces, however the use of specific techniques is often arbitrary and poorly explored. Six dimensionality techniques (both linear and non-linear) were hence applied [...] Read more.
Dimensionality reduction techniques are crucial for enabling deep learning driven quantitative structure-activity relationship (QSAR) models to navigate higher dimensional toxicological spaces, however the use of specific techniques is often arbitrary and poorly explored. Six dimensionality techniques (both linear and non-linear) were hence applied to a higher dimensionality mutagenicity dataset and compared in their ability to power a simple deep learning driven QSAR model, following grid searches for optimal hyperparameter values. It was found that comparatively simpler linear techniques, such as principal component analysis (PCA), were sufficient for enabling optimal QSAR model performances, which indicated that the original dataset was at least approximately linearly separable (in accordance with Cover’s theorem). However certain non-linear techniques such as kernel PCA and autoencoders performed at closely comparable levels, while (especially in the case of autoencoders) being more widely applicable to potentially non-linearly separable datasets. Analysis of the chemical space, in terms of XLogP and molecular weight, uncovered that the vast majority of testing data occurred within the defined applicability domain, as well as that certain regions were measurably more problematic and antagonised performances. It was however indicated that certain dimensionality reduction techniques were able to facilitate uniquely beneficial navigations of the chemical space. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences)
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14 pages, 3023 KiB  
Article
Predictive Models for Compound Binding to Androgen and Estrogen Receptors Based on Counter-Propagation Artificial Neural Networks
by Mark Stanojević, Marija Sollner Dolenc and Marjan Vračko
Toxics 2023, 11(6), 486; https://doi.org/10.3390/toxics11060486 - 26 May 2023
Viewed by 1107
Abstract
Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with the normal function of the human endocrine system. These chemicals can affect specific nuclear receptors, such as androgen receptors (ARs) or estrogen receptors (ER) α and β, which play a crucial role in regulating [...] Read more.
Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with the normal function of the human endocrine system. These chemicals can affect specific nuclear receptors, such as androgen receptors (ARs) or estrogen receptors (ER) α and β, which play a crucial role in regulating complex physiological processes in humans. It is now more crucial than ever to identify EDCs and reduce exposure to them. For screening and prioritizing chemicals for further experimentation, the use of artificial neural networks (ANN), which allow the modeling of complicated, nonlinear relationships, is most appropriate. We developed six models that predict the binding of a compound to ARs, ERα, or ERβ as agonists or antagonists, using counter-propagation artificial neural networks (CPANN). Models were trained on a dataset of structurally diverse compounds, and activity data were obtained from the CompTox Chemicals Dashboard. Leave-one-out (LOO) tests were performed to validate the models. The results showed that the models had excellent performance with prediction accuracy ranging from 94% to 100%. Therefore, the models can predict the binding affinity of an unknown compound to the selected nuclear receptor based solely on its chemical structure. As such, they represent important alternatives for the safety prioritization of chemicals. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences)
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8 pages, 645 KiB  
Article
The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity
by Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni and Emilio Benfenati
Toxics 2023, 11(5), 419; https://doi.org/10.3390/toxics11050419 - 29 Apr 2023
Cited by 3 | Viewed by 1342
Abstract
Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the [...] Read more.
Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)—inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set). Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences)
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9 pages, 769 KiB  
Article
CORAL Models for Drug-Induced Nephrotoxicity
by Andrey A. Toropov, Devon A. Barnes, Alla P. Toropova, Alessandra Roncaglioni, Alasdair R. Irvine, Rosalinde Masereeuw and Emilio Benfenati
Toxics 2023, 11(4), 293; https://doi.org/10.3390/toxics11040293 - 23 Mar 2023
Cited by 1 | Viewed by 1478
Abstract
Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to [...] Read more.
Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to avoid drug-induced kidney injuries. Computational predictions of drug-induced nephrotoxicity are an attractive approach to facilitate such an assessment and such models could serve as robust and reliable replacements for animal testing. To provide the chemical information for computational prediction, we used the convenient and common SMILES format. We examined several versions of so-called optimal SMILES-based descriptors. We obtained the highest statistical values, considering the specificity, sensitivity and accuracy of the prediction, by applying recently suggested atoms pairs proportions vectors and the index of ideality of correlation, which is a special statistical measure of the predictive potential. Implementation of this tool in the drug development process might lead to safer drugs in the future. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences)
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15 pages, 1204 KiB  
Article
QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish
by Linda Bertato, Nicola Chirico and Ester Papa
Toxics 2023, 11(3), 209; https://doi.org/10.3390/toxics11030209 - 23 Feb 2023
Cited by 1 | Viewed by 1332
Abstract
Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks [...] Read more.
Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMFL values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMFL in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Artificial Intelligence and Data Mining for Toxicological Sciences)
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