Toxics

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16 pages, 1374 KiB

Open AccessArticle

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

by Farina Tariq, Lutz Ahrens, Nikiforos A. Alygizakis, Karine Audouze, Emilio Benfenati, Pedro N. Carvalho, Ioana Chelcea, Spyros Karakitsios, Achilleas Karakoltzidis, Vikas Kumar, Liadys Mora Lagares, Dimosthenis Sarigiannis, Gianluca Selvestrel, Olivier Taboureau, Katrin Vorkamp and Patrik L. Andersson

Toxics 2024, 12(10), 736; https://doi.org/10.3390/toxics12100736 - 12 Oct 2024

Viewed by 988

Abstract

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. [...] Read more.

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically. 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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23 pages, 9770 KiB

Open AccessArticle

Health Risk Assessment of Heavy Metals in Agricultural Soils Based on Multi-Receptor Modeling Combined with Monte Carlo Simulation

by Yundong Wu, Yan Xia, Li Mu, Wenjie Liu, Qiuying Wang, Tianyan Su, Qiu Yang, Amani Milinga and Yanwei Zhang

Toxics 2024, 12(9), 643; https://doi.org/10.3390/toxics12090643 - 31 Aug 2024

Viewed by 1238

Abstract

The spatial characteristics, pollution sources, and risks of soil heavy metals were analyzed on Hainan Island. The results showed that the heavily polluted points accounted for 0.56%, and the number of mildly and above polluted points accounted for 15.27%, respectively, which were mainly [...] Read more.

The spatial characteristics, pollution sources, and risks of soil heavy metals were analyzed on Hainan Island. The results showed that the heavily polluted points accounted for 0.56%, and the number of mildly and above polluted points accounted for 15.27%, respectively, which were mainly distributed in the northern part of the study area. The principal component analysis–absolute principal component score–multiple linear regression (APCS-MLR) and the positive matrix factorization (PMF) revealed four sources of heavy metals: agricultural pollution sources for cadmium, (Cd), industrial and mining pollution sources for arsenic, (As), transportation pollution sources for zinc and lead (Zn and Pb), and natural pollution sources for chromium, nickel, and copper (Cr, Ni, and Cu). The human health risk assessment indicated that the average non-carcinogenic risk (HI) for both adults and children was within the safe threshold (<1), whereas Cr and Ni posed a carcinogenic risk (CR) to human health. In addition, the total non-carcinogenic risk (THI) indicated that heavy metals posed a potential non-carcinogenic risk to children, while the total carcinogenic risk (TCR) remained relatively high, mainly in the northern part of the study area. The results of the Monte Carlo simulation showed that the non-carcinogenic risk (HI) for all heavy metals was <1, but the total non-carcinogenic risk index (THI) for children was >1, indicating a potential health risk above the safe threshold. Meanwhile, nearly 100% and 99.94% of the TCR values exceeded 1 × 10⁻⁴ for children and adults, indicating that Cr and Ni are priority heavy metals for control. The research results provide the necessary scientific basis for the prevention and control of heavy metals in agricultural soils. 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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21 pages, 3448 KiB

Open AccessEditor’s ChoiceArticle

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

Cited by 2 | Viewed by 2316

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

Open AccessFeature PaperEditor’s ChoiceArticle

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 2 | Viewed by 2363

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

Open AccessArticle

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

Cited by 1 | Viewed by 1452

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

Open AccessArticle

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 4 | Viewed by 1648

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

Open AccessFeature PaperEditor’s ChoiceArticle

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 1972

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

Open AccessEditor’s ChoiceArticle

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 4 | Viewed by 1750

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 BMF_L 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 BMF_L 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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