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Kleinstreuer NC, Karmaus A, Mansouri K, Allen DG, Fitzpatrick JM, Patlewicz G. Predictive Models for Acute Oral Systemic Toxicity: A Workshop to Bridge the Gap from Research to Regulation. ACTA ACUST UNITED AC 2018; 8:21-24. [PMID: 30320239 DOI: 10.1016/j.comtox.2018.08.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In early 2018, the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) published the "Strategic Roadmap for Establishing New Approaches to Evaluate the Safety of Chemicals and Medical Products in the United States" (ICCVAM 2018). Cross-agency federal workgroups have been established to implement this roadmap for various toxicological testing endpoints, with an initial focus on acute toxicity testing. The ICCVAM acute toxicity workgroup (ATWG) helped organize a global collaboration to build predictive in silico models for acute oral systemic toxicity, based on a large dataset of rodent studies and targeted towards regulatory needs identified across federal agencies. Thirty-two international groups across government, industry, and academia participated in the project, culminating in a workshop in April 2018 held at the National Institutes of Health (NIH). At the workshop, computational modelers and regulatory decision makers met to discuss the feasibility of using predictive model outputs for regulatory use in lieu of acute oral systemic toxicity testing. The models were combined to yield consensus predictions which demonstrated excellent performance when compared to the animal data, and workshop outcomes and follow-up activities to make these tools available and put them into practice are discussed here.
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Affiliation(s)
- Nicole C Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Agnes Karmaus
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina 27560, United States
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina 27560, United States
| | - David G Allen
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina 27560, United States
| | - Jeremy M Fitzpatrick
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| | - Grace Patlewicz
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
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152
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Bai X, Yan L, Ji C, Zhang Q, Dong X, Chen A, Zhao M. A combination of ternary classification models and reporter gene assays for the comprehensive thyroid hormone disruption profiles of 209 polychlorinated biphenyls. CHEMOSPHERE 2018; 210:312-319. [PMID: 30005353 DOI: 10.1016/j.chemosphere.2018.07.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 06/24/2018] [Accepted: 07/05/2018] [Indexed: 06/08/2023]
Abstract
Computational toxicology is widely applied to screen tens and thousands of potential environmental endocrine disruptors (EDCs) for its great efficiency and rapid evaluation in recent years. Polychlorinated biphenyls (PCBs) with 209 congeners have not been comprehensively tested for their ability to interact with the thyroid receptor (TR), which is one of the most extensively studied targets related to the effects of EDCs. In this study, we aimed to determine the thyroid-disrupting mechanisms of 209 PCBs through the combination of a novel computational ternary classification model and luciferase reporter gene assay. The reporter gene assay was performed to examine the hormone activities of 22 PCBs via TR to classify their profiles as agonistic, antagonistic or inactive. Thus, six agonists, eleven antagonists and seven inactive chemicals against TR were identified in in vitro assays. Then, six relevant variables, including molecular structural descriptors and molecular docking scores, were selected for describing compounds. Machine learning methods (i.e., linear discriminant analysis (LDA) and support vector machines (SVM)) were used to build classification models. The optimal model was obtained by using SVM, with an accuracy of 88.24% in the training set, 80.0% in the test set and 75.0% in 10-fold cross-validation. The remaining 187 PCB congeners' hormone activities were predicted using the obtained models. Out of these PCBs, the SVM model predicted 38 agonists and 81 antagonists. The findings revealed that higher chlorinated PCBs tended to have TR-antagonistic activities, whereas lower chlorinated PCBs were agonists. This study provided a reference for further exploring PCBs' thyroid effect.
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Affiliation(s)
- Xiaoxia Bai
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310006, China
| | - Lu Yan
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China
| | - Chenyang Ji
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China
| | - Quan Zhang
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| | - An Chen
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, 310018, China
| | - Meirong Zhao
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China.
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153
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Grisoni F, Consonni V, Vighi M. Detecting the bioaccumulation patterns of chemicals through data-driven approaches. CHEMOSPHERE 2018; 208:273-284. [PMID: 29879561 DOI: 10.1016/j.chemosphere.2018.05.157] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 05/23/2018] [Accepted: 05/25/2018] [Indexed: 06/08/2023]
Abstract
This work investigates the bioaccumulation patterns of 168 organic chemicals in fish, by comparing their bioconcentration factor (BCF), biomagnification factor (BMF) and octanol-water partitioning coefficient (KOW). It aims to gain insights on the relationships between dietary and non-dietary bioaccumulation in aquatic environment, on the effectiveness of KOW and BCF to detect compounds that bioaccumulate through diet, as well as to detect the presence of structure-related bioaccumulation patterns. A linear relationship between logBMF and logKOW was observed (logBMF = 1.14·logBCF - 6.20) up to logKOW ≈ 4, as well as between logBMF and logBCF (logBMF = 0.96·logBCF - 4.06) up to a logBCF ≈ 5. 10% of compounds do not satisfy the linear BCF-BMF relationship. The deviations from such linear relationships were further investigated with the aid of a self-organizing map and canonical correlation analysis, which allowed us to shed light on some structure-related patterns. Finally, the usage of KOW- and BCF-based thresholds to detect compounds that accumulate through diet led to many false positives (47%-91% for KOW), and a moderate number of false negatives (up to 5% for BCF). These results corroborate the need of using the experimental BMF for hazard assessment practices, as well as of developing computational tools for BMF prediction.
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Affiliation(s)
- Francesca Grisoni
- University of Milano-Bicocca, Dept. of Earth and Environmental Sciences, Milano, Italy.
| | - Viviana Consonni
- University of Milano-Bicocca, Dept. of Earth and Environmental Sciences, Milano, Italy
| | - Marco Vighi
- IMDEA Water Institute, Alcalà de Henares, Madrid, Spain
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154
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Russo DP, Zorn KM, Clark AM, Zhu H, Ekins S. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction. Mol Pharm 2018; 15:4361-4370. [PMID: 30114914 PMCID: PMC6181119 DOI: 10.1021/acs.molpharmaceut.8b00546] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational methods, e.g., quantitative structure-activity relationship models, have become more reliable due to bigger training sets, increased computing power, and advanced machine learning algorithms, such as multilayered artificial neural networks. Machine learning models can be used to predict compounds for endocrine disrupting capabilities, such as binding to the estrogen receptor (ER), and allow for prioritization and further testing. In this work, an exhaustive comparison of multiple machine learning algorithms, chemical spaces, and evaluation metrics for ER binding was performed on public data sets curated using in-house cheminformatics software (Assay Central). Chemical features utilized in modeling consisted of binary fingerprints (ECFP6, FCFP6, ToxPrint, or MACCS keys) and continuous molecular descriptors from RDKit. Each feature set was subjected to classic machine learning algorithms (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, Support Vector Machine) and Deep Neural Networks (DNN). Models were evaluated using a variety of metrics: recall, precision, F1-score, accuracy, area under the receiver operating characteristic curve, Cohen's Kappa, and Matthews correlation coefficient. For predicting compounds within the training set, DNN has an accuracy higher than that of other methods; however, in 5-fold cross validation and external test set predictions, DNN and most classic machine learning models perform similarly regardless of the data set or molecular descriptors used. We have also used the rank normalized scores as a performance-criteria for each machine learning method, and Random Forest performed best on the validation set when ranked by metric or by data sets. These results suggest classic machine learning algorithms may be sufficient to develop high quality predictive models of ER activity.
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Affiliation(s)
- Daniel P. Russo
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
- first author
| | - Kimberley M. Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
- first author
| | - Alex M. Clark
- Molecular Materials Informatics, Inc., Montreal, Quebec, Canada
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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155
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Balabin IA, Judson RS. Exploring non-linear distance metrics in the structure-activity space: QSAR models for human estrogen receptor. J Cheminform 2018; 10:47. [PMID: 30229396 PMCID: PMC6755572 DOI: 10.1186/s13321-018-0300-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 08/29/2018] [Indexed: 01/06/2023] Open
Abstract
Background Quantitative structure-activity relationship (QSAR) models are important tools used in discovering new drug candidates and identifying potentially harmful environmental chemicals. These models often face two fundamental challenges: limited amount of available biological activity data and noise or uncertainty in the activity data themselves. To address these challenges, we introduce and explore a QSAR model based on custom distance metrics in the structure-activity space. Methods The model is built on top of the k-nearest neighbor model, incorporating non-linearity not only in the chemical structure space, but also in the biological activity space. The model is tuned and evaluated using activity data for human estrogen receptor from the US EPA ToxCast and Tox21 databases. Results The model closely trails the CERAPP consensus model (built on top of 48 individual human estrogen receptor activity models) in agonist activity predictions and consistently outperforms the CERAPP consensus model in antagonist activity predictions. Discussion We suggest that incorporating non-linear distance metrics may significantly improve QSAR model performance when the available biological activity data are limited.![]() Electronic supplementary material The online version of this article (10.1186/s13321-018-0300-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ilya A Balabin
- Leidos, Inc., 109 TW Alexander Drive, MD N127-01, Research Triangle Park, NC, 27711, USA.
| | - Richard S Judson
- US EPA, 109 TW Alexander Drive, ORD, NCCT, Research Triangle Park, NC, 27711, USA
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156
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Nicolas CI, Mansouri K, Phillips KA, Grulke CM, Richard AM, Williams AJ, Rabinowitz J, Isaacs KK, Yau A, Wambaugh JF. Rapid experimental measurements of physicochemical properties to inform models and testing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 636:901-909. [PMID: 29729507 PMCID: PMC6214190 DOI: 10.1016/j.scitotenv.2018.04.266] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 04/14/2023]
Abstract
The structures and physicochemical properties of chemicals are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize the risk for thousands of environmental chemicals, but experimental values are often lacking. In an attempt to efficiently fill data gaps in physicochemical property information, we generated new data for 200 structurally diverse compounds, which were rigorously selected from the USEPA ToxCast chemical library, and whose structures are available within the Distributed Structure-Searchable Toxicity Database (DSSTox). This pilot study evaluated rapid experimental methods to determine five physicochemical properties, including the log of the octanol:water partition coefficient (known as log(Kow) or logP), vapor pressure, water solubility, Henry's law constant, and the acid dissociation constant (pKa). For most compounds, experiments were successful for at least one property; log(Kow) yielded the largest return (176 values). It was determined that 77 ToxPrint structural features were enriched in chemicals with at least one measurement failure, indicating which features may have played a role in rapid method failures. To gauge consistency with traditional measurement methods, the new measurements were compared with previous measurements (where available). Since quantitative structure-activity/property relationship (QSAR/QSPR) models are used to fill gaps in physicochemical property information, 5 suites of QSPRs were evaluated for their predictive ability and chemical coverage or applicability domain of new experimental measurements. The ability to have accurate measurements of these properties will facilitate better exposure predictions in two ways: 1) direct input of these experimental measurements into exposure models; and 2) construction of QSPRs with a wider applicability domain, as their predicted physicochemical values can be used to parameterize exposure models in the absence of experimental data.
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Affiliation(s)
- Chantel I Nicolas
- ScitoVation, LLC 6 Davis Drive, Durham, NC 27703, USA; National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Kamel Mansouri
- ScitoVation, LLC 6 Davis Drive, Durham, NC 27703, USA; National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Katherine A Phillips
- National Exposure Research Laboratory, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Christopher M Grulke
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - James Rabinowitz
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Kristin K Isaacs
- National Exposure Research Laboratory, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Alice Yau
- Southwest Research Institute, San Antonio, TX 78238, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA.
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157
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Focks A, Grisoni F, Barsi A, Vighi M. Predictive Models in Ecotoxicology: Bridging the Gap Between Scientific Progress and Regulatory Applicability. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2018; 14:601-603. [PMID: 29457682 DOI: 10.1002/ieam.4039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 02/05/2018] [Accepted: 02/14/2018] [Indexed: 06/08/2023]
Abstract
This special series is the outcome of the session "Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability," held at the 27th SETAC Europe annual meeting (Brussels, May 2017). In this foreword the rationale behind the special series, the reasons for proposing it, and its objectives are described briefly. Integr Environ Assess Manag 2018;14:601-603. © 2018 SETAC.
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Affiliation(s)
- Andreas Focks
- Wageningen University & Research, Wageningen, The Netherlands
| | - Francesca Grisoni
- University of Milano-Bicocca, Department of Earth and Environmental Sciences, Milano, Italy
| | - Alpar Barsi
- Dutch Board for the Authorisation of Plant Protection Products and Biocides (Ctgb), Ede, The Netherlands
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158
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Sobus JR, Wambaugh JF, Isaacs KK, Williams AJ, McEachran AD, Richard AM, Grulke CM, Ulrich EM, Rager JE, Strynar MJ, Newton SR. Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2018; 28:411-426. [PMID: 29288256 PMCID: PMC6661898 DOI: 10.1038/s41370-017-0012-y] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 08/04/2017] [Accepted: 08/25/2017] [Indexed: 05/18/2023]
Abstract
Tens-of-thousands of chemicals are registered in the U.S. for use in countless processes and products. Recent evidence suggests that many of these chemicals are measureable in environmental and/or biological systems, indicating the potential for widespread exposures. Traditional public health research tools, including in vivo studies and targeted analytical chemistry methods, have been unable to meet the needs of screening programs designed to evaluate chemical safety. As such, new tools have been developed to enable rapid assessment of potentially harmful chemical exposures and their attendant biological responses. One group of tools, known as "non-targeted analysis" (NTA) methods, allows the rapid characterization of thousands of never-before-studied compounds in a wide variety of environmental, residential, and biological media. This article discusses current applications of NTA methods, challenges to their effective use in chemical screening studies, and ways in which shared resources (e.g., chemical standards, databases, model predictions, and media measurements) can advance their use in risk-based chemical prioritization. A brief review is provided of resources and projects within EPA's Office of Research and Development (ORD) that provide benefit to, and receive benefits from, NTA research endeavors. A summary of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) is also given, which makes direct use of ORD resources to benefit the global NTA research community. Finally, a research framework is described that shows how NTA methods will bridge chemical prioritization efforts within ORD. This framework exists as a guide for institutions seeking to understand the complexity of chemical exposures, and the impact of these exposures on living systems.
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Affiliation(s)
- Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Andrew D McEachran
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Christopher M Grulke
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Julia E Rager
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
- ToxStrategies, Inc., 9390 Research Blvd., Suite 100, Austin, TX, 78759, USA
| | - Mark J Strynar
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Seth R Newton
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
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159
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McEachran AD, Mansouri K, Grulke C, Schymanski EL, Ruttkies C, Williams AJ. "MS-Ready" structures for non-targeted high-resolution mass spectrometry screening studies. J Cheminform 2018; 10:45. [PMID: 30167882 PMCID: PMC6117229 DOI: 10.1186/s13321-018-0299-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 08/21/2018] [Indexed: 02/05/2023] Open
Abstract
Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workflow has been developed to produce structural representations observed using HRMS ("MS-Ready structures") and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA's Chemistry Dashboard ( https://comptox.epa.gov/dashboard/ ). This article describes the workflow for the generation and linking of ~ 700,000 MS-Ready structures (derived from ~ 760,000 original structures) as well as download, search and export capabilities to serve structure identification using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/ .
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Affiliation(s)
- Andrew D. McEachran
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
- Present Address: Integrated Laboratory Systems, Inc., 601 Keystone Dr., Morrisville, NC 27650 USA
| | - Chris Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, 4367 Belvaux, Luxembourg
| | - Christoph Ruttkies
- Department of Stress and Development Biology, Leibniz Institute of Plant Biochemistry (IPB), Weinberg 3, 06120 Halle (Saale), Germany
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop D143-02, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
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160
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Yan L, Zhang Q, Huang F, Nie WW, Hu CQ, Ying HZ, Dong XW, Zhao MR. Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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161
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Watford SM, Grashow RG, De La Rosa VY, Rudel RA, Friedman KP, Martin MT. Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2018; 7:46-57. [PMID: 32274464 PMCID: PMC7144681 DOI: 10.1016/j.comtox.2018.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.
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Affiliation(s)
- Sean M Watford
- ORAU, contractor to U.S. Environmental Protection Agency through the National Student Services Contract, Oak Ridge, TN
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, North Carolina, United States
| | - Rachel G Grashow
- Silent Spring Institute, Newton, MA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Vanessa Y De La Rosa
- Silent Spring Institute, Newton, MA
- Social Science Environmental Health Research Institute, Northeastern University, Boston, MA
| | | | | | - Matthew T Martin
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, NC, USA
- Currently at Pfizer Worldwide Research & Development, Groton, CT, USA
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162
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Gadaleta D, Manganelli S, Roncaglioni A, Toma C, Benfenati E, Mombelli E. QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis. J Chem Inf Model 2018; 58:1501-1517. [PMID: 29949360 DOI: 10.1021/acs.jcim.8b00297] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Nonalcoholic hepatic steatosis is a worldwide epidemiological concern since it is among the most prominent hepatic diseases. Indeed, research in toxicology and epidemiology has gathered evidence that exposure to endocrine disruptors can perturb cellular homeostasis and cause this disease. Therefore, assessing the likelihood of a chemical to trigger hepatic steatosis is a matter of the utmost importance. However, systematic in vivo testing of all the chemicals humans are exposed to is not feasible for ethical and economical reasons. In this context, predicting the molecular initiating events (MIE) leading to hepatic steatosis by QSAR modeling is an issue of practical relevance in modern toxicology. In this article, we present QSAR models based on random forest classifiers and DRAGON molecular descriptors for the prediction of in vitro assays that are relevant to MIEs leading to hepatic steatosis. These assays were provided by the ToxCast program and proved to be predictive for the detection of chemical-induced steatosis. During the modeling process, special attention was paid to chemical and toxicological data curation. We adopted two modeling strategies (undersampling and balanced random forests) to develop robust QSAR models from unbalanced data sets. The two modeling approaches gave similar results in terms of predictivity, and most of the models satisfy a minimum percentage of correctly predicted chemicals equal to 75%. Finally, and most importantly, the developed models proved to be useful as an effective in silico screening test for hepatic steatosis.
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Affiliation(s)
- Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences , IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Via la Masa 19 , 20156 Milano , Italy
| | - Serena Manganelli
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences , IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Via la Masa 19 , 20156 Milano , Italy
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences , IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Via la Masa 19 , 20156 Milano , Italy
| | - Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences , IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Via la Masa 19 , 20156 Milano , Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences , IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Via la Masa 19 , 20156 Milano , Italy
| | - Enrico Mombelli
- Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO) , Institut National de l'Environnement Industriel et des Risques (INERIS) , 60550 Verneuil en Halatte , France
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163
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Watt ED, Judson RS. Uncertainty quantification in ToxCast high throughput screening. PLoS One 2018; 13:e0196963. [PMID: 30044784 PMCID: PMC6059398 DOI: 10.1371/journal.pone.0196963] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 04/24/2018] [Indexed: 01/04/2023] Open
Abstract
High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.
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Affiliation(s)
- Eric D. Watt
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, North Carolina, United States of America
- Oak Ridge Institute for Science Education Postdoctoral Fellow, Oak Ridge, Tennessee, United States of America
| | - Richard S. Judson
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, North Carolina, United States of America
- * E-mail:
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164
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Alves VM, Golbraikh A, Capuzzi SJ, Liu K, Lam WI, Korn DR, Pozefsky D, Andrade CH, Muratov EN, Tropsha A. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models. J Chem Inf Model 2018; 58:1214-1223. [PMID: 29809005 DOI: 10.1021/acs.jcim.8b00124] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , GO 74605-170 , Brazil
| | - Alexander Golbraikh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Kammy Liu
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Wai In Lam
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Daniel Robert Korn
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Diane Pozefsky
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , GO 74605-170 , Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa , 65000 , Ukraine
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
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165
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Chiu WA, Axelrad DA, Dalaijamts C, Dockins C, Shao K, Shapiro AJ, Paoli G. Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects. ENVIRONMENTAL HEALTH PERSPECTIVES 2018; 126:067009. [PMID: 29968566 PMCID: PMC6084844 DOI: 10.1289/ehp3368] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 04/23/2018] [Accepted: 05/08/2018] [Indexed: 05/05/2023]
Abstract
BACKGROUND The National Academies recommended risk assessments redefine the traditional noncancer Reference Dose (RfD) as a probabilistically derived risk-specific dose, a framework for which was recently developed by the World Health Organization (WHO). OBJECTIVES Our aim was to assess the feasibility and implications of replacing traditional RfDs with probabilistic estimates of the human dose associated with an effect magnitude M and population incidence I (HDMI). METHODS We created a comprehensive, curated database of RfDs derived from animal data and developed a standardized, automated, web-accessible probabilistic dose-response workflow implementing the WHO framework. RESULTS We identified 1,464 RfDs and associated endpoints, representing 608 chemicals across many types of effects. Applying our standardized workflow resulted in 1,522 HDMI values. Traditional RfDs are generally within an order of magnitude of the HDMI lower confidence bound for I=1% and M values commonly used for benchmark doses. The greatest contributor to uncertainty was lack of benchmark dose estimates, followed by uncertainty in the extent of human variability. Exposure at the traditional RfD frequently implies an upper 95% confidence bound of several percent of the population affected. Whether such incidences are considered acceptable is likely to vary by chemical and risk context, especially given the wide range of severity of the associated effects, from clinical chemistry to mortality. CONCLUSIONS Overall, replacing RfDs with HDMI estimates can provide a more consistent, scientifically rigorous, and transparent basis for risk management decisions, as well as support additional decision contexts such as economic benefit-cost analysis, risk-risk tradeoffs, life-cycle impact analysis, and emergency response. https://doi.org/10.1289/EHP3368.
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Affiliation(s)
- Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Daniel A Axelrad
- Office of Policy (1809T), U.S. Environmental Protection Agency, Washington, District of Columbia, USA
| | - Chimeddulam Dalaijamts
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Chris Dockins
- Office of Policy (1809T), U.S. Environmental Protection Agency, Washington, District of Columbia, USA
| | - Kan Shao
- Department of Environmental and Occupational Health, Indiana University School of Public-Bloomington, Bloomington, Indiana, USA
| | - Andrew J Shapiro
- National Toxicology Program, National Institute for Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Greg Paoli
- Risk Sciences International, Ottawa, Ontario, Canada
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166
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Judson RS, Paul Friedman K, Houck K, Mansouri K, Browne P, Kleinstreuer NC. New approach methods for testing chemicals for endocrine disruption potential. CURRENT OPINION IN TOXICOLOGY 2018. [DOI: 10.1016/j.cotox.2018.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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167
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Wignall JA, Muratov E, Sedykh A, Guyton KZ, Tropsha A, Rusyn I, Chiu WA. Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals. ENVIRONMENTAL HEALTH PERSPECTIVES 2018; 126:057008. [PMID: 29847084 PMCID: PMC6071978 DOI: 10.1289/ehp2998] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 03/25/2018] [Accepted: 04/16/2018] [Indexed: 05/03/2023]
Abstract
BACKGROUND Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. OBJECTIVES As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. METHODS We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. RESULTS QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q2 of 0.25-0.45, mean model errors of 0.70-1.11 log10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. CONCLUSIONS An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.
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Affiliation(s)
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alexander Sedykh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kathryn Z Guyton
- Monographs Section, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
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168
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Leonard JA, Stevens C, Mansouri K, Chang D, Pudukodu H, Smith S, Tan YM. A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening. ACTA ACUST UNITED AC 2018; 6:71-83. [PMID: 30246166 DOI: 10.1016/j.comtox.2017.10.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.
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Affiliation(s)
- Jeremy A Leonard
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Caroline Stevens
- National Exposure Research Laboratory, United States Environmental Protection Agency, Athens, GA, USA
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.,National Center for Computational Toxicology, United States Environmental Protection Agency, Research Triangle Park, NC, USA.,ScitoVation LLC, Research Triangle Park, NC, USA
| | - Daniel Chang
- Office of Pollution and Prevention of Toxics, United States Environmental Protection Agency, Washington, D.C., USA
| | - Harish Pudukodu
- National Exposure Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sherrie Smith
- National Exposure Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Yu-Mei Tan
- National Exposure Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, NC, USA
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169
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Chushak YG, Shows HW, Gearhart JM, Pangburn HA. In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox. Toxicol Res (Camb) 2018; 7:423-431. [PMID: 30090592 PMCID: PMC6061186 DOI: 10.1039/c7tx00268h] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 01/10/2018] [Indexed: 12/12/2022] Open
Abstract
This study evaluates the application of QSAR Toolbox and ToxCast screening data to identify neurological targets for pyrethroids.
There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC50s for only active compounds, was used to predict AC50 values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC50 predictions demonstrated mixed accuracy compared with the ToxCast screening data.
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Affiliation(s)
- Y G Chushak
- Henry M Jackson Foundation for the Advancement of Military Medicine , Wright-Patterson AFB , Ohio 45433 , USA .
| | - H W Shows
- Biological Sciences Department , Wright State University , Dayton , Ohio 45435 , USA
| | - J M Gearhart
- Henry M Jackson Foundation for the Advancement of Military Medicine , Wright-Patterson AFB , Ohio 45433 , USA .
| | - H A Pangburn
- United States Air Force School of Aerospace Medicine , Aeromedical Research Department , Force Health Protection , Wright-Patterson AFB , Ohio 45433 , USA
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170
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Mansouri K, Grulke CM, Judson RS, Williams AJ. OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminform 2018. [PMID: 29520515 PMCID: PMC5843579 DOI: 10.1186/s13321-018-0263-1] [Citation(s) in RCA: 257] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The collection of chemical structure information and associated experimental data for quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.![]()
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Affiliation(s)
- 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. .,ScitoVation LLC, 6 Davis Drive, Research Triangle Park, NC, 27709, USA.
| | - Chris M Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, 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
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
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171
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Strope CL, Mansouri K, Clewell HJ, Rabinowitz JR, Stevens C, Wambaugh JF. High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 615:150-160. [PMID: 28964990 PMCID: PMC6055917 DOI: 10.1016/j.scitotenv.2017.09.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 08/30/2017] [Accepted: 09/04/2017] [Indexed: 05/16/2023]
Abstract
Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vdss). Here, estimates of ionization equilibrium constants (i.e., pKa) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vdss for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pKa. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vdss on predicted pKa using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vdss generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vdss and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.
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Affiliation(s)
- Cory L Strope
- Risk Assessment Division, Office of Pollution Prevention and Toxics, Office of Chemical Safety and Pollution Prevention, U.S. Environmental Protection Agency, Washington, DC, USA; ORISE Postdoctoral Research Fellow, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA; The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA.
| | - Kamel Mansouri
- ORISE Postdoctoral Research Fellow, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA; ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, USA
| | - Harvey J Clewell
- ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, USA
| | - James R Rabinowitz
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Caroline Stevens
- Ecosystems Research Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, GA, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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172
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Tachachartvanich P, Sangsuwan R, Ruiz HS, Sanchez SS, Durkin KA, Zhang L, Smith MT. Assessment of the Endocrine-Disrupting Effects of Trichloroethylene and Its Metabolites Using in Vitro and in Silico Approaches. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:1542-1550. [PMID: 29294279 PMCID: PMC6290898 DOI: 10.1021/acs.est.7b04832] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Trichloroethylene (TCE) is a ubiquitous environmental contaminant, which may have effects on both ecosystem and human health. TCE has been reported to cause several toxic effects, but little effort has been made to assess the ecological risks of TCE or its major metabolites: trichloroethanol (TCOH), trichloroacetic acid, and oxalic acid (OA). In this study, the endocrine-disrupting potential of TCE and its metabolites were investigated using in vitro and in silico approaches. We examined alterations in the steroidogenesis pathway using the NCI-H295R cell line and utilized receptor-mediated luciferase reporter cell lines to identify effects on estrogen and androgen receptors. Molecular docking was also used to explore chemical interactions with these receptors. All test chemicals except OA significantly increased 17β-estradiol production which can be attributed to an up-regulation of 17β-hydroxysteroid dehydrogenase. Moreover, TCOH exhibited significant antiestrogenic activity with a RIC20 (20% relative inhibitory concentration) of 3.7 × 10-7 M. Molecular docking simulation supported this finding with lower docking scores for TCOH, indicating that hydrogen bonds may stabilize the interaction between TCOH and the estrogen receptor binding pocket. These findings suggest that TCE contamination poses an endocrine-disrupting threat, which has implications for both ecological and human health.
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Affiliation(s)
- Phum Tachachartvanich
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
| | - Rapeepat Sangsuwan
- Department of Chemistry, University of California Berkeley, Berkeley, California 94720, United States
| | - Heather S. Ruiz
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
| | - Sylvia S. Sanchez
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
| | - Kathleen A. Durkin
- Department of Chemistry, University of California Berkeley, Berkeley, California 94720, United States
| | - Luoping Zhang
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
| | - Martyn T. Smith
- Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
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173
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Passeri GI, Trisciuzzi D, Alberga D, Siragusa L, Leonetti F, Mangiatordi GF, Nicolotti O. Strategies of Virtual Screening in Medicinal Chemistry. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.
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Affiliation(s)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Lydia Siragusa
- Molecular Discovery Ltd., Pinner, Middlesex, London, United Kingdom
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
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174
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Grisoni F, Ballabio D, Todeschini R, Consonni V. Molecular Descriptors for Structure-Activity Applications: A Hands-On Approach. Methods Mol Biol 2018; 1800:3-53. [PMID: 29934886 DOI: 10.1007/978-1-4939-7899-1_1] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.
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Affiliation(s)
- Francesca Grisoni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy.
| | - Davide Ballabio
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Roberto Todeschini
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Viviana Consonni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
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175
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Abstract
Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.
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176
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Filimonov D, Druzhilovskiy D, Lagunin A, Gloriozova T, Rudik A, Dmitriev A, Pogodin P, Poroikov V. Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation. ACTA ACUST UNITED AC 2018. [DOI: 10.18097/bmcrm00004] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.
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Affiliation(s)
| | | | - A.A. Lagunin
- Institute of Biomedical Chemistry; Pirogov Russian National Research Medical University, Moscow, Russia
| | | | - A.V. Rudik
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - P.V. Pogodin
- Institute of Biomedical Chemistry, Moscow, Russia
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177
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Grisoni F, Consonni V, Todeschini R. Impact of Molecular Descriptors on Computational Models. Methods Mol Biol 2018; 1825:171-209. [PMID: 30334206 DOI: 10.1007/978-1-4939-8639-2_5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Molecular descriptors encode a wide variety of molecular information and have become the support of many contemporary chemoinformatic and bioinformatic applications. They grasp specific molecular features (e.g., geometry, shape, pharmacophores, or atomic properties) and directly affect computational models, in terms of outcome, performance, and applicability. This chapter aims to illustrate the impact of different molecular descriptors on the structural information captured and on the perceived chemical similarity among molecules. After introducing the fundamental concepts of molecular descriptor theory and application, a step-by-step retrospective virtual screening procedure guides users through the fundamental processing steps and discusses the impact of different types of molecular descriptors.
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Affiliation(s)
- Francesca Grisoni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy.
| | - Viviana Consonni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Roberto Todeschini
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
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178
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Peisl BYL, Schymanski EL, Wilmes P. Dark matter in host-microbiome metabolomics: Tackling the unknowns-A review. Anal Chim Acta 2017; 1037:13-27. [PMID: 30292286 DOI: 10.1016/j.aca.2017.12.034] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/15/2017] [Accepted: 12/19/2017] [Indexed: 02/07/2023]
Abstract
The "dark matter" in metabolomics (unknowns) represents an exciting frontier with significant potential for discovery in relation to biochemistry, yet it also presents one of the largest challenges to overcome. This focussed review takes a close look at the current state-of-the-art and future challenges in tackling the unknowns with specific focus on the human gut microbiome and host-microbe interactions. Metabolomics, like metabolism itself, is a very dynamic discipline, with many workflows and methods under development, both in terms of chemical analysis and post-analysis data processing. Here, we look at developments in the mutli-omic analyses and the use of mass spectrometry to investigate the exchange of metabolites between the host and the microbiome as well as the environment within the microbiome. A case study using HuMiX, a microfluidics-based human-microbial co-culture system that enables the co-culture of human and microbial cells under controlled conditions, is used to highlight opportunities and current limitations. Common definitions, approaches, databases and elucidation techniques from both the environmental and metabolomics fields are covered, with perspectives on how to merge these, as the boundaries blur between the fields. While reflecting on the number of unknowns remaining to be conquered in typical complex samples measured with mass spectrometry (often orders of magnitude above the "knowns"), we provide an outlook on future perspectives and challenges in elucidating the relevant "dark matter".
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Affiliation(s)
- B Y Loulou Peisl
- Environmental Cheminformatics Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg; Eco-Systems Biology Group, LCSB, University of Luxembourg, 7, Avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
| | - Emma L Schymanski
- Environmental Cheminformatics Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
| | - Paul Wilmes
- Eco-Systems Biology Group, LCSB, University of Luxembourg, 7, Avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
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179
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Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs. Sci Rep 2017; 7:17311. [PMID: 29229971 PMCID: PMC5725422 DOI: 10.1038/s41598-017-17701-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 11/30/2017] [Indexed: 12/11/2022] Open
Abstract
Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.
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180
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Cronin MT, Richarz AN. Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity. ACTA ACUST UNITED AC 2017. [DOI: 10.1089/aivt.2017.0021] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England
| | - Andrea-Nicole Richarz
- European Commission, Joint Research Centre, Directorate for Health, Consumers and Reference Materials, Ispra, Italy
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181
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Rosenberg S, Watt E, Judson R, Simmons S, Paul Friedman K, Dybdahl M, Nikolov N, Wedebye E. QSAR models for thyroperoxidase inhibition and screening of U.S. and EU chemical inventories. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.comtox.2017.07.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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182
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Pradeep P, Mansouri K, Patlewicz G, Judson R. A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols. ACTA ACUST UNITED AC 2017; 4:22-30. [PMID: 30057968 DOI: 10.1016/j.comtox.2017.09.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions.
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Affiliation(s)
- Prachi Pradeep
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Grace Patlewicz
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Richard Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
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183
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Trisciuzzi D, Alberga D, Mansouri K, Judson R, Novellino E, Mangiatordi GF, Nicolotti O. Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals. J Chem Inf Model 2017; 57:2874-2884. [PMID: 29022712 DOI: 10.1021/acs.jcim.7b00420] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.
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Affiliation(s)
- Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy.,Centro Ricerche TIRES, Università degli Studi di Bari "Aldo Moro" , Via Amendola 173, I-70126 Bari, Italy
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education , Oak Ridge, Tennessee 37830, United States.,National Center for Computational Toxicology, U.S. Environmental Protection Agency , 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27711, United States.,ScitoVation LLC , 6 Davis Drive, Research Triangle Park, North Carolina 27709, United States
| | - Richard Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency , 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27711, United States
| | - Ettore Novellino
- Dipartimento di Farmacia, Università degli Studi di Napoli "Federico II" , Via D. Montesano 49, 80131 Napoli, Italy
| | - Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy.,Centro Ricerche TIRES, Università degli Studi di Bari "Aldo Moro" , Via Amendola 173, I-70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy.,Centro Ricerche TIRES, Università degli Studi di Bari "Aldo Moro" , Via Amendola 173, I-70126 Bari, Italy
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184
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Sakkiah S, Selvaraj C, Gong P, Zhang C, Tong W, Hong H. Development of estrogen receptor beta binding prediction model using large sets of chemicals. Oncotarget 2017; 8:92989-93000. [PMID: 29190972 PMCID: PMC5696238 DOI: 10.18632/oncotarget.21723] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 08/27/2017] [Indexed: 12/31/2022] Open
Abstract
We developed an ERβ binding prediction model to facilitate identification of chemicals specifically bind ERβ or ERα together with our previously developed ERα binding model. Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα.
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Affiliation(s)
- Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA
| | - Chaoyang Zhang
- School of Computer Science, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
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185
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Liu J, Patlewicz G, Williams AJ, Thomas RS, Shah I. Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure. Chem Res Toxicol 2017; 30:2046-2059. [PMID: 28768096 DOI: 10.1021/acs.chemrestox.7b00084] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches making use of high-throughput experimental data may provide more efficient means to predict chemical toxicity. Here, we use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performance was assessed based on F1 scores using 5-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%), and these gains were correlated (ρ = 0.92) with the number of chemicals. Overall, the results demonstrate that a combination of bioactivity and chemical descriptors can accurately predict a range of target organ toxicity outcomes in repeat-dose studies, but specific experimental and methodologic improvements may increase predictivity.
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Affiliation(s)
- Jie Liu
- Department of Information Science, University of Arkansas at Little Rock , Arkansas 72204, United States.,Oak Ridge Institute for Science Education, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, Durham, North Carolina 27711, United States
| | - Grace Patlewicz
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, Durham, North Carolina 27711, United States
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, Durham, North Carolina 27711, United States
| | - Russell S Thomas
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, Durham, North Carolina 27711, United States
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, Durham, North Carolina 27711, United States
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186
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Mesnage R, Phedonos A, Biserni M, Arno M, Balu S, Corton JC, Ugarte R, Antoniou MN. Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents. Food Chem Toxicol 2017; 108:30-42. [PMID: 28711546 DOI: 10.1016/j.fct.2017.07.025] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 06/20/2017] [Accepted: 07/11/2017] [Indexed: 01/05/2023]
Abstract
The safety, including the endocrine disruptive capability, of glyphosate-based herbicides (GBHs) is a matter of intense debate. We evaluated the estrogenic potential of glyphosate, commercial GBHs and polyethoxylated tallowamine adjuvants present as co-formulants in GBHs. Glyphosate (≥10,000 μg/L or 59 μM) promoted proliferation of estrogen-dependent MCF-7 human breast cancer cells. Glyphosate also increased the expression of an estrogen response element-luciferase reporter gene (ERE-luc) in T47D-KBluc cells, which was blocked by the estrogen antagonist ICI 182,780. Commercial GBH formulations or their adjuvants alone did not exhibit estrogenic effects in either assay. Transcriptomics analysis of MCF-7 cells treated with glyphosate revealed changes in gene expression reflective of hormone-induced cell proliferation but did not overlap with an ERα gene expression biomarker. Calculation of glyphosate binding energy to ERα predicts a weak and unstable interaction (-4.10 kcal mol-1) compared to estradiol (-25.79 kcal mol-1), which suggests that activation of this receptor by glyphosate is via a ligand-independent mechanism. Induction of ERE-luc expression by the PKA signalling activator IBMX shows that ERE-luc is responsive to ligand-independent activation, suggesting a possible mechanism of glyphosate-mediated activation. Our study reveals that glyphosate, but not other components present in GBHs, can activate ERα in vitro, albeit at relatively high concentrations.
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Affiliation(s)
- Robin Mesnage
- Gene Expression and Therapy Group, King's College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular Genetics, 8th Floor, Tower Wing, Guy's Hospital, Great Maze Pond, London SE1 9RT, United Kingdom
| | - Alexia Phedonos
- Gene Expression and Therapy Group, King's College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular Genetics, 8th Floor, Tower Wing, Guy's Hospital, Great Maze Pond, London SE1 9RT, United Kingdom
| | - Martina Biserni
- Gene Expression and Therapy Group, King's College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular Genetics, 8th Floor, Tower Wing, Guy's Hospital, Great Maze Pond, London SE1 9RT, United Kingdom
| | - Matthew Arno
- Genomics Centre, King's College London, Waterloo Campus, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Sucharitha Balu
- Genomics Centre, King's College London, Waterloo Campus, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - J Christopher Corton
- Integrated Systems Toxicology Division, US Environmental Protection Agency, 109 T.W. Alexander Dr MD-B143-06, Research Triangle Park, NC 27711, United States
| | - Ricardo Ugarte
- Instituto de Ciencias Químicas, Facultad de Ciencias, Universidad Austral de Chile, Independencia 641, Valdivia, Chile
| | - Michael N Antoniou
- Gene Expression and Therapy Group, King's College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular Genetics, 8th Floor, Tower Wing, Guy's Hospital, Great Maze Pond, London SE1 9RT, United Kingdom.
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187
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Vračko M, Drgan V. Grouping of CoMPARA data with respect to compounds from the carcinogenic potency database. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:801-813. [PMID: 29156996 DOI: 10.1080/1062936x.2017.1398184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 10/25/2017] [Indexed: 06/07/2023]
Abstract
Methods for clustering and measures of similarity of chemical structures have become an important supporting tool in chemoinformatics. They represent the basis for categorization of chemicals and read-across, where a molecular property is estimated from 'similar molecules'. This study proposes a clustering scheme within the given dataset with respect to a reference dataset. The scheme was applied on two datasets ToxCast_AR_Agonist and ToxCast_AR_Antagonists with 1654 and 1522 compounds, respectively. The compounds are tested to androgen receptor activity (AR) in 11 high throughput screening assays. The carcinogenic dataset was used as the reference set.
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Affiliation(s)
- M Vračko
- a National Institute of Chemistry , Kemijski Inštitut , Ljubljana , Slovenia
| | - V Drgan
- a National Institute of Chemistry , Kemijski Inštitut , Ljubljana , Slovenia
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188
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Selvaraj C, Sakkiah S, Tong W, Hong H. Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. Food Chem Toxicol 2017; 112:495-506. [PMID: 28843597 DOI: 10.1016/j.fct.2017.08.028] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 08/08/2017] [Accepted: 08/22/2017] [Indexed: 12/13/2022]
Abstract
Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.
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Affiliation(s)
- Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
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189
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Cavalluzzi MM, Mangiatordi GF, Nicolotti O, Lentini G. Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective. Expert Opin Drug Discov 2017; 12:1087-1104. [PMID: 28814111 DOI: 10.1080/17460441.2017.1365056] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.
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Affiliation(s)
| | | | - Orazio Nicolotti
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
| | - Giovanni Lentini
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
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190
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Grace P, George H, Prachi P, Imran S. Navigating through the minefield of read-across tools: A review of in silico tools for grouping. ACTA ACUST UNITED AC 2017; 3:1-18. [PMID: 30221211 DOI: 10.1016/j.comtox.2017.05.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Read-across is a popular data gap filling technique used within analogue and category approaches for regulatory purposes. In recent years there have been many efforts focused on the challenges involved in read-across development, its scientific justification and documentation. Tools have also been developed to facilitate read-across development and application. Here, we describe a number of publicly available read-across tools in the context of the category/analogue workflow and review their respective capabilities, strengths and weaknesses. No single tool addresses all aspects of the workflow. We highlight how the different tools complement each other and some of the opportunities for their further development to address the continued evolution of read-across.
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Affiliation(s)
- Patlewicz Grace
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
| | - Helman George
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Pradeep Prachi
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Shah Imran
- National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA
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191
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Wong JC, Zidar J, Ho J, Wang Y, Lee KK, Zheng J, Sullivan MB, You X, Kriegel R. Assessment of several machine learning methods towards reliable prediction of hormone receptor binding affinity. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.cdc.2017.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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192
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Ruiz P, Sack A, Wampole M, Bobst S, Vracko M. Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. CHEMOSPHERE 2017; 178:99-109. [PMID: 28319747 PMCID: PMC8265162 DOI: 10.1016/j.chemosphere.2017.03.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 03/06/2017] [Accepted: 03/07/2017] [Indexed: 05/30/2023]
Abstract
Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.
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Affiliation(s)
- P Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, GA, USA.
| | - A Sack
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, GA, USA
| | - M Wampole
- Thomson Reuters, Philadelphia, PA, USA
| | - S Bobst
- ToxSci Advisors, Houston, TX, USA
| | - M Vracko
- Kemijski Inštitut/National Institute of Chemistry, Hajdrihova 19, 1000, Ljubljana, Slovenia
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193
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Gwinn MR, Axelrad DA, Bahadori T, Bussard D, Cascio WE, Deener K, Dix D, Thomas RS, Kavlock RJ, Burke TA. Chemical Risk Assessment: Traditional vs Public Health Perspectives. Am J Public Health 2017; 107:1032-1039. [PMID: 28520487 DOI: 10.2105/ajph.2017.303771] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Preventing adverse health effects of environmental chemical exposure is fundamental to protecting individual and public health. When done efficiently and properly, chemical risk assessment enables risk management actions that minimize the incidence and effects of environmentally induced diseases related to chemical exposure. However, traditional chemical risk assessment is faced with multiple challenges with respect to predicting and preventing disease in human populations, and epidemiological studies increasingly report observations of adverse health effects at exposure levels predicted from animal studies to be safe for humans. This discordance reinforces concerns about the adequacy of contemporary risk assessment practices for protecting public health. It is becoming clear that to protect public health more effectively, future risk assessments will need to use the full range of available data, draw on innovative methods to integrate diverse data streams, and consider health endpoints that also reflect the range of subtle effects and morbidities observed in human populations. Considering these factors, there is a need to reframe chemical risk assessment to be more clearly aligned with the public health goal of minimizing environmental exposures associated with disease.
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Affiliation(s)
- Maureen R Gwinn
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Daniel A Axelrad
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Tina Bahadori
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - David Bussard
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Wayne E Cascio
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Kacee Deener
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - David Dix
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Russell S Thomas
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Robert J Kavlock
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
| | - Thomas A Burke
- At the time of the writing of this article, all of the authors were with the US Environmental Protection Agency, Washington, DC
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194
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Tebes-Stevens C, Patel JM, Jones WJ, Weber EJ. Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2017; 51:5008-5016. [PMID: 28430419 PMCID: PMC6776422 DOI: 10.1021/acs.est.6b05412] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Cheminformatics-based software tools can predict the molecular structure of transformation products using a library of transformation reaction schemes. This paper presents the development of such a library for abiotic hydrolysis of organic chemicals under environmentally relevant conditions. The hydrolysis reaction schemes in the library encode the process science gathered from peer-reviewed literature and regulatory reports. Each scheme has been ranked on a scale of one to six based on the median half-life in a data set compiled from literature-reported hydrolysis rates. These ranks are used to predict the most likely transformation route when more than one structural fragment susceptible to hydrolysis is present in a molecule of interest. Separate rank assignments are established for pH 5, 7, and 9 to represent standard conditions in hydrolysis studies required for registration of pesticides in Organisation for Economic Co-operation and Development (OECD) member countries. The library is applied to predict the likely hydrolytic transformation products for two lists of chemicals, one representative of chemicals used in commerce and the other specific to pesticides, to evaluate which hydrolysis reaction pathways are most likely to be relevant for organic chemicals found in the natural environment.
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Affiliation(s)
- Caroline Tebes-Stevens
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, Georgia 30605, United States
- Corresponding Author: Phone: (706) 355-8218;
| | - Jay M. Patel
- ORISE Fellow, U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, Georgia 30605, United States
| | - W. Jack Jones
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, Georgia 30605, United States
| | - Eric J. Weber
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, Georgia 30605, United States
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195
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Asako Y, Uesawa Y. High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures. Molecules 2017; 22:molecules22040675. [PMID: 28441746 PMCID: PMC6154693 DOI: 10.3390/molecules22040675] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 04/16/2017] [Accepted: 04/19/2017] [Indexed: 12/20/2022] Open
Abstract
Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. Owing to the correlation between our model's and the challenge's results, we consider that our model currently possesses the highest predictive power on agonist activity of the estrogen receptor ligand-binding domain. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.
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Affiliation(s)
- Yuki Asako
- Department of Clinical Pharmaceutics Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan.
| | - Yoshihiro Uesawa
- Department of Clinical Pharmaceutics Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan.
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196
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Schymanski EL, Ruttkies C, Krauss M, Brouard C, Kind T, Dührkop K, Allen F, Vaniya A, Verdegem D, Böcker S, Rousu J, Shen H, Tsugawa H, Sajed T, Fiehn O, Ghesquière B, Neumann S. Critical Assessment of Small Molecule Identification 2016: automated methods. J Cheminform 2017; 9:22. [PMID: 29086042 PMCID: PMC5368104 DOI: 10.1186/s13321-017-0207-1] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 03/13/2017] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. RESULTS The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification-In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification-Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. CONCLUSIONS The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .
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Affiliation(s)
- Emma L Schymanski
- Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland.
| | - Christoph Ruttkies
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Martin Krauss
- Department of Effect-Directed Analysis, UFZ: Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
| | - Céline Brouard
- Department of Computer Science, Aalto University, Konemiehentie 2, 02150, Espoo, Finland
- Helsinki Institute for Information Technology, Tekniikantie 14, 02150, Espoo, Finland
| | - Tobias Kind
- West Coast Metabolomics Center and Genome Center, University of California Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Kai Dührkop
- Chair of Bioinformatics, Friedrich-Schiller-University, Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Felicity Allen
- Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E9, Canada
| | - Arpana Vaniya
- West Coast Metabolomics Center and Genome Center, University of California Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
- Department of Chemistry, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Dries Verdegem
- Metabolomics Expertise Center, Vesalius Research Center (VRC), VIB, KU Leuven - University of Leuven, 3000, Louvain, Belgium
| | - Sebastian Böcker
- Chair of Bioinformatics, Friedrich-Schiller-University, Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Juho Rousu
- Department of Computer Science, Aalto University, Konemiehentie 2, 02150, Espoo, Finland
- Helsinki Institute for Information Technology, Tekniikantie 14, 02150, Espoo, Finland
| | - Huibin Shen
- Department of Computer Science, Aalto University, Konemiehentie 2, 02150, Espoo, Finland
- Helsinki Institute for Information Technology, Tekniikantie 14, 02150, Espoo, Finland
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science (CSRS), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Tanvir Sajed
- Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E9, Canada
| | - Oliver Fiehn
- West Coast Metabolomics Center and Genome Center, University of California Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Bart Ghesquière
- Metabolomics Expertise Center, Vesalius Research Center (VRC), VIB, KU Leuven - University of Leuven, 3000, Louvain, Belgium
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
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197
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Slavov SH, Beger RD. Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2017; 36:823-830. [PMID: 27509091 DOI: 10.1002/etc.3578] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 07/06/2016] [Accepted: 08/09/2016] [Indexed: 06/06/2023]
Abstract
The estrogenic potential (expressed as a score composite of 18 high throughput screening bioassays) of 1528 compounds from the ToxCast database was modeled by a 3-dimensional spectral data activity relationship approach (3D-SDAR). Due to a lack of 17 O nuclear magnetic resonance (NMR) simulation software, the most informative carbon-carbon 3D-SDAR fingerprints were augmented with indicator variables representing oxygen atoms from carbonyl and carboxamide, ester, sulfonyl, nitro, aliphatic hydroxyl, and phenolic hydroxyl groups. To evaluate the true predictive performance of the authors' model the United States Environmental Protection Agency provided them with a blind test set consisting of 2008 compounds. Of these, 543 had available literature data-their binding affinity served to estimate the external classification accuracy of the developed model: predictive accuracy of 0.62, sensitivity of 0.71, and specificity of 0.53 were obtained. Compared with alternative modeling techniques, the authors' model displayed very little reduction in performance between the modeling and the prediction set. A 3D-SDAR mapping technique allowed identification of structural features essential for estrogenicity: 1) the presence of a phenolic OH group or cyclohexenone, 2) a second aromatic or phenolic ring at a distance of 6 Å to 8 Å from the oxygen of the first phenol ring, 3) the presence of a methyl group approximately 6 Å away from the centroid of a phenol ring, and 4) a carbonyl group in close proximity (∼4 Å measured to the centroid) to 1 of the phenol rings. Environ Toxicol Chem 2017;36:823-830. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
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Affiliation(s)
- Svetoslav H Slavov
- Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Richard D Beger
- Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
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198
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Sirenko O, Grimm FA, Ryan KR, Iwata Y, Chiu WA, Parham F, Wignall JA, Anson B, Cromwell EF, Behl M, Rusyn I, Tice RR. In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell-derived model. Toxicol Appl Pharmacol 2017; 322:60-74. [PMID: 28259702 DOI: 10.1016/j.taap.2017.02.020] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 01/24/2017] [Accepted: 02/27/2017] [Indexed: 01/22/2023]
Abstract
An important target area for addressing data gaps through in vitro screening is the detection of potential cardiotoxicants. Despite the fact that current conservative estimates relate at least 23% of all cardiovascular disease cases to environmental exposures, the identities of the causative agents remain largely uncharacterized. Here, we evaluate the feasibility of a combinatorial in vitro/in silico screening approach for functional and mechanistic cardiotoxicity profiling of environmental hazards using a library of 69 representative environmental chemicals and drugs. Human induced pluripotent stem cell-derived cardiomyocytes were exposed in concentration-response for 30min or 24h and effects on cardiomyocyte beating and cellular and mitochondrial toxicity were assessed by kinetic measurements of intracellular Ca2+ flux and high-content imaging using the nuclear dye Hoechst 33342, the cell viability marker Calcein AM, and the mitochondrial depolarization probe JC-10. More than half of the tested chemicals exhibited effects on cardiomyocyte beating after 30min of exposure. In contrast, after 24h, effects on cell beating without concomitant cytotoxicity were observed in about one third of the compounds. Concentration-response data for in vitro bioactivity phenotypes visualized using the Toxicological Prioritization Index (ToxPi) showed chemical class-specific clustering of environmental chemicals, including pesticides, flame retardants, and polycyclic aromatic hydrocarbons. For environmental chemicals with human exposure predictions, the activity-to-exposure ratios between modeled blood concentrations and in vitro bioactivity were between one and five orders of magnitude. These findings not only demonstrate that some ubiquitous environmental pollutants might have the potential at high exposure levels to alter cardiomyocyte function, but also indicate similarities in the mechanism of these effects both within and among chemicals and classes.
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Affiliation(s)
| | - Fabian A Grimm
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Kristen R Ryan
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Yasuhiro Iwata
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Frederick Parham
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | | | - Blake Anson
- Cellular Dynamics International, Madison, WI, USA
| | | | - Mamta Behl
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Raymond R Tice
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
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199
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Manibusan MK, Touart LW. A comprehensive review of regulatory test methods for endocrine adverse health effects. Crit Rev Toxicol 2017; 47:433-481. [PMID: 28617201 DOI: 10.1080/10408444.2016.1272095] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Development of new endocrine disruption-relevant test methods has been the subject of intensive research efforts for the past several decades, prompted in part by mandates in the 1996 Food Quality Protection Act (FQPA). While scientific understanding and test methods have advanced, questions remain on whether current scientific methods are capable of adequately addressing the complexities of the endocrine system for regulatory health and ecological risk assessments. The specific objective of this article is to perform a comprehensive, detailed evaluation of the adequacy of current test methods to inform regulatory risk assessments of whether a substance has the potential to perturb endocrine-related pathways resulting in human adverse effects. To that end, approximately 42 existing test guidelines (TGs) were considered in the evaluation of coverage for endocrine-related adverse effects. In addition to evaluations of whether test methods are adequate to capture endocrine-related effects, considerations of further enhancements to current test methods, along with the need to develop novel test methods to address existing test method gaps are described. From this specific evaluation, up to 35 test methods are capable of informing whether a chemical substance perturbs known endocrine related biological pathways. Based on these findings, it can be concluded that current validated test methods are adequate to discern substances that may perturb the endocrine system, resulting in an adverse health effect. Together, these test methods predominantly form the core data requirements of a typical food-use pesticide registration submission. It is recognized, however, that the current state of science is rapidly advancing and there is a need to update current test methods to include added enhancements to ensure continued coverage and public health and environmental protection.
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Affiliation(s)
| | - L W Touart
- b Equiparent Consulting , Woodbridge , VA , USA
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Chibwe L, Titaley IA, Hoh E, Massey Simonich SL. Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures. ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2017; 4:32-43. [PMID: 35600207 PMCID: PMC9119311 DOI: 10.1021/acs.estlett.6b00455] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Complex environmental mixtures consist of hundreds to thousands of unknown and unregulated organic compounds that may have toxicological relevance, including transformation products (TPs) of anthropogenic organic pollutants. Non-targeted analysis and suspect screening analysis offer analytical approaches for potentially identifying these toxic transformation products. However, additional tools and strategies are needed in order to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose risks to humans and the environment. This brief review highlights recent developments in this field and suggests an integrated framework that incorporates complementary instrumental techniques, computational chemistry, and toxicity analysis, for prioritizing and identifying toxic TPs in the environment.
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Affiliation(s)
- Leah Chibwe
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Ivan A. Titaley
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Eunha Hoh
- Graduate School of Public Health, San Diego State University, San Diego, CA, 92182, USA
| | - Staci L. Massey Simonich
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
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