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Schumann PG, Chang D, Mayasich S, Vliet S, Brown T, LaLone CA. Cross-Species Molecular Docking Method to Support Predictions of Species Susceptibility to Chemical Effects. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2024; 30:100319. [PMID: 39381055 PMCID: PMC11457042 DOI: 10.1016/j.comtox.2024.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Affiliation(s)
- Peter G. Schumann
- Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - Daniel Chang
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, Research Triangle Park, North Carolina, USA
| | - Sally Mayasich
- Aquatic Sciences Center, University of Wisconsin‐Madison, Madison, Wisconsin, USA
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Sara Vliet
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Terry Brown
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Duluth, Minnesota, USA
| | - Carlie A. LaLone
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
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Duran T, Chaudhuri B. Where Might Artificial Intelligence Be Going in Pharmaceutical Development? Mol Pharm 2024; 21:993-995. [PMID: 38376360 DOI: 10.1021/acs.molpharmaceut.4c00112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
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Grillberger K, Cöllen E, Trivisani CI, Blum J, Leist M, Ecker GF. Structural Insights into Neonicotinoids and N-Unsubstituted Metabolites on Human nAChRs by Molecular Docking, Dynamics Simulations, and Calcium Imaging. Int J Mol Sci 2023; 24:13170. [PMID: 37685977 PMCID: PMC10487998 DOI: 10.3390/ijms241713170] [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: 07/28/2023] [Revised: 08/10/2023] [Accepted: 08/12/2023] [Indexed: 09/10/2023] Open
Abstract
Neonicotinoid pesticides were initially designed in order to achieve species selectivity on insect nicotinic acetylcholine receptors (nAChRs). However, concerns arose when agonistic effects were also detected in human cells expressing nAChRs. In the context of next-generation risk assessments (NGRAs), new approach methods (NAMs) should replace animal testing where appropriate. Herein, we present a combination of in silico and in vitro methodologies that are used to investigate the potentially toxic effects of neonicotinoids and nicotinoid metabolites on human neurons. First, an ensemble docking study was conducted on the nAChR isoforms α7 and α3β4 to assess potential crucial molecular initiating event (MIE) interactions. Representative docking poses were further refined using molecular dynamics (MD) simulations and binding energy calculations using implicit solvent models. Finally, calcium imaging on LUHMES neurons confirmed a key event (KE) downstream of the MIE. This method was also used to confirm the predicted agonistic effect of the metabolite descyano-thiacloprid (DCNT).
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Affiliation(s)
- Karin Grillberger
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria
| | - Eike Cöllen
- In Vitro Toxicology and Biomedicine, University of Konstanz, 78457 Konstanz, Germany
| | | | - Jonathan Blum
- In Vitro Toxicology and Biomedicine, University of Konstanz, 78457 Konstanz, Germany
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, University of Konstanz, 78457 Konstanz, Germany
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria
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Klambauer G, Clevert DA, Shah I, Benfenati E, Tetko IV. Introduction to the Special Issue: AI Meets Toxicology. Chem Res Toxicol 2023; 36:1163-1167. [PMID: 37599584 DOI: 10.1021/acs.chemrestox.3c00217] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Affiliation(s)
- Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenbergerstraße 69, Linz 4040, Austria
| | - Djork-Arné Clevert
- Machine Learning Research, Pfizer Worldwide Research Development and Medical, Linkstr. 10, Berlin 10785, Germany
| | - Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano 20156, Italy
| | - Igor V Tetko
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- BIGCHEM GmbH, Valerystr. 49, 85716 Unterschleißheim, Germany
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Hu Y, Ren Q, Liu X, Gao L, Xiao L, Yu W. In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods. Chem Res Toxicol 2023. [PMID: 37300507 DOI: 10.1021/acs.chemrestox.2c00411] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial intelligence methods are popularly regarded as a promising solution in chemical toxicology. Thus, we provided comprehensive in silico prediction models for eight significant human organ level toxicity end points using machine learning, deep learning, and transfer learning algorithms. In this work, our results showed that the graph-based deep learning approach was generally better than the conventional machine learning models, and good performances were observed for most of the human organ level toxicity end points in this study. In addition, we found that the transfer learning algorithm could improve model performance for skin sensitization end point using source domain of in vivo acute toxicity data and in vitro data of the Tox21 project. It can be concluded that our models can provide useful guidance for the rapid identification of the compounds with human organ level toxicity for drug discovery.
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Affiliation(s)
- Yuxuan Hu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Qiuhan Ren
- School of Science, China Pharmaceutical University, Nanjing 211198, China
| | - Xintong Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Liming Gao
- School of Science, China Pharmaceutical University, Nanjing 211198, China
| | - Lecheng Xiao
- School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Wenying Yu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
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Romano JD, Mei L, Senn J, Moore JH, Mortensen HM. Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 25:100261. [PMID: 37829618 PMCID: PMC10569310 DOI: 10.1016/j.comtox.2023.100261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency's Adverse Outcome Pathway Database (AOP-DB; aopdb.epa.gov) and the UK Biobank's genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.
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Affiliation(s)
- Joseph D. Romano
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
- Center of Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Liang Mei
- Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - Jonathan Senn
- Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Holly M. Mortensen
- United States Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, NC, United States
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Ruiz P, Loizou G. Editorial: Application of computational tools to health and environmental sciences, Volume II. Front Pharmacol 2022; 13:1102431. [DOI: 10.3389/fphar.2022.1102431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 12/04/2022] Open
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Hurben AK, Erber L. Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment. Chem Res Toxicol 2022; 35:1925-1928. [PMID: 36179327 PMCID: PMC9886504 DOI: 10.1021/acs.chemrestox.2c00269] [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: 02/01/2023]
Abstract
Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied across various facets of drug design, development, and safety assessment.
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Affiliation(s)
- Alexander K Hurben
- Department of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Luke Erber
- Department of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
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