101
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Zorn KM, Foil DH, Lane TR, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. Comparing Machine Learning Models for Aromatase (P450 19A1). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:15546-15555. [PMID: 33207874 PMCID: PMC8194505 DOI: 10.1021/acs.est.0c05771] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals. Therefore, predicting potential endocrine disruption via exogenous chemicals requires that aromatase inhibition be considered in addition to androgen and estrogen pathway interference. Bayesian machine learning methods can be used for prospective prediction from the molecular structure without the need for experimental data. Herein, the generation and evaluation of multiple machine learning models utilizing different sources of aromatase inhibition data are described. These models are applied to two test sets for external validation with molecules relevant to drug discovery from the public domain. In addition, the performance of multiple machine learning algorithms was evaluated by comparing internal five-fold cross-validation statistics of the training data. These methods to predict aromatase inhibition from molecular structure, when used in concert with estrogen and androgen machine learning models, allow for a more holistic assessment of endocrine-disrupting potential of chemicals with limited empirical data and enable the reduction of the use of hazardous substances.
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Affiliation(s)
- Kimberley M. Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Daniel H. Foil
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Thomas R. Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Wendy Hillwalker
- Global Product Safety, SC Johnson and Son, Inc., Racine, WI, USA
| | | | - Frank Jones
- Global Product Safety, SC Johnson and Son, Inc., Racine, WI, USA
| | | | | | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
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102
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Lunghini F, Gilles M, Azam P, Enrici MH, Van Miert E, Varnek A. Visualization and Analysis of the REACH-chemical Space with Generative Topographic Mapping. Mol Inform 2020; 40:e2000232. [PMID: 33231933 DOI: 10.1002/minf.202000232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/13/2020] [Indexed: 11/09/2022]
Abstract
In the framework of REACH (Registration Evaluation Authorization and restriction of Chemicals) regulation, industries have generated and reported a huge amount of (eco)toxicological data on substance produced or imported in Europe. The registration procedure initiated the creation of a large REACH database of well defined (eco)toxicological properties. Here, the data distribution in the REACH chemical space was analyzed with the help of the Generative Topographic Mapping (GTM) approach. GTM generates 2-dimensional maps on which each compound is represented as a data point. The 3rd dimension can be used in order to display a distribution of the given (eco)toxicological property, which can further be used for property assessment of new compounds projected on the map. We report the "Universal REACH map" which accommodates 11 endpoints, covering environmental fate and (eco)toxicological properties. This map demonstrates acceptable predictive performance: in cross-validation, balanced accuracy ranges from 0.60 to 0.78. The 11 endpoints profile has been computed for each REACH-registered substance. Some concerns related to acute aquatic toxicity have been identified, whereas for environmental fate and human health endpoints the amount of compounds predicted as of concern was much smaller. It has been demonstrated that superposition of several class landscapes allows to select the zones in the chemical space populated by compounds with a given (eco)toxicological profile.
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Affiliation(s)
- Filippo Lunghini
- Laboratory of Chemoinformatics - UMR7140, University of Strasbourg, 4 Rue Blaise Pascal, 67081, Strasbourg, France.,Toxicological and Environmental Risk Assessment unit, Solvay S.A., 85, avenue des Frères Perret, 69192, St. Fons, France
| | - Marcou Gilles
- Laboratory of Chemoinformatics - UMR7140, University of Strasbourg, 4 Rue Blaise Pascal, 67081, Strasbourg, France
| | - Philippe Azam
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., 85, avenue des Frères Perret, 69192, St. Fons, France
| | - Marie-Hélène Enrici
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., 85, avenue des Frères Perret, 69192, St. Fons, France
| | - Erik Van Miert
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., 85, avenue des Frères Perret, 69192, St. Fons, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics - UMR7140, University of Strasbourg, 4 Rue Blaise Pascal, 67081, Strasbourg, France
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103
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Zorn KM, Foil DH, Lane TR, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. Comparison of Machine Learning Models for the Androgen Receptor. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:13690-13700. [PMID: 33085465 PMCID: PMC8243727 DOI: 10.1021/acs.est.0c03984] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The androgen receptor (AR) is a target of interest for endocrine disruption research, as altered signaling can affect normal reproductive and neurological development for generations. In an effort to prioritize compounds with alternative methodologies, the U.S. Environmental Protection Agency (EPA) used in vitro data from 11 assays to construct models of AR agonist and antagonist signaling pathways. While these EPA ToxCast AR models require in vitro data to assign a bioactivity score, Bayesian machine learning methods can be used for prospective prediction from molecule structure alone. This approach was applied to multiple types of data corresponding to the EPA's AR signaling pathway with proprietary software, Assay Central. The training performance of all machine learning models, including six other algorithms, was evaluated by internal 5-fold cross-validation statistics. Bayesian machine learning models were also evaluated with external predictions of reference chemicals to compare prediction accuracies to published results from the EPA. The machine learning model group selected for further studies of endocrine disruption consisted of continuous AC50 data from the February 2019 release of ToxCast/Tox21. These efforts demonstrate how machine learning can be used to predict AR-mediated bioactivity and can also be applied to other targets of endocrine disruption.
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Affiliation(s)
- Kimberley M. Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Daniel H. Foil
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Thomas R. Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Wendy Hillwalker
- Global Product Safety, SC Johnson and Son, Inc., Racine, WI, USA
| | | | - Frank Jones
- Global Product Safety, SC Johnson and Son, Inc., Racine, WI, USA
| | | | | | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
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104
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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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105
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Zorn KM, Foil DH, Lane TR, Russo DP, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S. Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:12202-12213. [PMID: 32857505 PMCID: PMC8194504 DOI: 10.1021/acs.est.0c03982] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.
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Affiliation(s)
- Kimberley M Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel H Foil
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Daniel P Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey 08102, United States
| | - Wendy Hillwalker
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - David J Feifarek
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Frank Jones
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - William D Klaren
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Ashley M Brinkman
- Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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106
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Valsecchi C, Grisoni F, Motta S, Bonati L, Ballabio D. NURA: A curated dataset of nuclear receptor modulators. Toxicol Appl Pharmacol 2020; 407:115244. [PMID: 32961130 DOI: 10.1016/j.taap.2020.115244] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 08/27/2020] [Accepted: 09/14/2020] [Indexed: 01/10/2023]
Abstract
Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.3991561.
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Affiliation(s)
- Cecile Valsecchi
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126 Milano, Italy
| | - Francesca Grisoni
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Vladimir-Prelog-Weg 4, 8049 Zurich, Switzerland.
| | - Stefano Motta
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126 Milano, Italy
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126 Milano, Italy
| | - Davide Ballabio
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126 Milano, Italy
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107
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Tan H, Wang X, Hong H, Benfenati E, Giesy JP, Gini GC, Kusko R, Zhang X, Yu H, Shi W. Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:11424-11433. [PMID: 32786601 DOI: 10.1021/acs.est.0c02639] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.
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Affiliation(s)
- Haoyue Tan
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Xiaoxiang Wang
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Huixiao Hong
- National Center for Toxicological Research US Food and Drug Administration, 3900 NCTR Rd., Jefferson 72079, Arkansas, United States
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G. La Masa 19, Milan 20156, Italy
| | - John P Giesy
- Toxicology Centre, University of Saskatchewan, Saskatoon S7N 5B3, Canada
- Department of Veterinary Biomedical Sciences, University of Saskatchewan, Saskatoon S7N 5B4, Canada
- Department of Environmental Sciences, Baylor University, Waco 76706, Texas, United States
| | - Giuseppina C Gini
- Department of Electronics and Information, Politecnico di Milano, Piazza L. da Vinci 32, Milano 20133, Italy
| | - Rebeca Kusko
- Immuneering Corporation, Cambridge 02142, Massachusetts, United States
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Wei Shi
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
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108
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Bell S, Abedini J, Ceger P, Chang X, Cook B, Karmaus AL, Lea I, Mansouri K, Phillips J, McAfee E, Rai R, Rooney J, Sprankle C, Tandon A, Allen D, Casey W, Kleinstreuer N. An integrated chemical environment with tools for chemical safety testing. Toxicol In Vitro 2020; 67:104916. [PMID: 32553663 PMCID: PMC7393692 DOI: 10.1016/j.tiv.2020.104916] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/29/2020] [Accepted: 06/10/2020] [Indexed: 12/27/2022]
Abstract
Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.
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Affiliation(s)
- Shannon Bell
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Jaleh Abedini
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Patricia Ceger
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Bethany Cook
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Agnes L Karmaus
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Isabel Lea
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Jason Phillips
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - Eric McAfee
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - Ruhi Rai
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John Rooney
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Catherine Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Arpit Tandon
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - David Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
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109
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Levine SL, Webb EG, Saltmiras DA. Review and analysis of the potential for glyphosate to interact with the estrogen, androgen and thyroid pathways. PEST MANAGEMENT SCIENCE 2020; 76:2886-2906. [PMID: 32608552 DOI: 10.1002/ps.5983] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/23/2020] [Accepted: 07/01/2020] [Indexed: 06/11/2023]
Abstract
Glyphosate was recently evaluated for its potential to interact with the estrogen, androgen and thyroid (EAT) hormone pathways, including steroidogenesis, under the United States Environmental Protection Agency's (USEPA) Endocrine Disruptor Screening Program (EDSP), then by Germany, the rapporteur Member State who led the European Annex 1 renewal for glyphosate, and then by the European Food Protection Agency (EFSA) also as part of the Annex 1 renewal for glyphosate. Under the EDSP, 11 Tier 1 assays were run following the USEPA's validated 890-series test guidelines and included five in vitro and six in vivo assays to evaluate the EAT pathways. Steroidogenesis was evaluated as part of the estrogen and androgen pathways. An up-to-date critical review has been conducted that considered results from the EDSP Tier 1 battery, guideline regulatory studies and an in-depth analysis of the literature studies that informed an endocrine assessment. A strength of this evaluation was that it included data across multiple levels of biological organization, and mammalian and nonmammalian test systems. There was strong agreement across the in vitro and in vivo Tier 1 battery, guideline studies and relevant literature studies, demonstrating that glyphosate does not interact with EAT pathways including steroidogenesis. Based on an analysis of the comprehensive toxicology database for glyphosate and the literature, this review has concluded that glyphosate does not have endocrine-disrupting properties through estrogen, androgen, thyroid and steroidogenic modes of action. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Steven L Levine
- Global Regulatory Science, Bayer Crop Science, Chesterfield, MO, USA
| | - Elizabeth G Webb
- Global Regulatory Science, Bayer Crop Science, Chesterfield, MO, USA
| | - David A Saltmiras
- Global Regulatory Science, Bayer Crop Science, Chesterfield, MO, USA
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110
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Baker CM, Kidley NJ, Papachristos K, Hotson M, Carson R, Gravestock D, Pouliot M, Harrison J, Dowling A. Tautomer Standardization in Chemical Databases: Deriving Business Rules from Quantum Chemistry. J Chem Inf Model 2020; 60:3781-3791. [PMID: 32644790 DOI: 10.1021/acs.jcim.0c00232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear only once, the multiplicity of ways in which many compounds can be represented means that these databases require methods for standardizing the representation of chemistry. This is commonly achieved through the use of "Chemistry Business Rules", sets of predefined rules that describe the "house style" of the database in question. At Syngenta, the historical approach to the design of chemistry business rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, we overturn that convention. Through the use of quantum chemistry calculations, we define a set of chemistry business rules for tautomer standardization that reproduces gas-phase energetic preferences. We go on to show that, compared to our historic approach, this method yields tautomers that are in better agreement with those observed experimentally in condensed phases and that are better suited for use in predictive models.
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Affiliation(s)
- Christopher M Baker
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire RG42 6EY, U.K
| | - Nathan J Kidley
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire RG42 6EY, U.K
| | | | - Matthew Hotson
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire RG42 6EY, U.K
| | - Rob Carson
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire RG42 6EY, U.K
| | - David Gravestock
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire RG42 6EY, U.K
| | - Martin Pouliot
- Syngenta Crop Protection, Schaffhauserstrasse, Stein CH-4332, Switzerland
| | - Jim Harrison
- Datacraft Technologies, 110 Parkwood Place, Anstead, QLD 4070, Australia
| | - Alan Dowling
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire RG42 6EY, U.K
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111
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Kamerlin N, Delcey MG, Manzetti S, van der Spoel D. Toward a Computational Ecotoxicity Assay. J Chem Inf Model 2020; 60:3792-3803. [PMID: 32648756 DOI: 10.1021/acs.jcim.0c00574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.
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Affiliation(s)
- Natasha Kamerlin
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Mickaël G Delcey
- Department of Chemistry-Ångström Laboratory, Uppsala University, SE-75120 Uppsala, Sweden
| | - Sergio Manzetti
- Institute for Science and Technology, Fjordforsk A.S., Midtun, 6894 Vangsnes, Norway
| | - David van der Spoel
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
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112
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Judson R, Houck K, Paul Friedman K, Brown J, Browne P, Johnston PA, Close DA, Mansouri K, Kleinstreuer N. Selecting a minimal set of androgen receptor assays for screening chemicals. Regul Toxicol Pharmacol 2020; 117:104764. [PMID: 32798611 PMCID: PMC8356084 DOI: 10.1016/j.yrtph.2020.104764] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/03/2020] [Accepted: 08/07/2020] [Indexed: 01/01/2023]
Abstract
Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modeling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. Agonist batteries of as few as six assays and antagonist batteries of as few as five assays can yield balanced accuracies of 95% or better relative to the full model. Balanced accuracy for predicting reference chemicals is 100%. An approach is outlined for researchers to develop their own subset batteries to accurately detect AR activity using assays that map to the pathway of key molecular and cellular events involved in chemical-mediated AR activation and transcriptional activity. This work indicates in vitro bioactivity and in silico predictions that map to the AR pathway could be used in an integrated approach to testing and assessment for identifying chemicals that interact directly with the mammalian AR.
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Affiliation(s)
| | - Keith Houck
- U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Jason Brown
- U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Paul A Johnston
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Close
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., Morrisville, NC, USA
| | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, RTP, NC, USA
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113
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The TTC Data Mart: An interactive browser for threshold of toxicological concern calculations. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.comtox.2020.100128] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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114
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Tang W, Chen J, Hong H. Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training. CHEMOSPHERE 2020; 253:126768. [PMID: 32464767 DOI: 10.1016/j.chemosphere.2020.126768] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/08/2020] [Accepted: 04/08/2020] [Indexed: 06/11/2023]
Abstract
Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure-activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.
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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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115
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Patlewicz G. Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon. FRONTIERS IN TOXICOLOGY 2020; 2:2. [PMID: 35296116 PMCID: PMC8915910 DOI: 10.3389/ftox.2020.00002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/20/2020] [Indexed: 01/07/2023] Open
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116
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Stossi F, Mistry RM, Singh PK, Johnson HL, Mancini MG, Szafran AT, Mancini MA. Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals. SLAS DISCOVERY 2020; 25:684-694. [PMID: 32552291 DOI: 10.1177/2472555220934420] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cell-to-cell variation of protein expression in genetically homogeneous populations is a common biological trait often neglected during analysis of high-throughput (HT) screens and is rarely used as a metric to characterize chemicals. We have captured single-cell distributions of androgen receptor (AR) nuclear levels after perturbations as a means to evaluate assay reproducibility and characterize a small subset of chemicals. AR, a member of the nuclear receptor family of transcription factors, is the central regulator of male reproduction and is involved in many pathophysiological processes. AR protein levels and nuclear localization often increase following ligand binding, with dihydrotestosterone (DHT) being the natural agonist. HT AR immunofluorescence imaging was used in multiple cell lines to define single-cell nuclear values extracted from thousands of cells per condition treated with DHT or DMSO (control). Analysis of numerous biological replicates led to a quality control metric that takes into account the distribution of single-cell data, and how it changes upon treatments. Dose-response experiments across several cell lines showed a large range of sensitivity to DHT, prompting us to treat selected cell lines with 45 Environmental Protection Agency (EPA)-provided chemicals that include many endocrine-disrupting chemicals (EDCs); data from six of the compounds were then integrated with orthogonal assays. Our comprehensive results indicate that quantitative single-cell distribution analysis of AR protein levels is a valid method to detect the potential androgenic and antiandrogenic actions of environmentally relevant chemicals in a sensitive and reproducible manner.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.,Integrated Microscopy Core, Baylor College of Medicine, Houston, TX, USA.,GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA
| | - Ragini M Mistry
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.,Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Hannah L Johnson
- Integrated Microscopy Core, Baylor College of Medicine, Houston, TX, USA.,GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA
| | - Maureen G Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Adam T Szafran
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.,Integrated Microscopy Core, Baylor College of Medicine, Houston, TX, USA.,GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.,Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA.,Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA.,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
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117
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Yuan C, Tebes-Stevens C, Weber EJ. Reaction Library to Predict Direct Photochemical Transformation Products of Environmental Organic Contaminants in Sunlit Aquatic Systems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:7271-7279. [PMID: 32374162 PMCID: PMC7539852 DOI: 10.1021/acs.est.0c00484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Cheminformatics-based applications to predict transformation pathways of environmental contaminants are useful to quickly prioritize contaminants with potentially toxic/persistent products. Direct photolysis can be an important degradation pathway for sunlight-absorbing compounds in aquatic systems. In this study, we developed the first freely available direct phototransformation pathway predictive tool, which uses a rule-based reaction library. Journal publications studying diverse contaminants (such as pesticides, pharmaceuticals, and energetic compounds) were systematically compiled to encode 155 reaction schemes into the reaction library. The execution result of this predictive tool was internally evaluated against 390 compounds from the compiled journal publications and externally evaluated against 138 compounds from the regulatory reports. The recall (sensitivity) and precision (selectivity) were 0.62 and 0.35, respectively, for internal evaluation, and 0.56 and 0.20, respectively, for external evaluation, when only the products formed from the first reaction step were counted. This predictive tool could help to narrow the data gaps in chemical registration/evaluation and inform future experimental studies.
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Affiliation(s)
- Chenyi Yuan
- Oak Ridge Institute for Science and Education (ORISE), hosted at United States Environmental Protection Agency, Athens, Georgia 30605, United States
| | - Caroline Tebes-Stevens
- Center for Environmental Measurement and Modeling, United States Environmental Protection Agency, Athens, Georgia 30605, United States
| | - Eric J. Weber
- Center for Environmental Measurement and Modeling, United States Environmental Protection Agency, Athens, Georgia 30605, United States
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118
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Allen TEH, Nelms MD, Edwards SW, Goodman JM, Gutsell S, Russell PJ. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:7461-7470. [PMID: 32432465 DOI: 10.1021/acs.est.0c01105] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.
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Affiliation(s)
- Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
- MRC Toxicology Unit, University of Cambridge, Hodgkin Building, Lancaster Road, Leicester LE1 7HB, U.K
| | - Mark D Nelms
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37830, United States
- Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Stephen W Edwards
- Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
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119
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Bafna D, Ban F, Rennie PS, Singh K, Cherkasov A. Computer-Aided Ligand Discovery for Estrogen Receptor Alpha. Int J Mol Sci 2020; 21:E4193. [PMID: 32545494 PMCID: PMC7352601 DOI: 10.3390/ijms21124193] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/30/2020] [Accepted: 06/09/2020] [Indexed: 02/08/2023] Open
Abstract
Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada; (D.B.); (F.B.); (P.S.R.); (K.S.)
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120
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 327] [Impact Index Per Article: 81.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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121
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Browne P, Van Der Wal L, Gourmelon A. OECD approaches and considerations for regulatory evaluation of endocrine disruptors. Mol Cell Endocrinol 2020; 504:110675. [PMID: 31830512 DOI: 10.1016/j.mce.2019.110675] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 11/20/2019] [Accepted: 12/02/2019] [Indexed: 12/18/2022]
Abstract
Identifying the potential endocrine disruptor hazard of environmental chemicals is a regulatory mandate for many countries. However, due to the adaptive nature of the endocrine system, absence of a single method capable of identifying endocrine disruption, and the latency between exposure to endocrine disrupting chemical during sensitive life stages and the manifestation of adverse responses, satisfying the regulatory requirement needed to identify a chemical as an endocrine disruptor is a challenge. There are now a variety of validated regulatory tests that can be used in combination to provide evidence that a chemical affects the oestrogen, androgen, thyroid, and steroidogenic pathways of vertebrates, but most rely (at least to some extent) on animal testing and require considerable cost and time to produce the necessary data. Emerging research methods are able to evaluate other endocrine pathways, incorporate more sensitive endpoints, and combine multiple alternative methods to predict in vivo outcomes. Some research approaches may also bridge gaps that have been identified in current endocrine regulatory testing. For the near term, considering new endpoints in a regulatory context may require adding them to existing test methods in order to establish relationships between the traditional and the innovative. From the outset, endocrine testing has always required integration of multiple methods that provide data on different levels of biological organisation, thus, the area of endocrine disruption is particularly adaptable to adverse outcome pathway (AOP) frameworks and integrated test methods built around AOPs. Herein, we provide a review of the status of endocrine disruptors in the OECD context, examples where innovation from research is needed to improve or bridge gaps in endocrine testing, and suggestions for regulators and researchers to facilitate uptake of innovate methods for endocrine disruptor regulatory testing. The increase in several human complex human disorders that include an endocrine component and the alarming decrease in wildlife biodiversity are commanding directives to include the best, most informative, innovative approaches to accelerate the rate and throughput of chemical evaluation for endocrine disruption.
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Affiliation(s)
- Patience Browne
- Organisation for Economic Cooperation and Development, Environment Directorate, Paris, France.
| | - Leon Van Der Wal
- Organisation for Economic Cooperation and Development, Environment Directorate, Paris, France
| | - Anne Gourmelon
- Organisation for Economic Cooperation and Development, Environment Directorate, Paris, France
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122
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Valsecchi C, Grisoni F, Consonni V, Ballabio D. Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study. J Chem Inf Model 2020; 60:1215-1223. [PMID: 32073844 PMCID: PMC7997107 DOI: 10.1021/acs.jcim.9b01057] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
![]()
Consensus strategies have been widely
applied in many different
scientific fields, based on the assumption that the fusion of several
sources of information increases the outcome reliability. Despite
the widespread application of consensus approaches, their advantages
in quantitative structure–activity relationship (QSAR) modeling
have not been thoroughly evaluated, mainly due to the lack of appropriate
large-scale data sets. In this study, we evaluated the advantages
and drawbacks of consensus approaches compared to single classification
QSAR models. To this end, we used a data set of three properties (androgen
receptor binding, agonism, and antagonism) for approximately 4000
molecules with predictions performed by more than 20 QSAR models,
made available in a large-scale collaborative project. The individual
QSAR models were compared with two consensus approaches, majority
voting and the Bayes consensus with discrete probability distributions,
in both protective and nonprotective forms. Consensus strategies proved
to be more accurate and to better cover the analyzed chemical space
than individual QSARs on average, thus motivating their widespread
application for property prediction. Scripts and data to reproduce
the results of this study are available for download.
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Affiliation(s)
- Cecile Valsecchi
- Milano Chemometrics and QSAR Research Group, University of Milano Bicocca, P.za della Scienza 1, 20126 Milano, Italy
| | - Francesca Grisoni
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8049 Zurich, Switzerland
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano Bicocca, P.za della Scienza 1, 20126 Milano, Italy
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, University of Milano Bicocca, P.za della Scienza 1, 20126 Milano, Italy
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123
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Xi Y, Yang X, Zhang H, Liu H, Watson P, Yang F. Binding interactions of halo-benzoic acids, halo-benzenesulfonic acids and halo-phenylboronic acids with human transthyretin. CHEMOSPHERE 2020; 242:125135. [PMID: 31669991 DOI: 10.1016/j.chemosphere.2019.125135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 06/10/2023]
Abstract
The anionic form-dependent binding interaction of halo-phenolic substances with human transthyretin (hTTR) has been observed previously. This indicates that ionizable compounds should be the primary focus in screening potential hTTR disruptors. Here, the potential binding potency of halo-benzoic acids, halo-benzenesulfonic acids/sulfates and halo-phenylboronic acids with hTTR was determined and analyzed by competitive fluorescence displacement assay integrated with computational methods. The laboratorial results indicated that the three test groups of model compounds exhibited a distinct binding affinity to hTTR. All the tested halo-phenylboronic acids, some of the tested halo-benzoic acids and halo-benzenesulfonic acids/sulfates were shown to be inactive with hTTR. Other halo-benzoic acids and halo-benzenesulfonic acids/sulfates were moderate and/or weak hTTR binders. The binding affinity of halo-benzoic acids and halo-benzenesulfonic acids/sulfates with hTTR was similar. The low distribution ability of the model compounds from water to hTTR may be the reason why they exhibited the binding potency observed with hTTR. By introducing other highly hydrophobic compounds, we observed that the binding affinity between compounds and hTTR increased with increasing molecular hydrophobicity. Those results indicated that the highly hydrophobic halo-benzoic acids and halo-benzenesulfonic acids/sulfates may be high-priority hTTR disruptors. Finally, a binary classification model was constructed employing three predictive variables. The sensitivity (Sn), specificity (Sp), predictive accuracy (Q) values of the training set and validation set were >0.83, indicating that the model had good classification performance. Thus, the binary classification model developed here could be used to distinguish whether a given ionizable compound is a potential hTTR binder or not.
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Affiliation(s)
- Yue Xi
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Xianhai Yang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Hongyu Zhang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Peter Watson
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, 06268, CT, United States
| | - Feifei Yang
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, 06268, CT, United States
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124
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Value and limitation of structure-based profilers to characterize developmental and reproductive toxicity potential. Arch Toxicol 2020; 94:939-954. [PMID: 32100055 DOI: 10.1007/s00204-020-02671-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 02/11/2020] [Indexed: 10/24/2022]
Abstract
The uncertainty regarding the safety of chemicals leaching from food packaging triggers attention. In silico models provide solutions for screening of these chemicals, since many are toxicologically uncharacterized. For hazard assessment, information on developmental and reproductive toxicity (DART) is needed. The possibility to apply in silico toxicology to identify and quantify DART alerts was investigated. Open-source models and profilers were applied to 195 packaging chemicals and analogues. An approach based on DART and estrogen receptor (ER) binding profilers and molecular docking was able to identify all except for one chemical with documented DART properties. Twenty percent of the chemicals in the database known to be negative in experimental studies were classified as positive. The scheme was then applied to 121 untested chemicals. Alerts were identified for sixteen of them, five being packaging substances, the others structural analogues. Read-across was then developed to translate alerts into quantitative toxicological values. They can be used to calculate margins of exposure (MoE), the size of which reflects safety concern. The application of this approach appears valuable for hazard characterization of toxicologically untested packaging migrants. It is an alternative to the use of default uncertainty factor (UF) applied to animal chronic toxicity value to handle absence of DART data in hazard characterization.
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125
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Zhong S, Hu J, Fan X, Yu X, Zhang H. A deep neural network combined with molecular fingerprints (DNN-MF) to develop predictive models for hydroxyl radical rate constants of water contaminants. JOURNAL OF HAZARDOUS MATERIALS 2020; 383:121141. [PMID: 31610411 DOI: 10.1016/j.jhazmat.2019.121141] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 08/29/2019] [Accepted: 09/02/2019] [Indexed: 05/24/2023]
Abstract
This work combined a Deep Neural Network (DNN) with molecular fingerprints (MF) to develop models to predict the OH radical rate constants of 593 organic contaminants. Molecular descriptors, most often used in establishing quantitative structural-activity relationships (QSARs), were not used here because of their complicated generation processes that rely on advanced physicochemical and computational knowledge. Instead, we only fed the most basic information of the contaminant structures, i.e., MF encoding the types of atoms and how they are connected, to DNN and DNN then developed predictive models automatically. Here, a dataset containing 457 contaminants and their OH rate constants was first used to develop predictive models by DNN-MF. The hence developed models showed comparable accuracy to the traditional QSARs. The root mean square error (RMSE) values of the test sets were 0.358-0.384. The length of 2048 bits for the MF and 3 hidden layers (each with 1024 neurons) were found to be the optimal parameters for DNN. The model containing additional 89 micorpollutants in the training set was then successfully applied to predict the OH rate constants of 17 organophosphorus flame retardants and 29 additional micropollutants, with comparable accuracy to the reported molecular descriptors-based QSARs.
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Affiliation(s)
- Shifa Zhong
- Department of Civil Engineering, Case Western Reserve University, 2104 Adelbert Road, Cleveland, OH 44106-7201, USA
| | - Jiajie Hu
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 2104 Adelbert Road, Cleveland, OH 44106-7201, USA
| | - Xudong Fan
- Department of Civil Engineering, Case Western Reserve University, 2104 Adelbert Road, Cleveland, OH 44106-7201, USA
| | - Xiong Yu
- Department of Civil Engineering, Case Western Reserve University, 2104 Adelbert Road, Cleveland, OH 44106-7201, USA; Department of Electrical Engineering and Computer Science, Case Western Reserve University, 2104 Adelbert Road, Cleveland, OH 44106-7201, USA
| | - Huichun Zhang
- Department of Civil Engineering, Case Western Reserve University, 2104 Adelbert Road, Cleveland, OH 44106-7201, USA.
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126
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Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:27002. [PMID: 32074470 DOI: 10.23645/epacomptox.5176876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:27002. [PMID: 32074470 PMCID: PMC7064318 DOI: 10.1289/ehp5580] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/27/2019] [Accepted: 12/05/2019] [Indexed: 05/04/2023]
Abstract
BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M. Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G. Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V. Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen – German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B. Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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Beames T, Moreau M, Roberts LA, Mansouri K, Haider S, Smeltz M, Nicolas CI, Doheny D, Phillips MB, Yoon M, Becker RA, McMullen PD, Andersen ME, Clewell RA, Hartman JK. The role of fit-for-purpose assays within tiered testing approaches: A case study evaluating prioritized estrogen-active compounds in an in vitro human uterotrophic assay. Toxicol Appl Pharmacol 2020; 387:114774. [PMID: 31783037 DOI: 10.1016/j.taap.2019.114774] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/14/2019] [Accepted: 10/02/2019] [Indexed: 12/21/2022]
Abstract
Chemical risk assessment relies on toxicity tests that require significant numbers of animals, time and costs. For the >30,000 chemicals in commerce, the current scale of animal testing is insufficient to address chemical safety concerns as regulatory and product stewardship considerations evolve to require more comprehensive understanding of potential biological effects, conditions of use, and associated exposures. We demonstrate the use of a multi-level new approach methodology (NAMs) strategy for hazard- and risk-based prioritization to reduce animal testing. A Level 1/2 chemical prioritization based on estrogen receptor (ER) activity and metabolic activation using ToxCast data was used to select 112 chemicals for testing in a Level 3 human uterine cell estrogen response assay (IKA assay). The Level 3 data were coupled with quantitative in vitro to in vivo extrapolation (Q-IVIVE) to support bioactivity determination (as a surrogate for hazard) in a tissue-specific context. Assay AC50s and Q-IVIVE were used to estimate human equivalent doses (HEDs), and HEDs were compared to rodent uterotrophic assay in vivo-derived points of departure (PODs). For substances active both in vitro and in vivo, IKA assay-derived HEDs were lower or equivalent to in vivo PODs for 19/23 compounds (83%). Activity exposure relationships were calculated, and the IKA assay was as or more protective of human health than the rodent uterotrophic assay for all IKA-positive compounds. This study demonstrates the utility of biologically relevant fit-for-purpose assays and supports the use of a multi-level strategy for chemical risk assessment.
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Affiliation(s)
- Tyler Beames
- ScitoVation, 100 Capitola Drive, Suite 106, Durham, NC 27713, USA
| | - Marjory Moreau
- ScitoVation, 100 Capitola Drive, Suite 106, Durham, NC 27713, USA
| | - L Avery Roberts
- ScitoVation, 6 Davis Drive, Research Triangle Park, NC 27709, USA
| | - Kamel Mansouri
- ScitoVation, 6 Davis Drive, Research Triangle Park, NC 27709, USA
| | - Saad Haider
- ScitoVation, 100 Capitola Drive, Suite 106, Durham, NC 27713, USA
| | - Marci Smeltz
- ScitoVation, 100 Capitola Drive, Suite 106, Durham, NC 27713, USA
| | | | - Daniel Doheny
- ScitoVation, 6 Davis Drive, Research Triangle Park, NC 27709, USA
| | | | - Miyoung Yoon
- ScitoVation, 6 Davis Drive, Research Triangle Park, NC 27709, USA
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Martyniuk CJ, Feswick A, Munkittrick KR, Dreier DA, Denslow ND. Twenty years of transcriptomics, 17alpha-ethinylestradiol, and fish. Gen Comp Endocrinol 2020; 286:113325. [PMID: 31733209 PMCID: PMC6961817 DOI: 10.1016/j.ygcen.2019.113325] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/14/2019] [Accepted: 11/12/2019] [Indexed: 02/06/2023]
Abstract
In aquatic toxicology, perhaps no pharmaceutical has been investigated more intensely than 17alpha-ethinylestradiol (EE2), the active ingredient of the birth control pill. At the turn of the century, the fields of comparative endocrinology and endocrine disruption research witnessed the emergence of omics technologies, which were rapidly adapted to characterize potential hazards associated with exposures to environmental estrogens, such as EE2. Since then, significant advances have been made by the scientific community, and as a result, much has been learned about estrogen receptor signaling in fish from environmental xenoestrogens. Vitellogenin, the egg yolk precursor protein, was identified as a major estrogen-responsive gene, establishing itself as the premier biomarker for estrogenic exposures. Omics studies have identified a plethora of estrogen responsive genes, contributing to a wealth of knowledge on estrogen-mediated regulatory networks in teleosts. There have been ~40 studies that report on transcriptome responses to EE2 in a variety of fish species (e.g., zebrafish, fathead minnows, rainbow trout, pipefish, mummichog, stickleback, cod, and others). Data on the liver and testis transcriptomes dominate in the literature and have been the subject of many EE2 studies, yet there remain knowledge gaps for other tissues, such as the spleen, kidney, and pituitary. Inter-laboratory genomics studies have revealed transcriptional networks altered by EE2 treatment in the liver; networks related to amino acid activation and protein folding are increased by EE2 while those related to xenobiotic metabolism, immune system, circulation, and triglyceride storage are suppressed. EE2-responsive networks in other tissues are not as comprehensively defined which is a knowledge gap as regulated networks are expected to be tissue-specific. On the horizon, omics studies for estrogen-mediated effects in fish include: (1) Establishing conceptual frameworks for incorporating estrogen-responsive networks into environmental monitoring programs; (2) Leveraging in vitro and computational toxicology approaches to identify chemicals associated with estrogen receptor-mediated effects in fish (e.g., male vitellogenin production); (3) Discovering new tissue-specific estrogen receptor signaling pathways in fish; and (4) Developing quantitative adverse outcome pathway predictive models for estrogen signaling. As we look ahead, research into EE2 over the past several decades can serve as a template for the array of hormones and endocrine active substances yet to be fully characterized or discovered.
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Affiliation(s)
- Christopher J Martyniuk
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada; Center for Environmental & Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA; University of Florida Genetics Institute, USA; Canadian Rivers Institute, Canada.
| | - April Feswick
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada; Canadian Rivers Institute, Canada
| | - Kelly R Munkittrick
- Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada; Department of Biology, Wilfrid Laurier University, Waterloo, ON, Canada; Canadian Rivers Institute, Canada
| | - David A Dreier
- Center for Environmental & Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA; Syngenta Crop Protection, LLC, Greensboro, NC, USA
| | - Nancy D Denslow
- Center for Environmental & Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA; University of Florida Genetics Institute, USA
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130
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Selvaraj C, Selvaraj G, Kaliamurthi S, Cho WC, Wei DQ, Singh SK. Ion Channels as Therapeutic Targets for Type 1 Diabetes Mellitus. Curr Drug Targets 2020; 21:132-147. [PMID: 31538892 DOI: 10.2174/1389450119666190920152249] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 08/06/2019] [Accepted: 08/07/2019] [Indexed: 02/07/2023]
Abstract
Ion channels are integral proteins expressed in almost all living cells and are involved in muscle contraction and nutrient transport. They play a critical role in the normal functioning of the excitable tissues of the nervous system and regulate the action potential and contraction events. Dysfunction of genes encodes ion channel proteins, which disrupt the channel function and lead to a number of diseases, among which is type 1 diabetes mellitus (T1DM). Therefore, understanding the complex mechanism of ion channel receptors is necessary to facilitate the diagnosis and management of treatment. In this review, we summarize the mechanism of important ion channels and their potential role in the regulation of insulin secretion along with the limitations of ion channels as therapeutic targets. Furthermore, we discuss the recent investigations of the mechanism regulating the ion channels in pancreatic beta cells, which suggest that ion channels are active participants in the regulation of insulin secretion.
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Affiliation(s)
- Chandrabose Selvaraj
- Department of Bioinformatics, Computer-Aided Drug Design, and Molecular Modeling Lab, Science Block, Alagappa University, Karaikudi, Tamil Nadu, 630004, India
| | - Gurudeeban Selvaraj
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China
| | - Satyavani Kaliamurthi
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China
| | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong
| | - Dong-Qing Wei
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China
- Department of Bioinformatics, The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Sanjeev Kumar Singh
- Department of Bioinformatics, Computer-Aided Drug Design, and Molecular Modeling Lab, Science Block, Alagappa University, Karaikudi, Tamil Nadu, 630004, India
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131
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Schneider M, Pons JL, Bourguet W, Labesse G. Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity. Bioinformatics 2020; 36:160-168. [PMID: 31350558 PMCID: PMC6956784 DOI: 10.1093/bioinformatics/btz538] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/29/2019] [Accepted: 07/19/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα). RESULTS VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted. AVAILABILITY AND IMPLEMENTATION http://edmon.cbs.cnrs.fr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Melanie Schneider
- Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, 34090 Montpellier, France
| | - Jean-Luc Pons
- Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, 34090 Montpellier, France
| | - William Bourguet
- Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, 34090 Montpellier, France
| | - Gilles Labesse
- Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, 34090 Montpellier, France
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132
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Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, Wambaugh JF, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch Toxicol 2019; 94:1-58. [DOI: 10.1007/s00204-019-02613-4] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022]
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133
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Sheffield TY, Judson RS. Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:12793-12802. [PMID: 31560848 PMCID: PMC7047609 DOI: 10.1021/acs.est.9b03957] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
QSAR modeling can be used to aid testing prioritization of the thousands of chemical substances for which no ecological toxicity data are available. We drew on the U.S. Environmental Protection Agency's ECOTOX database with additional data from ECHA to build a large data set containing in vivo test data on fish for thousands of chemical substances. This was used to create QSAR models to predict two types of end points: acute LC50 (median lethal concentration) and points of departure similar to the NOEC (no observed effect concentration) for any duration (named the "LC50" and "NOEC" models, respectively). These models used study covariates, such as species and exposure route, as features to facilitate the simultaneous use of varied data types. A novel method of substituting taxonomy groups for species dummy variables was introduced to maximize generalizability to different species. A stacked ensemble of three machine learning methods-random forest, gradient boosted trees, and support vector regression-was implemented to best make use of a large data set with many descriptors. The LC50 and NOEC models predicted end points within 1 order of magnitude 81% and 76% of the time, respectively, and had RMSEs of roughly 0.83 and 0.98 log10(mg/L), respectively. Benchmarks against the existing TEST and ECOSAR tools suggest improved prediction accuracy.
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Affiliation(s)
- Thomas Y. Sheffield
- U.S. Department of Energy, Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37830, USA
| | - Richard S. Judson
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, NC, 27709, USA
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134
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Devillers J, Devillers H. Toxicity profiling and prioritization of plant-derived antimalarial agents. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:801-824. [PMID: 31565973 DOI: 10.1080/1062936x.2019.1665844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 09/06/2019] [Indexed: 06/10/2023]
Abstract
Human malaria is the most widespread mosquito-borne life-threatening disease worldwide. In the absence of effective vaccines, prevention and treatment of malaria only depend on prophylaxis and drug-based therapy either in monotherapy or in combination. Unfortunately, the number of available antimalarial drugs presenting different mechanisms of action is rather limited. In addition, the appearance of drug-resistance in the parasite strains impacts the efficacy of the treatments. As a result, there is a crucial need to find new drugs to circumvent resistance problems. In the quest to identify new antimalarial agents a huge number of plant-derived compounds (PDCs) have been investigated. Surprisingly in the in silico PDC screening programs, toxicity filters are either never used or so simple that their interest is limited. In this context, the goal of this study was to show how to take advantage of validated toxicity QSAR models for refining the selection of PDCs. From an original data set of 507 PDCs collected from the literature, the use of toxicity filters for endocrine disruption, developmental toxicity, and hepatotoxicity in conjunction with classical pharmacokinetic filters allowed us to obtain a list of 31 compounds of potential interest. The pros and cons of such a strategy have been discussed.
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Affiliation(s)
| | - H Devillers
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay , Jouy-en-Josas , France
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135
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Schneider M, Pons JL, Labesse G, Bourguet W. In Silico Predictions of Endocrine Disruptors Properties. Endocrinology 2019; 160:2709-2716. [PMID: 31265055 PMCID: PMC6804484 DOI: 10.1210/en.2019-00382] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 06/26/2019] [Indexed: 01/12/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.
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Affiliation(s)
- Melanie Schneider
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Jean-Luc Pons
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Gilles Labesse
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
| | - William Bourguet
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
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136
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Zakharov AV, Zhao T, Nguyen DT, Peryea T, Sheils T, Yasgar A, Huang R, Southall N, Simeonov A. Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models. J Chem Inf Model 2019; 59:4613-4624. [PMID: 31584270 DOI: 10.1021/acs.jcim.9b00526] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .
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Affiliation(s)
- Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Tongan Zhao
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Tyler Peryea
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Timothy Sheils
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Adam Yasgar
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Ruili Huang
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Noel Southall
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
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137
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Benfenati E, Chaudhry Q, Gini G, Dorne JL. Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy. ENVIRONMENT INTERNATIONAL 2019; 131:105060. [PMID: 31377600 DOI: 10.1016/j.envint.2019.105060] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 06/26/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.
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Affiliation(s)
- Emilio Benfenati
- Department of Environmental and Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano, Italy.
| | - Qasim Chaudhry
- University of Chester, Parkgate Road, Chester CH1 4BJ, United Kingdom
| | | | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
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138
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Pinto CL, Bloom RA, Laurenson JP. An Approach for Using In Vitro and In Silico Data to Identify Pharmaceuticals with Potential (Anti-)Estrogenic Activity in Aquatic Vertebrates at Environmentally Relevant Concentrations. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2019; 38:2154-2168. [PMID: 31291026 DOI: 10.1002/etc.4533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 05/06/2019] [Accepted: 07/02/2019] [Indexed: 06/09/2023]
Abstract
Endocrine-active pharmaceuticals can cause adverse reproductive and developmental effects in nontarget organisms. Aquatic vertebrates may be susceptible to the effects of such pharmaceuticals given that the structure of hormone receptors and the physiology of the endocrine system are highly conserved across vertebrates. To aid in the regulatory review of the environmental impact of drugs, we demonstrate an approach to screen and support the prioritization of pharmaceuticals based on their ability to interact with estrogen receptors (ERs) at environmentally relevant concentrations. Tox21 in vitro results from ER agonist and antagonist assays were retrieved for 1123 pharmaceuticals. In silico predictions from the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) models were used to estimate ER agonist and antagonist activity for an additional 170 pharmaceuticals not tested in the Tox21 assay platform. The estrogenic effect ratio (EER) and anti-estrogenic effect ratio (AEER) were calculated by comparing the activity concentration at half-maximal response (AC50) for ER agonism and antagonism, respectively, with estimated pharmaceutical concentrations in fish tissue based on estimates of environmental exposures. A total of 73 and 127 pharmaceuticals were identified as ER agonists and antagonists, respectively. As expected, 17β-estradiol and 17α-ethinylestradiol displayed EERs > 1, and raloxifene and bazedoxifene acetate displayed AEERs > 1, thus indicating that these pharmaceuticals have the potential to reach fish tissue levels that exceed concentrations estimated to interact with ERs. Four pharmaceuticals displayed EERs between 0.1 and 1, and 6 displayed AEERs between 0.1 and 1. This approach may help determine the need for submission of environmental assessment data for new drug applications and support prioritization of pharmaceuticals with the potential to disrupt endocrine signaling in vertebrates. Environ Toxicol Chem 2019;38:2154-2168. © 2019 SETAC.
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Affiliation(s)
- Caroline Lucia Pinto
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Raanan A Bloom
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - James P Laurenson
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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139
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Alofe O, Kisanga E, Inayat-Hussain SH, Fukumura M, Garcia-Milian R, Perera L, Vasiliou V, Whirledge S. Determining the endocrine disruption potential of industrial chemicals using an integrative approach: Public databases, in vitro exposure, and modeling receptor interactions. ENVIRONMENT INTERNATIONAL 2019; 131:104969. [PMID: 31310931 PMCID: PMC6728168 DOI: 10.1016/j.envint.2019.104969] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 06/25/2019] [Accepted: 06/26/2019] [Indexed: 05/18/2023]
Abstract
Environmental and occupational exposure to industrial chemicals has been linked to toxic and carcinogenic effects in animal models and human studies. However, current toxicology testing does not thoroughly explore the endocrine disrupting effects of industrial chemicals, which may have low dose effects not predicted when determining the limit of toxicity. The objective of this study was to evaluate the endocrine disrupting potential of a broad range of chemicals used in the petrochemical sector. Therefore, 139 chemicals were classified for reproductive toxicity based on the United Nations Globally Harmonized System for hazard classification. These chemicals were evaluated in PubMed for reported endocrine disrupting activity, and their endocrine disrupting potential was estimated by identifying chemicals with active nuclear receptor endpoints publicly available databases. Evaluation of ToxCast data suggested that these chemicals preferentially alter the activity of the estrogen receptor (ER). Four chemicals were prioritized for in vitro testing using the ER-positive, immortalized human uterine Ishikawa cell line and a range of concentrations below the reported limit of toxicity in humans. We found that 2,6-di-tert-butyl-p-cresol (BHT) and diethanolamine (DEA) repressed the basal expression of estrogen-responsive genes PGR, NPPC, and GREB1 in Ishikawa cells, while tetrachloroethylene (PCE) and 2,2'-methyliminodiethanol (MDEA) induced the expression of these genes. Furthermore, low-dose combinations of PCE and MDEA produced additive effects. All four chemicals interfered with estradiol-mediated induction of PGR, NPPC, and GREB1. Molecular docking demonstrated that these chemicals could bind to the ligand binding site of ERα, suggesting the potential for direct stimulatory or inhibitory effects. We found that these chemicals altered rates of proliferation and regulated the expression of cell proliferation associated genes. These findings demonstrate previously unappreciated endocrine disrupting effects and underscore the importance of testing the endocrine disrupting potential of chemicals in the future to better understand their potential to impact public health.
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Affiliation(s)
- Olubusayo Alofe
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Edwina Kisanga
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Salmaan H Inayat-Hussain
- Department of Product Stewardship and Toxicology, Group Health, Safety, Security and Environment, Petroliam Nasional Berhad, Kuala Lumpur, Malaysia; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Masao Fukumura
- Department of Product Stewardship and Toxicology, Group Health, Safety, Security and Environment, Petroliam Nasional Berhad, Kuala Lumpur, Malaysia
| | - Rolando Garcia-Milian
- Bioinformatics Support Program, Cushing/Whitney Medical Library, Yale School of Medicine, New Haven, CT, USA
| | - Lalith Perera
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Shannon Whirledge
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, CT, USA; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
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140
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Mansouri K, Cariello NF, Korotcov A, Tkachenko V, Grulke CM, Sprankle CS, Allen D, Casey WM, Kleinstreuer NC, Williams AJ. Open-source QSAR models for pKa prediction using multiple machine learning approaches. J Cheminform 2019; 11:60. [PMID: 33430972 PMCID: PMC6749653 DOI: 10.1186/s13321-019-0384-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 09/03/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. METHODS The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure-activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). RESULTS The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. CONCLUSIONS This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.
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Affiliation(s)
- Kamel Mansouri
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - Neal F. Cariello
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - Alexandru Korotcov
- Science Data Software LLC, 14914 Bradwill Court, Rockville, MD 20850 USA
| | - Valery Tkachenko
- Science Data Software LLC, 14914 Bradwill Court, Rockville, MD 20850 USA
| | - Chris M. Grulke
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Mail Code D143-02, Research Triangle Park, NC 27709 USA
| | - Catherine S. Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - David Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709 USA
| | - Warren M. Casey
- National Institute of Environmental Health Sciences, P.O. Box 12233, Mail Stop K2-16, Research Triangle Park, NC 27709 USA
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, P.O. Box 12233, Mail Stop K2-16, Research Triangle Park, NC 27709 USA
| | - Antony J. Williams
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Mail Code D143-02, Research Triangle Park, NC 27709 USA
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141
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Margina D, Nițulescu GM, Ungurianu A, Mesnage R, Goumenou M, Sarigiannis DA, Aschner M, Spandidos DA, Renieri EA, Hernández AF, Tsatsakis A. Overview of the effects of chemical mixtures with endocrine disrupting activity in the context of real-life risk simulation: An integrative approach (Review). WORLD ACADEMY OF SCIENCES JOURNAL 2019; 1:157-164. [PMID: 32346674 PMCID: PMC7188405 DOI: 10.3892/wasj.2019.17] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Research over the past years has indicated that chronic human exposure to very low doses of various chemical species in mixtures and administered via different routes (percutaneous, orally, etc.) should be the main focus of new biochemical and toxicological studies. Humans have daily contact with various chemicals, such as food additives, pesticides from fruits/vegetables, antibiotics (and other veterinary drugs) from meat, different types of preservatives from cosmetics, to name a few. Simultaneous exposure to this wide array of chemicals does not produce immediate effects, but summative effect/s over time that may be clinically manifested several years thereafter. Classical animal studies designed to test the toxic outcome of a single chemical are not suitable to assess, and then extrapolate to humans, the effects of a whole mixture of chemicals. Testing the aftermath of a combination of chemicals, at low doses, around or below the no observed adverse effect is stressed by many toxicologists. Thus, there is a need to reformulate the design of biochemical and toxicological studies in order to perform real-life risk simulation. This review discuss the potential use of computational methods as a complementary tool for in vitro and in vivo toxicity tests with a high predictive potential that could contribute to reduce animal testing, cost and time, when assessing the effects of chemical combinations. This review focused on the use of these methods to predict the potential endocrine disrupting activity of a mixture of chemicals.
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Affiliation(s)
- Denisa Margina
- 'Carol Davila' University of Medicine and Pharmacy, 020956 Bucharest, Romania
| | | | - Anca Ungurianu
- 'Carol Davila' University of Medicine and Pharmacy, 020956 Bucharest, Romania
| | - Robin Mesnage
- Gene Expression and Therapy Group, Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London SE1 9RT, United Kingdom
| | - Marina Goumenou
- Department of Forensic Sciences and Toxicology, Faculty of Medicine, University of Crete, 71409 Heraklion
| | - Dimosthenis A Sarigiannis
- Department of Chemical Engineering, Environmental Engineering Laboratory, Aristotle University of Thessaloniki, 54124 Thessaloniki
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, 57001 Thessaloniki, Greece
- Environmental Health Engineering, Department of Science, Technology and Society, School for Advanced Study (IUSS), 27100 Pavia, Italy
| | - Michael Aschner
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10463, USA
| | - Demetrios A Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - Elisavet A Renieri
- Centre of Toxicology Science and Research, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - Antonio F Hernández
- Department of Legal Medicine and Toxicology, University of Granada School of Medicine, Granada, Spain
| | - Aristidis Tsatsakis
- Department of Forensic Sciences and Toxicology, Faculty of Medicine, University of Crete, 71409 Heraklion
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142
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Cotterill JV, Palazzolo L, Ridgway C, Price N, Rorije E, Moretto A, Peijnenburg A, Eberini I. Predicting estrogen receptor binding of chemicals using a suite of in silico methods - Complementary approaches of (Q)SAR, molecular docking and molecular dynamics. Toxicol Appl Pharmacol 2019; 378:114630. [PMID: 31220507 DOI: 10.1016/j.taap.2019.114630] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/17/2019] [Accepted: 06/17/2019] [Indexed: 11/18/2022]
Abstract
With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERα receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERα receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest.
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Affiliation(s)
- J V Cotterill
- Fera Science Limited, Sand Hutton, York YO41 1LZ, UK
| | - L Palazzolo
- Università degli Studi di Milano, Dipartimento di Scienze Farmacologiche e Biomolecolari, Via Balzaretti 9, 20133 Milano, Italy
| | - C Ridgway
- Fera Science Limited, Sand Hutton, York YO41 1LZ, UK
| | - N Price
- Fera Science Limited, Sand Hutton, York YO41 1LZ, UK
| | - E Rorije
- Centre for Safety of Substances and Products, National Institute for Public Health and Environment (RIVM), P.O. Box 1, 3720 BA, Bilthoven, The Netherlands
| | - A Moretto
- Università degli Studi di Milano, Dipartimento di Scienze Biomediche e Cliniche, Ospedale L. Sacco, Padiglione 17, Via G.B. Grassi 74, 20157 Milano, Italy
| | - A Peijnenburg
- Wageningen University & Research, Wageningen, The Netherlands
| | - I Eberini
- Università degli Studi di Milano, Dipartimento di Scienze Farmacologiche e Biomolecolari & DSRC, Via Balzaretti 9, 20133 Milano, Italy.
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143
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Zheng Z, Peters GM, Arp HPH, Andersson PL. Combining in Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: A Case Study of Decabromodiphenyl Ether Alternatives. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:6341-6351. [PMID: 31081616 DOI: 10.1021/acs.est.8b07163] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Alternatives assessment is applied for minimizing the risk of unintentionally replacing a hazardous chemical with another hazardous chemical. Central challenges are the diversity of properties to consider and the lack of high-quality experimental data. To address this, a novel alternatives assessment procedure was developed based on in silico data and multicriteria decision analysis (MCDA) methods. As a case study, 16 alternatives to the flame retardant decabromodiphenyl ether were considered. The hazard properties included persistence (P), bioaccumulation potential (B), toxicities (T), and mobility in water (M). Databases were consulted and 2866 experimental data points were collected for the target chemicals; however, these were mostly replicate data points for some hazard criteria for a subset of alternatives. Therefore, in silico data and three MCDA strategies were tested including heat mapping, multiattribute utility theory (MAUT), and Elimination Et Choix Traduisant la REalité (ELECTRE III). The heat map clearly showed that none of the target chemicals are hazard-free, whereas MAUT and ELECTRE III agreed on ranking the "least worst" choices. This study identified several challenges and the complexity in the alternatives assessment processes motivating more case studies combining in silico and MCDA approaches.
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Affiliation(s)
- Ziye Zheng
- Department of Chemistry , Umeå University , SE-901 87 Umeå , Sweden
| | - Gregory M Peters
- Division of Environmental Systems Analysis , Chalmers University of Technology , SE-412 96 Göteborg , Sweden
- School of Civil and Environmental Engineering , University of New South Wales , AU-2052 Sydney , Australia
| | - Hans Peter H Arp
- Department of Environmental Engineering , Norwegian Geotechnical Institute , Ullevaal Stadion , NO-0806 Oslo , Norway
- Department of Chemistry , Norwegian University of Science and Technology (NTNU) , NO-7491 Trondheim , Norway
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Thomas RS, Bahadori T, Buckley TJ, Cowden J, Deisenroth C, Dionisio KL, Frithsen JB, Grulke CM, Gwinn MR, Harrill JA, Higuchi M, Houck KA, Hughes MF, Hunter ES, Isaacs KK, Judson RS, Knudsen TB, Lambert JC, Linnenbrink M, Martin TM, Newton SR, Padilla S, Patlewicz G, Paul-Friedman K, Phillips KA, Richard AM, Sams R, Shafer TJ, Setzer RW, Shah I, Simmons JE, Simmons SO, Singh A, Sobus JR, Strynar M, Swank A, Tornero-Valez R, Ulrich EM, Villeneuve DL, Wambaugh JF, Wetmore BA, Williams AJ. The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. Toxicol Sci 2019; 169:317-332. [PMID: 30835285 PMCID: PMC6542711 DOI: 10.1093/toxsci/kfz058] [Citation(s) in RCA: 217] [Impact Index Per Article: 43.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.
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Affiliation(s)
- Russell S. Thomas
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Tina Bahadori
- National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency
| | - Timothy J. Buckley
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - John Cowden
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Chad Deisenroth
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Kathie L. Dionisio
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Jeffrey B. Frithsen
- Chemical Safety for Sustainability National Research Program, Office of Research and Development, US Environmental Protection Agency
| | - Christopher M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Maureen R. Gwinn
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Joshua A. Harrill
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Mark Higuchi
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Keith A. Houck
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Michael F. Hughes
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - E. Sidney Hunter
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Thomas B. Knudsen
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Jason C. Lambert
- National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency
| | - Monica Linnenbrink
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Todd M. Martin
- National Risk Management Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Seth R. Newton
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Stephanie Padilla
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Grace Patlewicz
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Katie Paul-Friedman
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Katherine A. Phillips
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Reeder Sams
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Timothy J. Shafer
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - R. Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Jane E. Simmons
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Steven O. Simmons
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Amar Singh
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Jon R. Sobus
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Mark Strynar
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Adam Swank
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Rogelio Tornero-Valez
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Elin M. Ulrich
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Daniel L Villeneuve
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Barbara A. Wetmore
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
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145
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Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation. CURRENT OPINION IN TOXICOLOGY 2019. [DOI: 10.1016/j.cotox.2019.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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146
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Webster F, Gagné M, Patlewicz G, Pradeep P, Trefiak N, Judson RS, Barton-Maclaren TS. Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study. Regul Toxicol Pharmacol 2019; 106:278-291. [PMID: 31121201 DOI: 10.1016/j.yrtph.2019.05.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/07/2019] [Accepted: 05/16/2019] [Indexed: 10/26/2022]
Abstract
Traditional approaches for chemical risk assessment cannot keep pace with the number of substances requiring assessment. Thus, in a global effort to expedite and modernize chemical risk assessment, New Approach Methodologies (NAMs) are being explored and developed. Included in this effort is the OECD Integrated Approaches for Testing and Assessment (IATA) program, which provides a forum for OECD member countries to develop and present case studies illustrating the application of NAM in various risk assessment contexts. Here, we present an IATA case study for the prediction of estrogenic potential of three target phenols: 4-tert-butylphenol, 2,4-di-tert-butylphenol and octabenzone. Key features of this IATA include the use of two computational approaches for analogue selection for read-across, data collected from traditional and NAM sources, and a workflow to generate predictions regarding the targets' ability to bind the estrogen receptor (ER). Endocrine disruption can occur when a chemical substance mimics the activity of natural estrogen by binding to the ER and, if potency and exposure are sufficient, alters the function of the endocrine system to cause adverse effects. The data indicated that of the three target substances that were considered herein, 4-tert-butylphenol is a potential endocrine disruptor. Further, this IATA illustrates that the NAM approach explored is health protective when compared to in vivo endpoints traditionally used for human health risk assessment.
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147
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Vighi M, Barsi A, Focks A, Grisoni F. Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability-Remarks and research needs. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2019; 15:345-351. [PMID: 30821044 DOI: 10.1002/ieam.4136] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 02/18/2019] [Indexed: 06/09/2023]
Abstract
This paper concludes a special series of 7 articles (4 on toxicokinetic-toxicodynamic [TK-TD] models and 3 on quantitative structure-activity relationship [QSAR] models) published in previous issues of Integrated Environmental Assessment and Management (IEAM). The present paper summarizes the special series articles and highlights their contribution to the topic of increasing the regulatory applicability of effect models. For both TK-TD and QSAR approaches, we then describe the main research needs. The use of TK-TD models for describing sublethal effects must be better developed, particularly through the improvement of the dynamic energy budget (DEBtox) approach. The potential of TK-TD models for moving from lower (molecular) to higher (population) hierarchical levels is highlighted as a promising research line. Some relevant issues to improve the acceptance of QSAR models at the regulatory level are also described, such as increased transparency of the performance assessment and of the modeling algorithms, model documentation, relevance of the chosen target for regulatory needs, and improved mechanistic interpretability. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.
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Affiliation(s)
- Marco Vighi
- IMDEA Water Institute, Alcalà de Henares (Madrid), Spain
| | - Alpar Barsi
- Dutch Board for the Authorisation of Plant Protection Products and Biocides (Ctgb), Ede, Netherlands
| | - Andreas Focks
- Wageningen University & Research, Wageningen, Netherlands
| | - Francesca Grisoni
- University of Milano-Bicocca, Department of Earth and Environmental Sciences, Milano, Italy
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148
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Gonzalez TL, Rae JM, Colacino JA, Richardson RJ. Homology models of mouse and rat estrogen receptor- α ligand-binding domain created by in silico mutagenesis of a human template: molecular docking with 17ß-estradiol, diethylstilbestrol, and paraben analogs. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 10:1-16. [PMID: 30740556 PMCID: PMC6363358 DOI: 10.1016/j.comtox.2018.11.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Crystal structures exist for human, but not rodent, estrogen receptor-α ligand-binding domain (ERα-LBD). Consequently, rodent studies involving binding of compounds to ERα-LBD are limited in their molecular-level interpretation and extrapolation to humans. Because the sequences of rodent and human ERα-LBDs are > 95% identical, we expected their 3D structures and ligand binding to be highly similar. To test this hypothesis, we used the human ERα-LBD structure (PDB 3UUD) as a template to produce rat and mouse homology models. Employing the rodent models and human structure, we generated docking poses of 23 Group A ligands (17ß-estradiol, diethylstilbestrol, and 21 paraben analogs) in AutoDock Vina for interspecies comparisons. Ligand RMSDs (Å) (median, 95% CI) were 0.49 (0.21-1.82) (human-mouse) and 1.19 (0.22-1.82) (human-rat), well below the 2.0-2.5 Å range for equivalent docking poses. Numbers of interspecies ligand-receptor residue contacts were highly similar, with Sorensen Sc (%) = 96.8 (90.0-100) (human-mouse) and 97.7 (89.5-100) (human-rat). Likewise, numbers of interspecies ligand-receptor residue contacts were highly correlated: Pearson r = 0.913 (human-mouse) and 0.925 (human-rat). Numbers of interspecies ligand-receptor atom contacts were even more tightly correlated: r = 0.979 (human-mouse) and 0.986 (human-rat). Pyramid plots of numbers of ligand-receptor atom contacts by residue exhibited high interspecies symmetry and had Spearman r s = 0.977 (human-mouse) and 0.966 (human-rat). Group B ligands included 15 ring-substituted parabens recently shown experimentally to exhibit decreased binding to human ERα and to exert increased antimicrobial activity. Ligand efficiencies calculated from docking ligands into human ERα-LBD were well correlated with those derived from published experimental data (Pearson partial r p = 0.894 and 0.918; Groups A and B, respectively). Overall, the results indicate that our constructed rodent ERα-LBDs interact with ligands in like manner to the human receptor, thus providing a high level of confidence in extrapolations of rodent to human ligand-receptor interactions.
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Affiliation(s)
- Thomas L. Gonzalez
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - James M. Rae
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Justin A. Colacino
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Rudy J. Richardson
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
- Department of Neurology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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149
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Manganelli S, Roncaglioni A, Mansouri K, Judson RS, Benfenati E, Manganaro A, Ruiz P. Development, validation and integration of in silico models to identify androgen active chemicals. CHEMOSPHERE 2019; 220:204-215. [PMID: 30584954 PMCID: PMC6778835 DOI: 10.1016/j.chemosphere.2018.12.131] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 12/11/2018] [Accepted: 12/18/2018] [Indexed: 05/21/2023]
Abstract
Humans are exposed to large numbers of environmental chemicals, some of which potentially interfere with the endocrine system. The identification of potential endocrine disrupting chemicals (EDCs) has gained increasing priority in the assessment of environmental hazards. The U.S. Environmental Protection Agency (U.S. EPA) has developed the Endocrine Disruptor Screening Program (EDSP) which aims to prioritize and screen potential EDCs. The Toxicity Forecaster (ToxCast) program has generated data using in vitro high-throughput screening (HTS) assays measuring activity of chemicals at multiple points along the androgen receptor (AR) activity pathway. In the present study, using a large and diverse data set of 1667 chemicals provided by the U.S. EPA from the combined ToxCast AR assays in the framework of the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA). Two models were built using ADMET Predictor™; one is based on Artificial Neural Networks (ANNs) technology and the other uses a Support Vector Machine (SVM) algorithm; one model is a Decision Tree (DT) developed in R; and two models make use of differently combined sets of structural alerts (SAs) automatically extracted by SARpy. We used two strategies to integrate predictions from single models; one is based on a majority vote approach and the other on prediction convergence. These strategies led to enhanced statistical performance in most cases. Moreover, the majority vote approach improved prediction coverage when one or more single models were not able to provide any estimations. This study integrates multiple in silico approaches as a virtual screening tool for use in risk assessment of endocrine disrupting chemicals.
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Affiliation(s)
- Serena Manganelli
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G. La Masa 19, 20156, Milan, Italy
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G. La Masa 19, 20156, Milan, Italy
| | - Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, 1299 Bethel Valley Road, Oak Ridge, TN 37830, USA; Integrated Laboratory Systems, Inc., 601 Keystone Dr, Morrisville, NC 27650, USA
| | - Richard S Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G. La Masa 19, 20156, Milan, Italy
| | - Alberto Manganaro
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G. La Masa 19, 20156, Milan, Italy
| | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, GA, Georgia.
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Sun L, Yang H, Cai Y, Li W, Liu G, Tang Y. In Silico Prediction of Endocrine Disrupting Chemicals Using Single-Label and Multilabel Models. J Chem Inf Model 2019; 59:973-982. [PMID: 30807141 DOI: 10.1021/acs.jcim.8b00551] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Endocrine disruption (ED) has become a serious public health issue and also poses a significant threat to the ecosystem. Due to complex mechanisms of ED, traditional in silico models focusing on only one mechanism are insufficient for detection of endocrine disrupting chemicals (EDCs), let alone offering an overview of possible action mechanisms for a known EDC. To remove these limitations, in this study both single-label and multilabel models were constructed across six ED targets, namely, AR (androgen receptor), ER (estrogen receptor alpha), TR (thyroid receptor), GR (glucocorticoid receptor), PPARg (peroxisome proliferator-activated receptor gamma), and aromatase. Two machine learning methods were used to build the single-label models, with multiple random under-sampling combining voting classification to overcome the challenge of data imbalance. Four methods were explored to construct the multilabel models that can predict the interaction of one EDC against multiple targets simultaneously. The single-label models of all the six targets have achieved reasonable performance with balanced accuracy (BA) values from 0.742 to 0.816. Each top single-label model was then joined to predict the multilabel test set with BA values from 0.586 to 0.711. The multilabel models could offer a significant boost over the single-label baselines with BA values for the multilabel test set from 0.659 to 0.832. Therefore, we concluded that single-label models could be employed for identification of potential EDCs, while multilabel ones are preferable for prediction of possible mechanisms of known EDCs.
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Affiliation(s)
- Lixia Sun
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Yingchun Cai
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
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