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Liu G, Li X, Guo Y, Zhang L, Liu H, Ai H. Ensemble multiclassification model for predicting developmental toxicity in zebrafish. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2024; 271:106936. [PMID: 38723470 DOI: 10.1016/j.aquatox.2024.106936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/21/2024]
Abstract
In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.
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Affiliation(s)
- Gaohua Liu
- College of Life Science, Liaoning University, Shenyang, 110036, China
| | - Xinran Li
- College of Life Science, Liaoning University, Shenyang, 110036, China
| | - Yaxu Guo
- College of Life Science, Liaoning University, Shenyang, 110036, China
| | - Li Zhang
- College of Life Science, Liaoning University, Shenyang, 110036, China; China Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, China
| | - Hongsheng Liu
- College of Life Science, Liaoning University, Shenyang, 110036, China; China Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, China
| | - Haixin Ai
- College of Life Science, Liaoning University, Shenyang, 110036, China; China Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, China.
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2
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Daneshmand M, SalarAmoli J, BaghbanZadeh N. A QSAR study for predicting malformation in zebrafish embryo. Toxicol Mech Methods 2024:1-7. [PMID: 38586962 DOI: 10.1080/15376516.2024.2338907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/30/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure-activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. METHODS The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew's correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. RESULTS A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. CONCLUSIONS The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.
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Affiliation(s)
- Mahsa Daneshmand
- Department of Comparative Bioscience, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Jamileh SalarAmoli
- Department of Comparative Bioscience, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
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3
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Cao C, Wang H, Yang JR, Chen Q, Guo YM, Chen JZ. MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity. Comput Biol Med 2024; 171:108037. [PMID: 38377716 DOI: 10.1016/j.compbiomed.2024.108037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/21/2023] [Accepted: 01/26/2024] [Indexed: 02/22/2024]
Abstract
The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a critical role for it to bind in the target. It is a big issue to figure out the bioactive conformation in deep learning without the co-crystal structure or highly precise molecular simulations. In this study, we developed a deep learning framework of Multi-Conformation Point Network (MCPNET) to construct classification and regression models, respectively, based on electrostatic potential distributions on vdW surfaces around multiple conformations of the compound using a dataset of compounds with developmental toxicity in zebrafish embryo. MCPNET applied 3D multi-conformational surface point cloud to extract the molecular features for model training, which may be critical for capturing the structural diversity of compounds. The models achieved an accuracy of 85 % on the classification task and R2 of 0.66 on the regression task, outperforming traditional machine learning models and other deep learning models. The key feature of our model is its interpretability with the component visualization to identify the factors contributing to the prediction and to understand the compound action mechanism. MCPNET may predict the conformation quietly close to the bioactive conformation of a compound by attention-based multi-conformation pooling mechanism. Our results demonstrated the potential of deep learning based on 3D molecular representations in accurately predicting developmental toxicity. The source code is publicly available at https://github.com/Superlit-CC/MCPNET.
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Affiliation(s)
- Cheng Cao
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China; Polytechnic Institute, Zhejiang University, 269 Shixiang Rd, Hangzhou, Zhejiang, 310015, China
| | - Hao Wang
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China
| | - Jin-Rong Yang
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China; Polytechnic Institute, Zhejiang University, 269 Shixiang Rd, Hangzhou, Zhejiang, 310015, China
| | - Qiang Chen
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China
| | - Ya-Min Guo
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China
| | - Jian-Zhong Chen
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China.
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4
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Zhao W, Chen Y, Hu N, Long D, Cao Y. The uses of zebrafish (Danio rerio) as an in vivo model for toxicological studies: A review based on bibliometrics. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 272:116023. [PMID: 38290311 DOI: 10.1016/j.ecoenv.2024.116023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/20/2024] [Accepted: 01/24/2024] [Indexed: 02/01/2024]
Abstract
An in vivo model is necessary for toxicology. This review analyzed the uses of zebrafish (Danio rerio) in toxicology based on bibliometrics. Totally 56,816 publications about zebrafish from 2002 to 2023 were found in Web of Science Core Collection, with Toxicology as the top 6 among all disciplines. Accordingly, the bibliometric map reveals that "toxicity" has become a hot keyword. It further reveals that the most common exposure types include acute, chronic, and combined exposure. The toxicological effects include behavioral, intestinal, cardiovascular, hepatic, endocrine toxicity, neurotoxicity, immunotoxicity, genotoxicity, and reproductive and transgenerational toxicity. The mechanisms include oxidative stress, inflammation, autophagy, and dysbiosis of gut microbiota. The toxicants commonly evaluated by using zebrafish model include nanomaterials, arsenic, metals, bisphenol, and dioxin. Overall, zebrafish provide a unique and well-accepted model to investigate the toxicological effects and mechanisms. We also discussed the possible ways to address some of the limitations of zebrafish model, such as the combination of human organoids to avoid species differences.
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Affiliation(s)
- Weichao Zhao
- Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, PR China
| | - Yuna Chen
- Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, PR China
| | - Nan Hu
- Key Discipline Laboratory for National Defense for Biotechnology in Uranium Mining and Hydrometallurgy, University of South China, Hengyang 421001, PR China.
| | - Dingxin Long
- Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, PR China.
| | - Yi Cao
- Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, PR China.
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Vittoria Togo M, Mastrolorito F, Orfino A, Graps EA, Tondo AR, Altomare CD, Ciriaco F, Trisciuzzi D, Nicolotti O, Amoroso N. Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives. Expert Opin Drug Metab Toxicol 2023:1-17. [PMID: 38141160 DOI: 10.1080/17425255.2023.2298827] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/20/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.
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Affiliation(s)
- Maria Vittoria Togo
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Fabrizio Mastrolorito
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Angelica Orfino
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Elisabetta Anna Graps
- ARESS Puglia - Agenzia Regionale strategica per laSalute ed il Sociale, Presidenza della Regione Puglia", Bari, Italy
| | - Anna Rita Tondo
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Cosimo Damiano Altomare
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Fulvio Ciriaco
- Department of Chemistry, Universitá degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Daniela Trisciuzzi
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Orazio Nicolotti
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Nicola Amoroso
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
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6
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Béquignon OM, Gómez-Tamayo JC, Lenselink EB, Wink S, Hiemstra S, Lam CC, Gadaleta D, Roncaglioni A, Norinder U, Water BVD, Pastor M, van Westen GJP. Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells. J Chem Inf Model 2023; 63:5433-5445. [PMID: 37616385 PMCID: PMC10498489 DOI: 10.1021/acs.jcim.3c00220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Indexed: 08/26/2023]
Abstract
Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.
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Affiliation(s)
- Olivier
J. M. Béquignon
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Jose C. Gómez-Tamayo
- Research
Programme on Biomedical Informatics (GRIB), Department of Medicine
and Life Sciences, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Carrer del Dr. Aiguader 88, 08002 Barcelona, Spain
| | - Eelke B. Lenselink
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Steven Wink
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Steven Hiemstra
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Chi Chung Lam
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Domenico Gadaleta
- Laboratory
of Environmental Chemistry and Toxicology, Department of Environmental
Health Sciences, IRCCS—Istituto di
Ricerche Farmacologiche Mario Negri, Via la Masa 19, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Laboratory
of Environmental Chemistry and Toxicology, Department of Environmental
Health Sciences, IRCCS—Istituto di
Ricerche Farmacologiche Mario Negri, Via la Masa 19, 20156 Milano, Italy
| | - Ulf Norinder
- MTM
Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden
| | - Bob van de Water
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
| | - Manuel Pastor
- Research
Programme on Biomedical Informatics (GRIB), Department of Medicine
and Life Sciences, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Carrer del Dr. Aiguader 88, 08002 Barcelona, Spain
| | - Gerard J. P. van Westen
- Leiden
Academic Centre for Drug Research, Leiden
University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands
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7
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Kim C, Jeong J, Choi J. Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data. Chem Res Toxicol 2022; 35:2219-2226. [PMID: 36475638 DOI: 10.1021/acs.chemrestox.2c00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The development of toxicity classification models using the ToxCast database has been extensively studied. Machine learning approaches are effective in identifying the bioactivity of untested chemicals. However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. Thirty models established by combining five molecular fingerprints (i.e., Morgan, MACCS, RDKit, Pattern, and Layered) and six algorithms [i.e., gradient boosting tree, random forest (RF), multi-layered perceptron, k-nearest neighbor, logistic regression, and naive Bayes] were trained using the selected assay data set. Of the 30 trained models, MACCS-RF showed the best performance and thus was selected for analyses of the effects of CI and DS. Results showed that recall and F1 were significantly lower when training with the CI assays than with the DS assays. In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. This study also emphasized the importance of using appropriate evaluation metrics and tuning hyperparameters in model development.
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Affiliation(s)
- Changhun Kim
- Chemical Bigdata Research Center, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.,School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea
| | - Jaeseong Jeong
- Chemical Bigdata Research Center, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.,School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea
| | - Jinhee Choi
- Chemical Bigdata Research Center, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.,School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea
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8
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Delre P, Lavado GJ, Lamanna G, Saviano M, Roncaglioni A, Benfenati E, Mangiatordi GF, Gadaleta D. Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques. Front Pharmacol 2022; 13:951083. [PMID: 36133824 PMCID: PMC9483173 DOI: 10.3389/fphar.2022.951083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.
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Affiliation(s)
- Pietro Delre
- CNR—Institute of Crystallography, Bari, Italy
- Chemistry Department, University of Bari “Aldo Moro”, Bari, Italy
| | - Giovanna J. Lavado
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Giuseppe Lamanna
- CNR—Institute of Crystallography, Bari, Italy
- Chemistry Department, University of Bari “Aldo Moro”, Bari, Italy
| | | | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Giuseppe Felice Mangiatordi
- CNR—Institute of Crystallography, Bari, Italy
- *Correspondence: Giuseppe Felice Mangiatordi, ; Domenico Gadaleta,
| | - Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
- *Correspondence: Giuseppe Felice Mangiatordi, ; Domenico Gadaleta,
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9
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Zhao L, Zhou M, Zhao Y, Yang J, Pu Q, Yang H, Wu Y, Lyu C, Li Y. Potential Toxicity Risk Assessment and Priority Control Strategy for PAHs Metabolism and Transformation Behaviors in the Environment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10972. [PMID: 36078713 PMCID: PMC9517862 DOI: 10.3390/ijerph191710972] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/25/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
In this study, 16 PAHs were selected as the priority control pollutants to summarize their environmental metabolism and transformation processes, including photolysis, plant degradation, bacterial degradation, fungal degradation, microalgae degradation, and human metabolic transformation. Meanwhile, a total of 473 PAHs by-products generated during their transformation and degradation in different environmental media were considered. Then, a comprehensive system was established for evaluating the PAHs by-products' neurotoxicity, immunotoxicity, phytotoxicity, developmental toxicity, genotoxicity, carcinogenicity, and endocrine-disrupting effect through molecular docking, molecular dynamics simulation, 3D-QSAR model, TOPKAT method, and VEGA platform. Finally, the potential environmental risk (phytotoxicity) and human health risks (neurotoxicity, immunotoxicity, genotoxicity, carcinogenicity, developmental toxicity, and endocrine-disrupting toxicity) during PAHs metabolism and transformation were comprehensively evaluated. Among the 473 PAH's metabolized and transformed products, all PAHs by-products excluding ACY, CHR, and DahA had higher neurotoxicity, 152 PAHs by-products had higher immunotoxicity, and 222 PAHs by-products had higher phytotoxicity than their precursors during biological metabolism and environmental transformation. Based on the TOPKAT model, 152 PAH by-products possessed potential developmental toxicity, and 138 PAH by-products had higher genotoxicity than their precursors. VEGA predicted that 247 kinds of PAH derivatives had carcinogenic activity, and only the natural transformation products of ACY did not have carcinogenicity. In addition to ACY, 15 PAHs produced 123 endocrine-disrupting substances during metabolism and transformation. Finally, the potential environmental and human health risks of PAHs metabolism and transformation products were evaluated using metabolic and transformation pathway probability and degree of toxic risk as indicators. Accordingly, the priority control strategy for PAHs was constructed based on the risk entropy method by screening the priority control pathways. This paper assesses the potential human health and environmental risks of PAHs in different environmental media with the help of models and toxicological modules for the toxicity prediction of PAHs by-products, and thus designs a risk priority control evaluation system for PAHs.
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Affiliation(s)
- Lei Zhao
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Mengying Zhou
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Yuanyuan Zhao
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Jiawen Yang
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Qikun Pu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Hao Yang
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Yang Wu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Cong Lyu
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Yu Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
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10
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Jeong J, Choi J. Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7532-7543. [PMID: 35666838 DOI: 10.1021/acs.est.1c07413] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.
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Affiliation(s)
- Jaeseong Jeong
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, South Korea
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, South Korea
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Saavedra LM, Duchowicz PR. Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148820. [PMID: 34328907 DOI: 10.1016/j.scitotenv.2021.148820] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/11/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (Rtrain2 = 0.64, RMSEtrain = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (Rtest2,Rm2, QF12, QF22, QF32 and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.
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Affiliation(s)
- Laura M Saavedra
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina.
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina.
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Lotfi S, Ahmadi S, Kumar P. A hybrid descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116465] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Lovrić M, Malev O, Klobučar G, Kern R, Liu JJ, Lučić B. Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem. Molecules 2021; 26:1617. [PMID: 33803931 PMCID: PMC7998177 DOI: 10.3390/molecules26061617] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/03/2021] [Accepted: 03/11/2021] [Indexed: 02/06/2023] Open
Abstract
The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental effects occurring in dechorionated zebrafish embryos for 120 hours post fertilization and monitored via gross malformations and mortality. We report the predictive capability of 209 quantitative structure-activity relationship (QSAR) models developed by machine learning methods using penalization techniques and diverse model quality metrics to cope with the imbalanced endpoints. All these QSAR models were generated to test how the imbalanced classification (toxic or non-toxic) endpoints could be predicted regardless which of three algorithms is used: logistic regression, multi-layer perceptron, or random forests. Additionally, QSAR toxicity models are developed starting from sets of classical molecular descriptors, structural fingerprints and their combinations. Only 8 out of 209 models passed the 0.20 Matthew's correlation coefficient value defined a priori as a threshold for acceptable model quality on the test sets. The best models were obtained for endpoints mortality (MORT), ActivityScore and JAW (deformation). The low predictability of the QSAR model developed from the zebrafish embryotoxicity data in the database is mainly due to a higher sensitivity of 19 measurements of endpoints carried out on dechorionated embryos at low concentrations.
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Affiliation(s)
- Mario Lovrić
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (R.K.)
- Ruđer Bošković Institute, P.O. Box 180, 10002 Zagreb, Croatia;
| | - Olga Malev
- Ruđer Bošković Institute, P.O. Box 180, 10002 Zagreb, Croatia;
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia;
| | - Göran Klobučar
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia;
| | - Roman Kern
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (R.K.)
- Institute of Interactive Systems and Data Science, TU Graz, Inffeldgasse 16c, 8010 Graz, Austria
| | - Jay J. Liu
- Department of Chemical Engineering, Pukyong National University, Busan 608-739, Korea
| | - Bono Lučić
- Ruđer Bošković Institute, P.O. Box 180, 10002 Zagreb, Croatia;
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Giangreco NP, Elias JE, Tatonetti NP. No population left behind: Improving paediatric drug safety using informatics and systems biology. Br J Clin Pharmacol 2020; 88:1464-1470. [PMID: 33332641 PMCID: PMC8209126 DOI: 10.1111/bcp.14705] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 10/26/2020] [Accepted: 12/05/2020] [Indexed: 12/12/2022] Open
Abstract
Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.
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Affiliation(s)
- Nicholas P Giangreco
- Department of Biomedical Informatics and Systems Biology, Columbia University, New York, NY, USA
| | - Jonathan E Elias
- Department of Pediatrics, Instructor in Pediatrics, Assistant Medical Director of Information Services, Weill Cornell Medical & NYP Weill Cornell Medical Center, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics and Systems Biology, Columbia University, New York, NY, USA
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