1
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Zhao X, Li C, Zeng H, Gu X, Zheng J. Determination of nitrobenzene potential genotoxic impurities in nifedipine with GC-MS. J Pharm Biomed Anal 2024; 248:116274. [PMID: 38852298 DOI: 10.1016/j.jpba.2024.116274] [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: 04/15/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
There is an increasing scientific interest in the detection of genotoxic impurities (GTIs), with nitrobenzene compounds being considered potential genotoxic impurities due to their structural alerts, which demonstrates a threat to drug safety for patient. While current reports on the detection of nifedipine impurity primarily focus on general impurities in nifedipine. In this study, an effective and simple gas chromatography-mass spectrometry (GC-MS) method was established and verified for the separation and quantification of 2-nitrotoluene, 2-nitrobenzyl alcohol, 2-nitrobenzaldehyde, 3-nitrobenzaldehyde, 4-nitrobenzaldehyde, and 2-nitrobenzyl bromide in nifedipine, which have not been previously reported. The validation of this GC-MS method was conducted following the International Conference of Harmonization (ICH) guidelines, exhibiting good linearity within the range of 2-40 μg/g and accuracy between 84.6 % and 107.8 %, the RSD% of intra-day and inter-day precision was in the range of 1.77-4.55 %, stability and robustness also met acceptance criteria. This method filled the gap in detection method for nitrobenzene compounds in nifedipine, offering a novel method and technical support for nifedipine quality control.
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Affiliation(s)
- Xinyu Zhao
- Zhejiang University of Technology, Hangzhou 310014, China
| | - Caixia Li
- Zhejiang Institute for Food and Drug Control, Hangzhou 310052, China; Key Laboratory for Core Technology of Generic Drug Evaluation National Medical Product Administration, Key Laboratory of Drug Contacting Materials Quality Control of Zhejiang Provincial, Hangzhou 310052, China
| | - Hongxia Zeng
- Zhejiang Institute for Food and Drug Control, Hangzhou 310052, China; Key Laboratory for Core Technology of Generic Drug Evaluation National Medical Product Administration, Key Laboratory of Drug Contacting Materials Quality Control of Zhejiang Provincial, Hangzhou 310052, China
| | - Xiao Gu
- Zhejiang Institute for Food and Drug Control, Hangzhou 310052, China; Key Laboratory for Core Technology of Generic Drug Evaluation National Medical Product Administration, Key Laboratory of Drug Contacting Materials Quality Control of Zhejiang Provincial, Hangzhou 310052, China
| | - Jinqi Zheng
- Zhejiang Institute for Food and Drug Control, Hangzhou 310052, China; Key Laboratory for Core Technology of Generic Drug Evaluation National Medical Product Administration, Key Laboratory of Drug Contacting Materials Quality Control of Zhejiang Provincial, Hangzhou 310052, China.
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2
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Zhang L, Li M, Zhang D, Yue W, Qian Z. Prioritizing of potential environmental exposure carcinogens beyond IARC group 1-2B based on weight of evidence (WoE) approach. Regul Toxicol Pharmacol 2024; 150:105646. [PMID: 38777300 DOI: 10.1016/j.yrtph.2024.105646] [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: 02/09/2024] [Revised: 05/12/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
Environmental exposures are the main cause of cancer, and their carcinogenicity has not been fully evaluated, identifying potential carcinogens that have not been evaluated is critical for safety. This study is the first to propose a weight of evidence (WoE) approach based on computational methods to prioritize potential carcinogens. Computational methods such as read across, structural alert, (Quantitative) structure-activity relationship and chemical-disease association were evaluated and integrated. Four different WoE approach was evaluated, compared to the best single method, the WoE-1 approach gained 0.21 and 0.39 improvement in the area under the receiver operating characteristic curve (AUC) and Matthew's correlation coefficient (MCC) value, respectively. The evaluation of 681 environmental exposures beyond IARC list 1-2B prioritized 52 chemicals of high carcinogenic concern, of which 21 compounds were known carcinogens or suspected carcinogens, and eight compounds were identified as potential carcinogens for the first time. This study illustrated that the WoE approach can effectively complement different computational methods, and can be used to prioritize chemicals of carcinogenic concern.
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Affiliation(s)
- Lu Zhang
- Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China; Tianjin Key Laboratory of Pathogenic Microbiology of Infectious Disease, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China
| | - Min Li
- Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China; Tianjin Key Laboratory of Pathogenic Microbiology of Infectious Disease, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China
| | - Dalong Zhang
- Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China
| | - Wenbo Yue
- Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China
| | - Zhiyong Qian
- Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China.
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3
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Chauhan SS, Garg P, Parthasarathi R. Computational framework for identifying and evaluating mutagenic and xenoestrogenic potential of food additives. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134233. [PMID: 38603913 DOI: 10.1016/j.jhazmat.2024.134233] [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: 11/13/2023] [Revised: 03/23/2024] [Accepted: 04/05/2024] [Indexed: 04/13/2024]
Abstract
Food additives are chemicals incorporated in food to enhance its flavor, color and prevent spoilage. Some of these are associated with substantial health hazards, including developmental disorders, increase cancer risk, and hormone disruption. Hence, this study aimed to comprehend the in-silico toxicology framework for evaluating mutagenic and xenoestrogenic potential of food additives and their association with breast cancer. A total of 2885 food additives were screened for toxicity based on Threshold of Toxicological Concern (TTC), mutagenicity endpoint prediction, and mutagenic structural alerts/toxicophores identification. Ten food additives were identified as having mutagenic potential based on toxicity screening. Furthermore, Protein-Protein Interaction (PPI) analysis identified ESR1, as a key hub gene in breast cancer. KEGG pathway analysis verified that ESR1 plays a significant role in breast cancer pathogenesis. Additionally, competitive interaction studies of the predicted potential mutagenic food additives with the estrogen receptor-α were evaluated at agonist and antagonist binding sites. Indole, Dichloromethane, Trichloroethylene, Quinoline, 6-methyl quinoline, Ethyl nitrite, and 4-methyl quinoline could act as agonists, and Paraldehyde, Azodicarbonamide, and 2-acetylfuranmay as antagonists. The systematic risk assessment framework reported in this study enables the exploration of mutagenic and xenoestrogenic potential associated with food additives for hazard identification and management.
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Affiliation(s)
- Shweta Singh Chauhan
- Computational Toxicology Facility, Toxicoinformatics & Industrial Research, CSIR, Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow, Uttar Pradesh 226001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Prekshi Garg
- Computational Toxicology Facility, Toxicoinformatics & Industrial Research, CSIR, Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow, Uttar Pradesh 226001, India
| | - Ramakrishnan Parthasarathi
- Computational Toxicology Facility, Toxicoinformatics & Industrial Research, CSIR, Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow, Uttar Pradesh 226001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India.
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4
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Hu XM, Hou YY, Teng XR, Liu Y, Li Y, Li W, Li Y, Ai CZ. Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation. Arch Toxicol 2024; 98:1457-1467. [PMID: 38492097 DOI: 10.1007/s00204-024-03701-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/31/2024] [Indexed: 03/18/2024]
Abstract
Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.
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Affiliation(s)
- Xin-Man Hu
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources/Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China
| | - Yan-Yao Hou
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources/Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China
| | - Xin-Ru Teng
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources/Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China
| | - Yong Liu
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, 124221, People's Republic of China
| | - Yu Li
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources/Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China
| | - Wei Li
- Translational Medicine Research Institute, College of Medicine, Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, 136 Jiangyangzhong Road, Yangzhou, 225001, People's Republic of China.
| | - Yan Li
- Department of Materials Science and Chemical Engineering, Dalian University of Technology, Dalian, 116023, Liaoning, People's Republic of China
| | - Chun-Zhi Ai
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources/Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China.
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5
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Tan L, Hirte S, Palmacci V, Stork C, Kirchmair J. Tackling assay interference associated with small molecules. Nat Rev Chem 2024; 8:319-339. [PMID: 38622244 DOI: 10.1038/s41570-024-00593-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/29/2024] [Indexed: 04/17/2024]
Abstract
Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.
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Affiliation(s)
- Lu Tan
- Drug Discovery Sciences, Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria
| | - Steffen Hirte
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria
| | - Vincenzo Palmacci
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria
| | - Conrad Stork
- Department of Informatics, Center for Bioinformatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
- BASF SE, Ludwigshafen am Rhein, Germany
| | - Johannes Kirchmair
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
- Christian Doppler Laboratory for Molecular Informatics in the Biosciences, Department for Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
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6
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Chen C, Huang Z, Zou X, Li S, Zhang D, Wang SL. Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170435. [PMID: 38286298 DOI: 10.1016/j.scitotenv.2024.170435] [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: 11/11/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 01/31/2024]
Abstract
Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.
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Affiliation(s)
- Chao Chen
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Zhengliang Huang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; School of Public Health, Hubei University of Medicine, Shiyan 442000, PR China
| | - Xuyan Zou
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Sheng Li
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Di Zhang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Shou-Lin Wang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; State Key Lab of Reproductive Medicine and Offspring Health, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China.
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7
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Lou S, Yu Z, Huang Z, Wang H, Pan F, Li W, Liu G, Tang Y. In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods. Chem Res Toxicol 2024; 37:513-524. [PMID: 38380652 DOI: 10.1021/acs.chemrestox.4c00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.
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Affiliation(s)
- Shang Lou
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhuohang Yu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zejun Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Haoqiang Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Fei Pan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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8
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Setiya A, Jani V, Sonavane U, Joshi R. MolToxPred: small molecule toxicity prediction using machine learning approach. RSC Adv 2024; 14:4201-4220. [PMID: 38292268 PMCID: PMC10826801 DOI: 10.1039/d3ra07322j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/23/2024] [Indexed: 02/01/2024] Open
Abstract
Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.
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Affiliation(s)
- Anjali Setiya
- HPC-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC) Innovation Park, Panchawati, Pashan Pune 411008 India
| | - Vinod Jani
- HPC-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC) Innovation Park, Panchawati, Pashan Pune 411008 India
| | - Uddhavesh Sonavane
- HPC-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC) Innovation Park, Panchawati, Pashan Pune 411008 India
| | - Rajendra Joshi
- HPC-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC) Innovation Park, Panchawati, Pashan Pune 411008 India
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9
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Yu Z, Wu Z, Zhou M, Cao K, Li W, Liu G, Tang Y. EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18013-18025. [PMID: 37053516 DOI: 10.1021/acs.est.2c08558] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Kangjia Cao
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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10
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Wu W, Qian J, Liang C, Yang J, Ge G, Zhou Q, Guan X. GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation. Chem Res Toxicol 2023; 36:1717-1730. [PMID: 37839069 DOI: 10.1021/acs.chemrestox.3c00199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug development. Several DILI prediction models have been published, but they are built on different data sets, making it difficult to compare model performance. Moreover, most existing models are based on molecular fingerprints or descriptors, neglecting molecular geometric properties and lacking interpretability. To address these limitations, we developed GeoDILI, an interpretable graph neural network that uses a molecular geometric representation. First, we utilized a geometry-based pretrained molecular representation and optimized it on the DILI data set to improve predictive performance. Second, we leveraged gradient information to obtain high-precision atomic-level weights and deduce the dominant substructure. We benchmarked GeoDILI against recently published DILI prediction models, as well as popular GNN models and fingerprint-based machine learning models using the same data set, showing superior predictive performance of our proposed model. We applied the interpretable method in the DILI data set and derived seven precise and mechanistically elucidated structural alerts. Overall, GeoDILI provides a promising approach for accurate and interpretable DILI prediction with potential applications in drug discovery and safety assessment. The data and source code are available at GitHub repository (https://github.com/CSU-QJY/GeoDILI).
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Affiliation(s)
- Wenxuan Wu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jiayu Qian
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Changjie Liang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jingya Yang
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Guangbo Ge
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Qingping Zhou
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Xiaoqing Guan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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11
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Kwon H, Ali ZA, Wong BM. Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs). ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2023; 10:1017-1022. [PMID: 38025956 PMCID: PMC10653214 DOI: 10.1021/acs.estlett.2c00530] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/23/2022] [Indexed: 12/01/2023]
Abstract
Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.
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Affiliation(s)
- Hyuna Kwon
- Department
of Chemical & Environmental Engineering, University of California-Riverside, Riverside, California 92521, United States
| | - Zulfikhar A. Ali
- Department
of Physics & Astronomy, University of
California-Riverside, Riverside, California 92521, United States
| | - Bryan M. Wong
- Department
of Chemical & Environmental Engineering, University of California-Riverside, Riverside, California 92521, United States
- Department
of Physics & Astronomy, University of
California-Riverside, Riverside, California 92521, United States
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12
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Balaji S. Metabophore-mediated retro-metabolic ('MeMeReMe') approach in drug design. Drug Discov Today 2023; 28:103736. [PMID: 37586644 DOI: 10.1016/j.drudis.2023.103736] [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: 03/01/2023] [Revised: 06/13/2023] [Accepted: 08/09/2023] [Indexed: 08/18/2023]
Abstract
Preclinical toxicity assessments of new drugs require the use of in silico prediction techniques as ethics, cost, time, and complexity limit in vitro and in vivo methods. This review discusses the fundamental concepts of biophores especially toxicophores and their detection methodologies, tools and techniques, as well as ongoing challenges, and methods for overcoming them. This will guide the design community in manipulating lead compounds via a pre-determined pathway based on the MeMeReMe approach. The ideas discussed will be useful both for predicting toxicity and for de-risking leads through optimization.
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Affiliation(s)
- Seetharaman Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 57614, India.
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13
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Zhang R, Wang B, Li L, Li S, Guo H, Zhang P, Hua Y, Cui X, Li Y, Mu Y, Huang X, Li X. Modeling and insights into the structural characteristics of endocrine-disrupting chemicals. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115251. [PMID: 37451095 DOI: 10.1016/j.ecoenv.2023.115251] [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: 01/20/2023] [Revised: 07/03/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.
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Affiliation(s)
- Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Bailun Wang
- Department of Anesthesiology and perioperative medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Anesthesia and Respiratory Intensive Care Medicine, Jinan 250014, China
| | - Ling Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Shengjie Li
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Mu
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xin Huang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
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14
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Zhang R, Chen Z, Wang B, Li Y, Mu Y, Li X. Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity. ACS OMEGA 2023; 8:31675-31682. [PMID: 37692239 PMCID: PMC10483523 DOI: 10.1021/acsomega.3c01725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023]
Abstract
Mitochondria are the energy metabolism center of cells and are involved in a number of other processes, such as cell differentiation and apoptosis, signal transduction, and regulation of cell cycle and cell proliferation. It is of great significance to evaluate the mitochondrial toxicity of drugs and other chemicals. In the present study, we aimed to propose easily available artificial intelligence (AI) models for the prediction of chemical mitochondrial toxicity and investigate the structural characteristics with the analysis of molecular properties and structural alerts. The consensus model achieved good predictive results with high total accuracy at 87.21% for validation sets. The models can be accessed freely via https://ochem.eu/article/158582. Besides, several commonly used chemical properties were significantly different between chemicals with and without mitochondrial toxicity. We also detected the structural alerts (SAs) responsible for mitochondrial toxicity and integrated them into the web-server SApredictor (www.sapredictor.cn). The study may provide useful tools for in silico estimation of mitochondrial toxicity and be helpful to understand the mechanisms of mitochondrial toxicity.
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Affiliation(s)
- Ruiqiu Zhang
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Zhaoyang Chen
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Baobao Wang
- Department
of Nephrology, The First Affiliated Hospital
of Shandong First Medical University & Shandong Provincial Qianfoshan
Hospital, Jinan 250014, China
| | - Yan Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Mu
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
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15
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Lou C, Yang H, Deng H, Huang M, Li W, Liu G, Lee PW, Tang Y. Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods. J Cheminform 2023; 15:35. [PMID: 36941726 PMCID: PMC10029263 DOI: 10.1186/s13321-023-00707-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/06/2023] [Indexed: 03/23/2023] Open
Abstract
Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.
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Affiliation(s)
- Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Hongbin Yang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Hua Deng
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Mengting Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Philip W Lee
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
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16
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Lo Piparo E, Christinat N, Badoud F. From Structural Alerts to Signature Fragment Alerts: A Case Study on Pyrrolizidine Alkaloids. Chem Res Toxicol 2023; 36:213-229. [PMID: 36692496 DOI: 10.1021/acs.chemrestox.2c00292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Even though modeling is considered a valid alternative to mutagenicity testing for substances with known structures, it can be applied for mixtures only if all of the single chemical structures are identified. Within the present work, we investigate a new avenue to exploit computational toxicology for mixtures, such as plant-based food ingredients. Indeed, considering that in the absence of toxicological information, an important early consideration is whether any substance may be genotoxic through the mutagenic mechanism of action, we tried to establish a correspondence between genotoxic structural alerts (SAs) and so-called signature fragment alerts (SFAs). Once this correspondence is established, chromatograms could be screened for chemical features associated with genotoxic alerts. Pyrrolizidine alkaloids (PAs), a large group of natural toxins (several of them known as genotoxic) were used as a case study because their early identification would bring significant benefits. The method was built using 56 PA pure standards, resulting in the characterization of signature fragment alerts. Finally, the approach was verified in real plant-based samples such as herbal tea and alfalfa, where the screening of signature fragment alerts allowed highlighting quickly the presence of genotoxic PAs in plant-based mixtures. Therefore, the SFA analysis can be used for risk prioritization of newly identified PAs and for their identification in mixtures, contributing to the unnecessary use of animal experimentation for genotoxicity testing.
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Affiliation(s)
- Elena Lo Piparo
- Food Safety Research, Nestlé Research, CH-1000 Lausanne, Switzerland
| | | | - Flavia Badoud
- Analytical Sciences, Nestlé Research, CH-1000 Lausanne, Switzerland
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17
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Zhao X, Sun Y, Zhang R, Chen Z, Hua Y, Zhang P, Guo H, Cui X, Huang X, Li X. Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity. J Chem Inf Model 2022; 62:6035-6045. [PMID: 36448818 DOI: 10.1021/acs.jcim.2c01131] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.
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Affiliation(s)
- Xia Zhao
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Yuhao Sun
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Xin Huang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, Shandong250014, China
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18
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Guo H, Zhang P, Zhang R, Hua Y, Zhang P, Cui X, Huang X, Li X. Modeling and insights into the structural characteristics of drug-induced autoimmune diseases. Front Immunol 2022; 13:1015409. [PMID: 36353637 PMCID: PMC9637949 DOI: 10.3389/fimmu.2022.1015409] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/11/2022] [Indexed: 09/12/2023] Open
Abstract
The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.
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Affiliation(s)
- Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Peitao Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xin Huang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
- Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
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19
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Wu X, Zhou Q, Mu L, Hu X. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives. JOURNAL OF HAZARDOUS MATERIALS 2022; 438:129487. [PMID: 35816807 DOI: 10.1016/j.jhazmat.2022.129487] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.
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Affiliation(s)
- Xiaotong Wu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Qixing Zhou
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Li Mu
- Tianjin Key Laboratory of Agro-environment and Safe-product, Key Laboratory for Environmental Factors Control of Agro-product Quality Safety (Ministry of Agriculture and Rural Affairs), Institute of Agro-environmental Protection, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China.
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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20
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Zhang R, Guo H, Hua Y, Cui X, Shi Y, Li X. Modeling and insights into the structural basis of chemical acute aquatic toxicity. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 242:113940. [PMID: 35999760 DOI: 10.1016/j.ecoenv.2022.113940] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/16/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).
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Affiliation(s)
- Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China; Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan 250014, China.
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21
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Cronin MTD, Bauer FJ, Bonnell M, Campos B, Ebbrell DJ, Firman JW, Gutsell S, Hodges G, Patlewicz G, Sapounidou M, Spînu N, Thomas PC, Worth AP. A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties. Regul Toxicol Pharmacol 2022; 135:105249. [PMID: 36041585 PMCID: PMC9585125 DOI: 10.1016/j.yrtph.2022.105249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/12/2022] [Accepted: 08/17/2022] [Indexed: 11/26/2022]
Abstract
Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification. Structural alerts are useful tools for predictive toxicology. 12 criteria to evaluate structural alerts have been identified. A strategy to determine confidence of structural alerts is presented. Different use cases require different characteristics of structural alerts. A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.
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Affiliation(s)
- Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Franklin J Bauer
- KREATiS SAS, 23 rue du Creuzat, ZAC de St-Hubert, 38080, L'Isle d'Abeau, France
| | - Mark Bonnell
- Science and Risk Assessment Directorate, Environment & Climate Change Canada, 351 St. Joseph Blvd, Gatineau, Quebec, K1A 0H3, Canada
| | - Bruno Campos
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK44 1LQ, UK
| | - David J Ebbrell
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Steve Gutsell
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK44 1LQ, UK
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK44 1LQ, UK
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, 109 TW Alexander Dr, RTP, NC, 27709, USA
| | - Maria Sapounidou
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Nicoleta Spînu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Paul C Thomas
- KREATiS SAS, 23 rue du Creuzat, ZAC de St-Hubert, 38080, L'Isle d'Abeau, France
| | - Andrew P Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
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22
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Shan M, Jiang C, Qin L, Cheng G. A Review of Computational Methods in Predicting hERG Channel Blockers. ChemistrySelect 2022. [DOI: 10.1002/slct.202201221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Mengyi Shan
- School of Pharmaceutical Sciences Zhejiang Chinese Medical University Hangzhou 310053 People's Republic of China
| | - Chen Jiang
- QuanMin RenZheng (HangZhou) Technology Co. Ltd. China
| | - Lu‐Ping Qin
- School of Pharmaceutical Sciences Zhejiang Chinese Medical University Hangzhou 310053 People's Republic of China
| | - Gang Cheng
- School of Pharmaceutical Sciences Zhejiang Chinese Medical University Hangzhou 310053 People's Republic of China
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23
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Hua Y, Cui X, Liu B, Shi Y, Guo H, Zhang R, Li X. SApredictor: An Expert System for Screening Chemicals Against Structural Alerts. Front Chem 2022; 10:916614. [PMID: 35910729 PMCID: PMC9326022 DOI: 10.3389/fchem.2022.916614] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.
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Affiliation(s)
- Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Bo Liu
- Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
- Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- *Correspondence: Xiao Li, , , orcid.org/0000-0002-1148-9898
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24
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Weyrich A, Joel M, Lewin G, Hofmann T, Frericks M. Review of the state of science and evaluation of currently available in silico prediction models for reproductive and developmental toxicity: A case study on pesticides. Birth Defects Res 2022; 114:812-842. [PMID: 35748219 PMCID: PMC9545887 DOI: 10.1002/bdr2.2062] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/10/2022] [Accepted: 05/28/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND In silico methods for toxicity prediction have increased significantly in recent years due to the 3Rs principle. This also applies to predicting reproductive toxicology, which is one of the most critical factors in pesticide approval. The widely used quantitative structure-activity relationship (QSAR) models use experimental toxicity data to create a model that relates experimentally observed toxicity to molecular structures to predict toxicity. Aim of the study was to evaluate the available prediction models for developmental and reproductive toxicity regarding their strengths and weaknesses in a pesticide database. METHODS The reproductive toxicity of 315 pesticides, which have a GHS classification by ECHA, was compared with the prediction of different in silico models: VEGA, OECD (Q)SAR Toolbox, Leadscope Model Applier, and CASE Ultra by MultiCASE. RESULTS In all models, a large proportion (up to 77%) of all pesticides were outside the chemical space of the model. Analysis of the prediction of remaining pesticides revealed a balanced accuracy of the models between 0.48 and 0.66. CONCLUSION Overall, predictions were only meaningful in rare cases and therefore always require evaluation by an expert. The critical factors were the underlying data and determination of molecular similarity, which offer great potential for improvement.
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Affiliation(s)
| | - Madeleine Joel
- Preclinical Science - Föll, Mecklenburg & Partner GmbH, Münster, Germany
| | - Geertje Lewin
- Preclinical Science - Föll, Mecklenburg & Partner GmbH, Münster, Germany
| | - Thomas Hofmann
- Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | - Markus Frericks
- Agricultural Solutions - Toxicology CP, BASF SE, Limburgerhof, Germany
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25
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Lou C, Yang H, Wang J, Huang M, Li W, Liu G, Lee PW, Tang Y. IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method. J Chem Inf Model 2022; 62:2788-2799. [PMID: 35607907 DOI: 10.1021/acs.jcim.2c00297] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more difficult to manually extract useful medicinal chemistry knowledge. To this end, we introduced IDL-PPBopt, a computational strategy for predicting and optimizing the plasma protein binding (PPB) property based on an interpretable deep learning method. At first, a curated PPB data set was used to construct an interpretable deep learning model, which showed excellent predictive performance with a root mean squared error of 0.112 for the entire test set. Then, we designed a detection protocol based on the model and Wilcoxon test to identify the PPB-related substructures (named privileged substructures, PSubs) for each molecule. In total, 22 general privileged substructures (GPSubs) were identified, which shared some common features such as nitrogen-containing groups, diamines with two carbon units, and azetidine. Furthermore, a series of second-level chemical rules for each GPSub were derived through a statistical test and then summarized into substructure pairs. We demonstrated that these substructure pairs were equally applicable outside the training set and accordingly customized the structural modification schemes for each GPSub, which provided alternatives for the optimization of the PPB property. Therefore, IDL-PPBopt provides a promising scheme for the prediction and optimization of the PPB property and would be helpful for lead optimization of other pharmacokinetic properties.
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Affiliation(s)
- Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Hongbin Yang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jiye Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Mengting Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Philip W Lee
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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26
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Goel R, Reilly SM, Valerio LG. A Computational Approach for Respiratory Hazard Identification of Flavor Chemicals in Tobacco Products. Chem Res Toxicol 2022; 35:450-458. [PMID: 35239324 DOI: 10.1021/acs.chemrestox.1c00361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Flavor chemicals contribute to the appeal and toxicity of tobacco products, including electronic nicotine delivery systems (ENDS). The assortment of flavor chemicals available for use in tobacco products is extensive. In this study, a chemistry-driven computational approach was used to evaluate flavor chemicals based on intrinsic hazardous structures and reactivity of chemicals. A large library of 3012 unique flavor chemicals was compiled from publicly available information. Next, information was computed and collated based on their (1) physicochemical properties, (2) global harmonization system (GHS) health hazard classification, (3) structural alerts linked to the chemical's reactivity, instability, or toxicity, and (4) common substructure shared with FDA's harmful and potentially harmful constituents (HPHCs) flavor chemicals that are respiratory toxicants. Computational analysis of the constructed flavor library flagged 638 chemicals with GHS classified respiratory health hazards, 1079 chemicals with at least one structural alert, and 2297 chemicals with substructural similarity to FDA's established and proposed list of HPHCs. A subsequent analysis was performed on a subset of 173 chemicals in the flavor library that are respiratory health hazards, contain structural alerts as well as flavor HPHC substructures. Four general toxicophore structures with an increased potential for respiratory toxicity were then identified. In summary, computational methods are efficient tools for hazard identification and understanding structure-toxicity relationship. With appropriate context of use and interpretation, in silico methods may provide scientific evidence to support toxicological evaluations of chemicals in or emitted from tobacco products.
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27
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Caballero Alfonso AY, Mora Lagares L, Novic M, Benfenati E, Kumar A. Exploration of structural requirements for azole chemicals towards human aromatase CYP19A1 activity: Classification modeling, structure-activity relationships and read-across study. Toxicol In Vitro 2022; 81:105332. [PMID: 35176449 DOI: 10.1016/j.tiv.2022.105332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/10/2022] [Accepted: 02/10/2022] [Indexed: 01/23/2023]
Abstract
Human aromatase, also called CYP19A1, plays a major role in the conversion of androgens into estrogens. Inhibition of aromatase is an important target for estrogen receptor (ER)-responsive breast cancer therapy. Use of azole compounds as aromatase inhibitors is widespread despite their low selectivity. A toxicological evaluation of commonly used azole-based drugs and agrochemicals with respect to CYP19A1is currently requested by the European Union- Registration, Evaluation, Authorization and Restriction of Chemicals (EU-REACH) regulations due to their potential as endocrine disruptors. In this connection, identification of structural alerts (SAs) is an effective strategy for the toxicological assessment and safe drug design. The present study describes the identification of SAs of azole-based chemicals as guiding experts to predict the aromatase activity. Total 21 SAs associated with aromatase activity were extracted from dataset of 326 azole-based drugs/chemicals obtained from Tox21 library. A cross-validated classification model having high accuracy (error rate 5%) was proposed which can precisely classify azole chemicals into active/inactive toward aromatase. In addition, mechanistic details and toxicological properties (agonism/antagonism) of azoles with respect to aromatase were explored by comparing active and inactive chemicals using structure-activity relationships (SAR). Lastly, few structural alerts were applied to form chemical categories for read-across applications.
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Affiliation(s)
- Ana Y Caballero Alfonso
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche "Mario Negri"-IRCCS, Milano, Italy; Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Liadys Mora Lagares
- Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia; Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Marjana Novic
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche "Mario Negri"-IRCCS, Milano, Italy
| | - Anil Kumar
- Department of Applied Sciences, University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India.
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28
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Yu Z, Wu Z, Li W, Liu G, Tang Y. ADENet: a novel network-based inference method for prediction of drug adverse events. Brief Bioinform 2022; 23:6510157. [PMID: 35039845 DOI: 10.1093/bib/bbab580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/02/2021] [Accepted: 12/19/2021] [Indexed: 11/13/2022] Open
Abstract
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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29
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Ebert A, Goss KU. Screening of 6000 Compounds for Uncoupling Activity: A Comparison Between a Mechanistic Biophysical Model and the Structural Alert Profiler Mitotox. Toxicol Sci 2021; 185:208-219. [PMID: 34865177 DOI: 10.1093/toxsci/kfab139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes, or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in vitro uncoupling activity, using a biophysical model based on ab initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.
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Affiliation(s)
- Andrea Ebert
- Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research-UFZ, D-04318 Leipzig, Germany
| | - Kai-Uwe Goss
- Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research-UFZ, D-04318 Leipzig, Germany.,Institute of Chemistry, Martin Luther University, D-06120 Halle, Germany
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30
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Li T, Tong W, Roberts R, Liu Z, Thakkar S. DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation. Front Artif Intell 2021; 4:757780. [PMID: 34870186 PMCID: PMC8636933 DOI: 10.3389/frai.2021.757780] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/27/2021] [Indexed: 12/16/2022] Open
Abstract
Carcinogenicity testing plays an essential role in identifying carcinogens in environmental chemistry and drug development. However, it is a time-consuming and label-intensive process to evaluate the carcinogenic potency with conventional 2-years rodent animal studies. Thus, there is an urgent need for alternative approaches to providing reliable and robust assessments on carcinogenicity. In this study, we proposed a DeepCarc model to predict carcinogenicity for small molecules using deep learning-based model-level representations. The DeepCarc Model was developed using a data set of 692 compounds and evaluated on a test set containing 171 compounds in the National Center for Toxicological Research liver cancer database (NCTRlcdb). As a result, the proposed DeepCarc model yielded a Matthews correlation coefficient (MCC) of 0.432 for the test set, outperforming four advanced deep learning (DL) powered quantitative structure-activity relationship (QSAR) models with an average improvement rate of 37%. Furthermore, the DeepCarc model was also employed to screen the carcinogenicity potential of the compounds from both DrugBank and Tox21. Altogether, the proposed DeepCarc model could serve as an early detection tool (https://github.com/TingLi2016/DeepCarc) for carcinogenicity assessment.
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Affiliation(s)
- Ting Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States,University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Joint Bioinformatics Program, Little Rock, AR, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States
| | - Ruth Roberts
- ApconiX Ltd., Alderley Edge, United Kingdom,Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States,*Correspondence: Zhichao Liu, ; Shraddha Thakkar,
| | - Shraddha Thakkar
- Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States,*Correspondence: Zhichao Liu, ; Shraddha Thakkar,
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31
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Hua Y, Shi Y, Cui X, Li X. In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods. Mol Divers 2021; 25:1585-1596. [PMID: 34196933 DOI: 10.1007/s11030-021-10255-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 06/14/2021] [Indexed: 12/15/2022]
Abstract
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.
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Affiliation(s)
- Yuqing Hua
- School of Pharmacy, Shandong First Medical University, Taian, 271000, China.,Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China. .,Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
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32
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Singh AK, Bilal M, Iqbal HMN, Raj A. Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 770:144561. [PMID: 33736422 DOI: 10.1016/j.scitotenv.2020.144561] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/13/2020] [Accepted: 12/13/2020] [Indexed: 02/05/2023]
Abstract
The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.
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Affiliation(s)
- Anil Kumar Singh
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Hafiz M N Iqbal
- Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico.
| | - Abhay Raj
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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33
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Kim KB, Kwack SJ, Lee JY, Kacew S, Lee BM. Current opinion on risk assessment of cosmetics. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2021; 24:137-161. [PMID: 33832410 DOI: 10.1080/10937404.2021.1907264] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Risk assessment of cosmetic ingredients is a useful scientific method to characterize potential adverse effects resulting from using cosmetics. The process of risk assessment consists of four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification of chemicals refers to the initial stage of risk assessment and generally utilizes animal studies to evaluate toxicity. Since 2013, however, toxicity studies of cosmetic ingredients using animals have not been permitted in the EU and alternative toxicity test methods for animal studies have momentum to be developed for cosmetic ingredients. In this paper, we briefly review the alternative test methods that are available for cosmetic ingredients including read-across, in silico, in chemico, and invitro methods. In addition, new technologies such as omics and artificial intelligence (AI) have been discussed to expand or improve the knowledge and hazard identification of cosmetic ingredients. Aggregate exposure of cosmetic ingredients is another safety issue and methods for its improvement were reviewed. There have been concerns over the safety of nano-cosmetics for a long time, but the risk of nano-cosmetics remains unclear. Therefore, current issues of cosmetic risk assessment are discussed and expert opinion will be provided for the safety of cosmetics.
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Affiliation(s)
- Kyu-Bong Kim
- College of Pharmacy, Dankook University, Cheonan, Chungnam, South Korea
| | - Seung Jun Kwack
- Department of Bio Health Science, College of Natural Science, Changwon National University, Changwon, Gyeongnam, Suwon, Gyeonggi-Do, South Korea
| | - Joo Young Lee
- College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea
| | - Sam Kacew
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada
| | - Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
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34
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Wu Z, Wang Q, Yang H, Wang J, Li W, Liu G, Yang Y, Zhao Y, Tang Y. Discovery of Natural Products Targeting NQO1 via an Approach Combining Network-Based Inference and Identification of Privileged Substructures. J Chem Inf Model 2021; 61:2486-2498. [PMID: 33955748 DOI: 10.1021/acs.jcim.1c00260] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
NAD(P)H:quinone oxidoreductase 1 (NQO1) has been shown to be a potential therapeutic target for various human diseases, such as cancer and neurodegenerative disorders. Recent advances in computational methods, especially network-based methods, have made it possible to identify novel compounds for a target with high efficiency and low cost. In this study, we designed a workflow combining network-based methods and identification of privileged substructures to discover new compounds targeting NQO1 from a natural product library. According to the prediction results, we purchased 56 compounds for experimental validation. Without the assistance of privileged substructures, 31 compounds (31/56 = 55.4%) showed IC50 < 100 μM, and 11 compounds (11/56 = 19.6%) showed IC50 < 10 μM. With the assistance of privileged substructures, the two success rates were increased to 61.8 and 26.5%, respectively. Seven natural products further showed inhibitory activity on NQO1 at the cellular level, which may serve as lead compounds for further development. Moreover, network analysis revealed that osthole may exert anticancer effects against multiple cancer types by inhibiting not only carbonic anhydrases IX and XII but also NQO1. Our workflow and computational methods can be easily applied in other targets and become useful tools in drug discovery and development.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Qiaohui Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.,Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jiye Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yi Yang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuzheng Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.,Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Jörg M, Madden KS. The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery. RSC Med Chem 2021; 12:646-665. [PMID: 34124668 PMCID: PMC8152813 DOI: 10.1039/d1md00022e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/15/2021] [Indexed: 12/15/2022] Open
Abstract
The reconnection of the scientific community with phenotypic drug discovery has created exciting new possibilities to develop therapies for diseases with highly complex biology. It promises to revolutionise fields such as neurodegenerative disease and regenerative medicine, where the development of new drugs has consistently proved elusive. Arguably, the greatest challenge in readopting the phenotypic drug discovery approach exists in establishing a crucial chain of translatability between phenotype and benefit to patients in the clinic. This remains a key stumbling block for the field which needs to be overcome in order to fully realise the potential of phenotypic drug discovery. Excellent quality chemical probes and chemistry-based target deconvolution techniques will be a crucial part of this process. In this review, we discuss the current capabilities of chemical probes and chemistry-based target deconvolution methods and evaluate the next advances necessary in order to fully support phenotypic screening approaches in drug discovery.
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Affiliation(s)
- Manuela Jörg
- School of Natural and Environmental Sciences, Newcastle University Bedson Building Newcastle upon Tyne NE1 7RU UK
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University Parkville Victoria 3052 Australia
| | - Katrina S Madden
- School of Natural and Environmental Sciences, Newcastle University Bedson Building Newcastle upon Tyne NE1 7RU UK
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University Parkville Victoria 3052 Australia
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36
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Firman JW, Pestana CB, Rathman JF, Vinken M, Yang C, Cronin MTD. A Robust, Mechanistically Based In Silico Structural Profiler for Hepatic Cholestasis. Chem Res Toxicol 2020; 34:641-655. [PMID: 33314907 DOI: 10.1021/acs.chemrestox.0c00465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Owing to the primary role which it holds within metabolism of xenobiotics, the liver stands at heightened risk of exposure to, and injury from, potentially hazardous substances. A principal manifestation of liver dysfunction is cholestasis-the impairment of physiological bile circulation from its point of origin within the organ to the site of action in the small intestine. The capacity for early identification of compounds liable to exert cholestatic effects is of particular utility within the field of pharmaceutical development, where contribution toward candidate attrition is great. Shortcomings associated with the present in vitro methodologies forecasting cholestasis render their predictivity questionable, permitting scope for the adoption of computational toxicology techniques. As such, the intention of this study has been to construct an in silico profiler, founded upon clinical data, highlighting structural motifs most reliably associated with the end point. Drawing upon a list of >1500 small molecular drugs, compiled and annotated by Kotsampasakou, E. and Ecker, G. F. (J. Chem. Inf. Model. 2017, 57, 608-615), we have formulated a series of 15 structural alerts. These describe fragments intrinsic within distinct pharmaceutical classes including psychoactive tricyclics, β-lactam antimicrobials, and estrogenic/androgenic steroids. Description of the coverage and selectivity of each are provided, alongside consideration of the underlying reactive mechanisms and relevant structure-activity concerns. Provision of mechanistic anchoring ensures that potential exists for framing within the adverse outcome pathway paradigm-the chemistry conveyed through the alert, in particular enabling rationalization at the level of the molecular initiating event.
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Affiliation(s)
- James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Cynthia B Pestana
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - James F Rathman
- Molecular Networks GmbH, Neumeyerstraße 28, 90411 Nuremberg, Germany.,Altamira, LLC, Columbus, Ohio 43210, United States.,Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Mathieu Vinken
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Chihae Yang
- Molecular Networks GmbH, Neumeyerstraße 28, 90411 Nuremberg, Germany.,Altamira, LLC, Columbus, Ohio 43210, United States
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
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37
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Krebs J, McKeague M. Green Toxicology: Connecting Green Chemistry and Modern Toxicology. Chem Res Toxicol 2020; 33:2919-2931. [DOI: 10.1021/acs.chemrestox.0c00260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Johanna Krebs
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Department of Health Sciences and Technology, ETH Zürich, Universitätstrasse 2, Zurich, Switzerland CH 8092
| | - Maureen McKeague
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Faculty of Science, Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0B8
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38
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Tinkov O, Polishchuk P, Matveieva M, Grigorev V, Grigoreva L, Porozov Y. The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds. Mol Inform 2020; 40:e2000209. [PMID: 33029954 DOI: 10.1002/minf.202000209] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 10/01/2020] [Indexed: 12/28/2022]
Abstract
Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.
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Affiliation(s)
- Oleg Tinkov
- Department of Computer Science, Military Institute of the Ministry of Defense, 3300, Gogol str. 2"B", Tiraspol, Transdniestria, Moldova.,Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Transnistrian State University, 3300, October 25 str. 128, Tiraspol, Transdniestria, Moldova
| | - Pavel Polishchuk
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Mariia Matveieva
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Veniamin Grigorev
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432, Severniy proezd 1, Chernogolovka, Moscow region, Russia
| | - Ludmila Grigoreva
- Department of Fundamental Physical and Chemical Engineering, Moscow State University, 119991, Leninskiye Gory 1/51, Moscow, Russia
| | - Yuri Porozov
- World-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Department of Computational Biology, Sirius University of Science and Technology, 354340, Olympic Ave 1, Sochi, Russia
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39
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Tang W, Chen J, Hong H. Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods. CHEMOSPHERE 2020; 273:128567. [PMID: 34756375 DOI: 10.1016/j.chemosphere.2020.128567] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/03/2020] [Accepted: 10/06/2020] [Indexed: 06/13/2023]
Abstract
Mitochondrial fusion and fission are processes to maintain mitochondrial function when cells respond to environment stresses. Disruption of mitochondrial fusion and fission influences cell health and can cause adverse events such as neurodegenerative disorders. It is critical to identify environmental chemicals that can disrupt mitochondrial fusion and fission. However, experimentally testing all the chemicals is not practical because experimental methods are time-consuming and costly. Quantitative structure-activity relationship (QSAR) modeling is an attractive approach for evaluation of chemicals disrupting potential on mitochondrial fusion and fission. In this study, QSAR models were developed for differentiating chemicals capable of inhibition of mitochondrial fusion and fission using machine learning algorithms (i.e. random forest, logistic regression, Bernoulli naive Bayes, and deep neural network). One hundred iterations of five-fold cross validations and external validations showed that the best model on mitochondrial fusion had area under the receiver operating characteristic curve (AUC) of 82.8% and 78.1%, respectively; and the best model for mitochondrial fission yielded AUC of 84.3% and 97.5%, respectively. Furthermore, 45 and 56 structural alerts were identified for inhibition of mitochondrial fusion and fission, respectively. The results demonstrated that the models and the structural alerts could be useful for screening chemicals that inhibit mitochondrial fusion and fission.
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Affiliation(s)
- Weihao Tang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA.
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