1
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Wang T, Russo DP, Demokritou P, Jia X, Huang H, Yang X, Zhu H. An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits. NANO LETTERS 2024. [PMID: 39120132 DOI: 10.1021/acs.nanolett.4c02568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.
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Affiliation(s)
- Tong Wang
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Daniel P Russo
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, Massachusetts 02115, United States
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Xuelian Jia
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, Maryland 20742, United States
| | - Xinyu Yang
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Hao Zhu
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
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2
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Oh M, Shen M, Liu R, Stavitskaya L, Shen J. Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor. ACS Chem Neurosci 2024; 15:2842-2852. [PMID: 38990780 DOI: 10.1021/acschemneuro.4c00212] [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] [Indexed: 07/13/2024] Open
Abstract
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat μOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.
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Affiliation(s)
- Myongin Oh
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Maximilian Shen
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742, United States
| | - Ruibin Liu
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Lidiya Stavitskaya
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
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3
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Daood NJ, Russo DP, Chung E, Qin X, Zhu H. Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms. ENVIRONMENT & HEALTH (WASHINGTON, D.C.) 2024; 2:474-485. [PMID: 39049897 PMCID: PMC11264268 DOI: 10.1021/envhealth.4c00026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 07/27/2024]
Abstract
Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards. However, few computational modeling studies for immunotoxicity were reported, with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity. In this study, we employed a data-driven quantitative structure-activity relationship (QSAR) modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity. To this end, a probe data set of 6,341 chemicals was obtained from a high-throughput screening (HTS) assay testing for the activation of the aryl hydrocarbon receptor (AhR) signaling pathway, a key event leading to immunotoxicity. Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds. 100 assays were selected to develop QSAR models based on their correlations to AhR agonism. Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints. 5-fold cross-validation of the resulting models showed good predictivity (average CCR = 0.73). A total of 20 assays were further selected based on QSAR model performance, and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals. This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints, which have limited training data and complicated toxicity mechanisms.
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Affiliation(s)
- Nada J. Daood
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Daniel P. Russo
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Elena Chung
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
- Center
for Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Xuebin Qin
- Tulane
National Primate Research Center, Tulane
University School of Medicine, Covington, Louisiana 70433, United States
| | - Hao Zhu
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
- Center
for Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
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4
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Oh M, Shen M, Liu R, Stavitskaya L, Shen J. Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588485. [PMID: 38645122 PMCID: PMC11030315 DOI: 10.1101/2024.04.07.588485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured E max values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat μOR, κOR, or δOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.
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Affiliation(s)
- Myongin Oh
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD 20993, United States
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, United States
| | - Maximilian Shen
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD
| | - Ruibin Liu
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, United States
| | - Lidiya Stavitskaya
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD 20993, United States
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, United States
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5
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Chung E, Wen X, Jia X, Ciallella HL, Aleksunes LM, Zhu H. Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134297. [PMID: 38677119 DOI: 10.1016/j.jhazmat.2024.134297] [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: 01/08/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024]
Abstract
Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.
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Affiliation(s)
- Elena Chung
- Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Xia Wen
- Department of Pharmacology and Toxicology, Rutgers University, Piscataway, NJ, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Heather L Ciallella
- Department of Toxicology, Cuyahoga County Medical Examiner's Office, Cleveland, OH, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Rutgers University, Piscataway, NJ, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA.
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6
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Li Q, Yang H, Hao N, Du M, Zhao Y, Li Y, Li X. Biodegradability analysis of Dioxins through in silico methods: Model construction and mechanism analysis. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118898. [PMID: 37657295 DOI: 10.1016/j.jenvman.2023.118898] [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: 06/04/2023] [Revised: 08/18/2023] [Accepted: 08/27/2023] [Indexed: 09/03/2023]
Abstract
The biodegradation treatment of dioxins has long been of interest due to its good ecological and economic effects. In this study, the biodegradability of polychlorinated dibenzo-p-dioxins (PCDDs) were investigated by constructing machine learning and multiple linear regression models. The maximum chlorine atomic charge (qHirshfeldCl+), which characterizes the biodegradation ability of PCDDs, was used as the response value. The random forest model was used to rank the importance on the 1471 descriptors of PCDDs, and the BCUTp-1 h, QXZ, JGI4, ATSC8c, VE3_Dt, topoShape, and maxwHBa were screened as the important descriptors by Pearson's correlation coefficient method. A quantitative structure-activity relationship (QSAR) model was constructed to predict the biodegradability of PCDDs. In addition, the extreme gradient boosting (XGBoost) and random forest model were also constructed and proved the good predictability of QSAR model. The biodegradability of polychlorinated dibenzofurans (PCDFs) can also be predicted by the constructed three models from a certain level after adjusting some model parameters, which further proved the versatility of the models. Besides, the sensitivity analysis of the QSAR model and a 3D-QSAR model was developed to investigate the biodegradability mechanisms of PCDDs. Results showed that the descriptors BCUTp-1 h, JGI4, and maxwHBa were the key descriptors in the biodegradability effect by the sensitivity analysis of the QSAR model. Coupled with the results of PCDDs biodegradability 3D-QSAR model, BCUTp-1 h, JGI4, and maxwHBa were confirmed as the main descriptors that affect the biodegradability of dioxins. This study provides a novel theoretical perspective for the research of the biodegradation of both PCDDs and PCDFs dioxins.
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Affiliation(s)
- Qing Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Hao Yang
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Ning Hao
- College of New Energy and Environment, Jilin University, Changchun, 130012, China.
| | - Meijn Du
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Yuanyuan Zhao
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Xixi Li
- Center for Environmental Health Risk Assessment and Research, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3X5, Canada.
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7
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Feng H, Wang R, Zhan CG, Wei GW. Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex. J Med Chem 2023; 66:12479-12498. [PMID: 37623046 PMCID: PMC11037444 DOI: 10.1021/acs.jmedchem.3c01053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion model with a pretrained autoencoder. The molecular generator enables efficient generation of molecules that target multiple opioid receptors, including mu, kappa, and delta. Additionally, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify druglike compounds. We develop a molecular optimization approach to enhance the pharmacokinetic properties of some lead compounds. Advanced binding affinity predictors were built using molecular fingerprints, including autoencoder embeddings, transformer embeddings, and topological Laplacians. Our process yields druglike molecules that can be used in highly focused experimental studies to further evaluate their pharmacological effects. Our machine learning platform serves as a valuable tool for designing effective molecules to address OUD.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Chang-Guo Zhan
- Department of Pharmaceutical Sciences, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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8
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Feng H, Jiang J, Wei GW. Machine-learning repurposing of DrugBank compounds for opioid use disorder. Comput Biol Med 2023; 160:106921. [PMID: 37178605 DOI: 10.1016/j.compbiomed.2023.106921] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Jian Jiang
- Department of Mathematics, Michigan State University, MI 48824, USA; Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
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9
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Liu Y, Li X, Pu Q, Fu R, Wang Z, Li Y, Li X. Innovative screening for functional improved aromatic amine derivatives: Toxicokinetics, free radical oxidation pathway and carcinogenic adverse outcome pathway. JOURNAL OF HAZARDOUS MATERIALS 2023; 454:131541. [PMID: 37146326 DOI: 10.1016/j.jhazmat.2023.131541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/08/2023] [Accepted: 04/28/2023] [Indexed: 05/07/2023]
Abstract
Aromatic amines, one of the most widely used low-cost antioxidants in rubbers, have been regarded as pollutants with human health concerns. To overcome this problem, this study developed a systematic molecular design, screening, and performance evaluation method to design functionally improved, environmentally friendly and synthesizable aromatic amine alternatives for the first time. Nine of 33 designed aromatic amine derivatives have improved antioxidant property (lower bond dissociation energy of N-H), and their environmental and bladder carcinogenicity impacts were evaluated through toxicokinetic model and molecular dynamics simulation. The environmental fate of the designed AAs-11-8, AAs-11-16, and AAs-12-2 after antioxidation (i.e., peroxyl radicals (ROO·), hydroxyl radicals (HO·), superoxide anion radicals (O2·-) and ozonation reaction) was also analyzed. Results showed that the by-products of AAs-11-8 and AAs-12-2 have less toxicity after antioxidation. In addition, human bladder carcinogenicity of the screened alternatives was also evaluated through adverse outcome pathway. The carcinogenic mechanisms were analyzed and verified through amino acid residue distribution characteristics, 3D-QSAR and 2D-QSAR models. AAs-12-2, with high antioxidation property, low environmental impacts and carcinogenicity, was screened as the optimum alternative for 3,5-Dimethylbenzenamine. This study provided theoretical support for designing environmentally friendly and functionally improved aromatic amine alternatives from toxicity evaluation and mechanism analysis.
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Affiliation(s)
- Yajing Liu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Xinao Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Qikun Pu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Rui Fu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Zhonghe Wang
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Yu Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
| | - Xixi Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; State Environmental Protection Key Laboratory of Ecological Effect and Risk Assessment of Chemicals, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3X5, Canada.
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10
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Chung E, Russo DP, Ciallella HL, Wang YT, Wu M, Aleksunes LM, Zhu H. Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:6573-6588. [PMID: 37040559 PMCID: PMC10134506 DOI: 10.1021/acs.est.3c00648] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicity-related models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency's Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds' carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.
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Affiliation(s)
- Elena Chung
- Department
of Chemistry and Biochemistry, Rowan University, 201 Mullica Hill Road, Glassboro, New Jersey 08028, United States
| | - Daniel P. Russo
- Department
of Chemistry and Biochemistry, Rowan University, 201 Mullica Hill Road, Glassboro, New Jersey 08028, United States
| | - Heather L. Ciallella
- Department
of Toxicology, Cuyahoga County Medical Examiner’s
Office, 11001 Cedar Avenue, Cleveland, Ohio 44106, United States
| | - Yu-Tang Wang
- Institute
of Agro-Products Processing Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products
Processing, Ministry of Agriculture, Beijing 100193, China
| | - Min Wu
- School
of Life Science and Technology, China Pharmaceutical
University, No. 24, Tong Jia Xiang, Nanjing 210009, China
| | - Lauren M. Aleksunes
- Department
of Pharmacology and Toxicology, Rutgers
University, Ernest Mario School of Pharmacy, 170 Frelinghuysen Road, Piscataway, New Jersey 08854, United States
| | - Hao Zhu
- Department
of Chemistry and Biochemistry, Rowan University, 201 Mullica Hill Road, Glassboro, New Jersey 08028, United States
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11
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Wang T, Russo DP, Bitounis D, Demokritou P, Jia X, Huang H, Zhu H. Integrating structure annotation and machine learning approaches to develop graphene toxicity models. CARBON 2023; 204:484-494. [PMID: 36845527 PMCID: PMC9957041 DOI: 10.1016/j.carbon.2022.12.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.
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Affiliation(s)
- Tong Wang
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Daniel P. Russo
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, Pennsylvania, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
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12
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Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem 2023 update. Nucleic Acids Res 2022; 51:D1373-D1380. [PMID: 36305812 PMCID: PMC9825602 DOI: 10.1093/nar/gkac956] [Citation(s) in RCA: 589] [Impact Index Per Article: 294.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 01/30/2023] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jia He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- To whom correspondence should be addressed. Tel: +1 301 451 1811; Fax: +1 301 480 4559;
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13
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Jia X, Wen X, Russo DP, Aleksunes LM, Zhu H. Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay. JOURNAL OF HAZARDOUS MATERIALS 2022; 436:129193. [PMID: 35739723 PMCID: PMC9262097 DOI: 10.1016/j.jhazmat.2022.129193] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 05/20/2023]
Abstract
Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.
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Affiliation(s)
- Xuelian Jia
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Xia Wen
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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14
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Ciallella HL, Chung E, Russo DP, Zhu H. Automatic Quantitative Structure-Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening. Methods Mol Biol 2022; 2474:169-187. [PMID: 35294765 DOI: 10.1007/978-1-0716-2213-1_16] [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] [Indexed: 06/14/2023]
Abstract
Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. Quantitative structure-activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently developing optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background.
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Affiliation(s)
- Heather L Ciallella
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Elena Chung
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
- Department of Chemistry, Rutgers University, Camden, NJ, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
- Department of Chemistry, Rutgers University, Camden, NJ, USA.
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15
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The Psychonauts' Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity. Pharmaceuticals (Basel) 2021; 14:ph14080720. [PMID: 34451817 PMCID: PMC8398354 DOI: 10.3390/ph14080720] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 12/28/2022] Open
Abstract
Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder®) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder®. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.
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