1
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Boone KS, Di Toro DM, Davis CW, Parkerton TF, Redman A. In Silico Acute Aquatic Hazard Assessment and Prioritization Using a Grouped Target Site Model: A Case Study of Organic Substances Reported in Permian Basin Hydraulic Fracturing Operations. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024. [PMID: 38415890 DOI: 10.1002/etc.5826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/17/2023] [Accepted: 01/15/2024] [Indexed: 02/29/2024]
Abstract
Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;00:1-12. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Kathleen S Boone
- Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
| | - Dominic M Di Toro
- Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
| | - Craig W Davis
- ExxonMobil Biomedical Sciences, Annandale, New Jersey, USA
| | | | - Aaron Redman
- ExxonMobil Biomedical Sciences, Annandale, New Jersey, USA
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2
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Yang Y, Zhong J, Shen S, Huang J, Hong Y, Qu X, Chen Q, Niu B. Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment. Med Chem 2024; 20:2-16. [PMID: 37038674 DOI: 10.2174/1573406419666230406091759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 04/12/2023]
Abstract
Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.
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Affiliation(s)
- Yunfeng Yang
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Junjie Zhong
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Songyu Shen
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Jiajun Huang
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Yihan Hong
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, Goang Xi, China
| | - Qin Chen
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Bing Niu
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
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3
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Firman JW, Ebbrell DJ, Bauer FJ, Sapounidou M, Hodges G, Campos B, Roberts J, Gutsell S, Thomas PC, Bonnell M, Cronin MTD. Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:17805-17814. [PMID: 36445296 PMCID: PMC9775196 DOI: 10.1021/acs.est.2c03736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.
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Affiliation(s)
- James W. Firman
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| | - David J. Ebbrell
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| | - Franklin J. Bauer
- KREATiS
SAS, 23 rue du Creuzat, ZAC de St-Hubert 38080, L′Isle d′Abeau, France
| | - Maria Sapounidou
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| | - Geoff Hodges
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Bruno Campos
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Jayne Roberts
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Steve Gutsell
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Paul C. Thomas
- KREATiS
SAS, 23 rue du Creuzat, ZAC de St-Hubert 38080, L′Isle d′Abeau, France
| | - Mark Bonnell
- Science
and Risk Assessment Directorate, Environment
& Climate Change Canada, 351 St. Joseph Blvd, Gatineau, Quebec K1A 0H3, Canada
| | - Mark T. D. Cronin
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
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4
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Lambert FN, Vivian DN, Raimondo S, Tebes-Stevens CT, Barron MG. Relationships Between Aquatic Toxicity, Chemical Hydrophobicity, and Mode of Action: Log Kow Revisited. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2022; 83:326-338. [PMID: 35864329 PMCID: PMC11375592 DOI: 10.1007/s00244-022-00944-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
Relationships between toxicity and chemical hydrophobicity have been known for nearly 100 years in mammals and fish, typically using the log of the octanol:water partition coefficient (Kow). The current study reassessed the influence of mode of action (MOA) on acute aquatic toxicity-log Kow relationships using a comprehensive database of 617 organic chemicals with curated and standardized acute toxicity data that did not exceed solubility limits, their consensus log Kow values, and weight of evidence-based MOA classifications (including 6 broad and 26 specific MOAs). A total of 166 significant (p < 0.05) log Kow-toxicity models were developed across six taxa groups that included QSARs for 5 of the broad and 13 of the specific MOAs. In this study, we demonstrate that QSARs based on MOAs can significantly increase LC50 prediction accuracy for specific acting chemicals. Prediction accuracy increases when QSARs are built based on highly specific MOAs, rather than broad MOA classifications. Additionally, we demonstrate that building QSAR models with chemicals in specific MOA groupings, rather than broader MOA groups leads to significantly better estimates. We also evaluated the differences between models developed from mass-based (µg/L) and mole-based (µmol/L) toxicity data and demonstrate that both are suitable for QSAR development with no clear trend in greater model accuracy. Overall, the results reveal that, despite high variance in all taxa and MOA groups, specific MOA-based models can improve the accuracy of aquatic toxicity predictions over more general groupings.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The affiliations are correct.
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Affiliation(s)
- Faith N Lambert
- Office of Research and Development, U.S. EPA, U.S. EPA, 1 Sabine Island Drive, Gulf Breeze, FL, USA
- Syngenta, Research Triangle Park, NC, 27709, USA
| | - Deborah N Vivian
- Office of Research and Development, U.S. EPA, U.S. EPA, 1 Sabine Island Drive, Gulf Breeze, FL, USA
| | - Sandy Raimondo
- Office of Research and Development, U.S. EPA, U.S. EPA, 1 Sabine Island Drive, Gulf Breeze, FL, USA
| | | | - Mace G Barron
- Office of Research and Development, U.S. EPA, U.S. EPA, 1 Sabine Island Drive, Gulf Breeze, FL, USA.
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5
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Shavalieva G, Papadokonstantakis S, Peters G. Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity. J Chem Inf Model 2022; 62:4018-4031. [PMID: 35998659 PMCID: PMC9472271 DOI: 10.1021/acs.jcim.1c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Indexed: 11/30/2022]
Abstract
Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models' performance.
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Affiliation(s)
- Gulnara Shavalieva
- Department
of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Stavros Papadokonstantakis
- Department
of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Institute
of Chemical, Environmental and Bioscience Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
| | - Gregory Peters
- Department
of Technology Management and Economics, Chalmers University of Technology, SE-411 33 Gothenburg, Sweden
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6
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Zhang J, Wang C, Huang N, Xiang M, Jin L, Yang Z, Li S, Lu Z, Shi C, Cheng B, Xie H, Li H. Humic acid promoted activation of peroxymonosulfate by Fe 3S 4 for degradation of 2,4,6-trichlorophenol: An experimental and theoretical study. JOURNAL OF HAZARDOUS MATERIALS 2022; 434:128913. [PMID: 35452989 DOI: 10.1016/j.jhazmat.2022.128913] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
Chlorophenols are difficult to degrade and biohazardous in the natural environment. This study demonstrated that humic acid (HA) could promote Fe3S4 activation of peroxymonosulfate (PMS) to degrade 2,4,6-trichlorophenol (TCP), the degradation efficiency of TCP was increased by 33%. The system of Fe3S4-HA/PMS produced more reactive oxygen species, and •OH was the dominant ROS. The genealogy of iron oxides together with S0 on the Fe3S4 surface inhibited PMS activation leading to the significant reduction of TCP degraded (< 70%). These problems could be solved successfully through introducing HA, which facilitated electron transfer and increased the continuous release of iron ions by 2 times. In accordance with the determined density functional theory (DFT), the degradation pathway was put forward, which indicated that TCP dechlorination and oxidation to 2,6-dichloro-1,4-benzoquinone constituted the main degradation pathway. Furthermore, the intermediates that were produced in the main degradation processes of TCP showed lower toxicity than TCP according to results that were obtained utilizing the calculations of quantitative structure-activity relationship (QSAR) together with Toxicity Estimation Software Tool (TEST). Thus, the Fe3S4-HA/PMS system was demonstrated to be an efficient and safe technology for organic pollutant degradation in contaminated groundwater and surface water environments.
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Affiliation(s)
- Jin Zhang
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Chen Wang
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China.
| | - Nannan Huang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 10012, China
| | - Minghui Xiang
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Lide Jin
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Zhiyuan Yang
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Siyang Li
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Zhen Lu
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Chongli Shi
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Biao Cheng
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China
| | - Haijiao Xie
- Hangzhou Yanqu Information Technology Co., Ltd., Hangzhou, Zhejiang Province, 310003, PR China
| | - Hui Li
- Institute for Environmental pollution and health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, PR China.
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7
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Myatt GJ, Bassan A, Bower D, Johnson C, Miller S, Pavan M, Cross KP. Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100201. [PMID: 35036665 PMCID: PMC8754399 DOI: 10.1016/j.comtox.2021.100201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Mechanistically-driven alternative approaches to hazard assessment invariably require a battery of tests, including both in silico models and experimental data. The decision-making process, from selection of the methods to combining the information based on the weight-of-evidence, is ideally described in published guidelines or protocols. This ensures that the application of such approaches is defendable to reviewers within regulatory agencies and across the industry. Examples include the ICH M7 pharmaceutical impurities guideline and the published in silico toxicology protocols. To support an efficient, transparent, consistent and fully documented implementation of these protocols, a new and novel interactive software solution is described to perform such an integrated hazard assessment based on public and proprietary information.
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Affiliation(s)
| | | | - Dave Bower
- Instem, 1393 Dublin Road, Columbus, Ohio 43215, USA
| | | | - Scott Miller
- Instem, 1393 Dublin Road, Columbus, Ohio 43215, USA
| | - Manuela Pavan
- Innovatune, Via Giulio Zanon 130/D, 35129 Padova, Italy
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8
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Cross K, Johnson C, Myatt GJ. Implementation of In Silico Toxicology Protocols in Leadscope. Methods Mol Biol 2022; 2425:419-434. [PMID: 35188641 DOI: 10.1007/978-1-0716-1960-5_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In silico toxicology protocols help ensure the results from computational toxicology models are performed and documented in a standardized, consistent, transparent, and accepted manner. In silico toxicology protocols for skin sensitization and genetic toxicology have been previously published and have been implemented within the Leadscope software. The following chapter outlines how such protocols have been deployed in the Leadscope platform including integration with toxicology databases and a battery of computational toxicology models.
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9
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Dilger JM, Martin TM, Wilkins BP, Bohrer BC, Thoreson KM, Fedick PW. Detection and toxicity modeling of anthraquinone dyes and chlorinated side products from a colored smoke pyrotechnic reaction. CHEMOSPHERE 2022; 287:131845. [PMID: 34523441 PMCID: PMC10058345 DOI: 10.1016/j.chemosphere.2021.131845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 05/20/2023]
Abstract
"Green" pyrotechnics seek to remove known environmental pollutants and health hazards from their formulations. This chemical engineering approach often focuses on maintaining performance effects upon replacement of objectionable ingredients, yet neglects the chemical products formed by the exothermic reaction. In this work, milligram quantities of a lab-scale pyrotechnic red smoke composition were functioned within a thermal probe for product identification by pyrolysis-gas chromatography-mass spectrometry. Thermally decomposed ingredients and new side product derivatives were identified at lower relative abundances to the intact organic dye (as the engineered sublimation product). Side products included chlorination of the organic dye donated by the chlorate oxidizer. Machine learning quantitative structure-activity relationship models computed impacts to health and environmental hazards. High to very high toxicities were predicted for inhalation, mutagenicity, developmental, and endocrine disruption for common military pyrotechnic dyes and their analogous chlorinated side products. These results underscore the need to revise objectives of "green" pyrotechnic engineering.
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Affiliation(s)
- Jonathan M Dilger
- Naval Surface Warfare Center, Crane Division, 300 Highway 361, Crane, IN, 47522, USA.
| | - Todd M Martin
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, 26 W. Martin Luther King Dr., Cincinnati, OH, 45268, USA
| | - Benjamin P Wilkins
- Naval Surface Warfare Center, Crane Division, 300 Highway 361, Crane, IN, 47522, USA
| | - Brian C Bohrer
- Department of Chemistry, University of Southern Indiana, 8600 University Blvd., Evansville, IN, 47712, USA
| | - Kelly M Thoreson
- Naval Surface Warfare Center, Crane Division, 300 Highway 361, Crane, IN, 47522, USA
| | - Patrick W Fedick
- Naval Air Warfare Center Weapons Division, 1900 N. Knox Road, China Lake, CA, 93555, USA
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10
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Gajewicz-Skretna A, Furuhama A, Yamamoto H, Suzuki N. Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods. CHEMOSPHERE 2021; 280:130681. [PMID: 34162070 DOI: 10.1016/j.chemosphere.2021.130681] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 06/13/2023]
Abstract
There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.
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Affiliation(s)
- Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland.
| | - Ayako Furuhama
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan; Division of Genetics and Mutagenesis, National Institute of Health Sciences (NIHS), 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa, 210-9501, Japan
| | - Hiroshi Yamamoto
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan
| | - Noriyuki Suzuki
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan
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11
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Carnesecchi E, Toma C, Roncaglioni A, Kramer N, Benfenati E, Dorne JLCM. Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 735:139243. [PMID: 32480144 DOI: 10.1016/j.scitotenv.2020.139243] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD50) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R2 = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.
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Affiliation(s)
- Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.
| | - Cosimo Toma
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Alessandra Roncaglioni
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Nynke Kramer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
| | - Emilio Benfenati
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Jean Lou C M Dorne
- European Food Safety Authority (EFSA), Scientific Committee and Emerging Risks Unit, Via Carlo Magno 1A, 43126 Parma, Italy
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12
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Serra A, Önlü S, Festa P, Fortino V, Greco D. MaNGA: a novel multi-niche multi-objective genetic algorithm for QSAR modelling. Bioinformatics 2020; 36:145-153. [PMID: 31233136 DOI: 10.1093/bioinformatics/btz521] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/27/2019] [Accepted: 06/19/2019] [Indexed: 01/19/2023] Open
Abstract
SUMMARY Quantitative structure-activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs. AVAILABILITY AND IMPLEMENTATION The python implementation of MaNGA is available at https://github.com/Greco-Lab/MaNGA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere 33200, Finland
| | - Serli Önlü
- Faculty of Medicine and Health Technology, Tampere University, Tampere 33200, Finland
| | - Paola Festa
- Department of Mathematics and Applications, University of Napoli Federico II, Naples 80138, Italy
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Kuopio, 80101 Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere 33200, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, 00014 Finland.,BioMediTech Institute, Tampere University, Tampere 33200, Finland
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Mesoporous Silica Platforms with Potential Applications in Release and Adsorption of Active Agents. Molecules 2020; 25:molecules25173814. [PMID: 32825791 PMCID: PMC7503268 DOI: 10.3390/molecules25173814] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/13/2020] [Accepted: 08/20/2020] [Indexed: 01/01/2023] Open
Abstract
In recent years, researchers focused their attention on mesoporous silica nanoparticles (MSNs) owing to the considerable advancements of the characterization methods, especially electron microscopy methods, which allowed for a clear visualization of the pore structure and the materials encapsulated within the pores, along with the X-ray diffraction (small angles) methods and specific surface area determination by Brunauer–Emmett–Teller (BET) technique. Mesoporous silica gained important consideration in biomedical applications thanks to its tunable pore size, high surface area, surface functionalization possibility, chemical stability, and pore nature. Specifically, the nature of the pores allows for the encapsulation and release of anti-cancer drugs into tumor tissues, which makes MSN ideal candidates as drug delivery carriers in cancer treatment. Moreover, the inner and outer surfaces of the MSN provide a platform for further functionalization approaches that could enhance the adsorption of the drug within the silica network and the selective targeting and controlled release to the desired site. Additionally, stimuli-responsive mesoporous silica systems are being used as mediators in cancer therapy, and through the release of the therapeutic agents hosted inside the pores under the action of specific triggering factors, it can selectively deliver them into tumor tissues. Another important application of the mesoporous silica nanomaterials is related to its ability to extract different hazardous species from aqueous media, some of these agents being antibiotics, pesticides, or anti-tumor agents. The purpose of this paper is to analyze the methods of MSN synthesis and related characteristics, the available surface functionalization strategies, and the most important applications of MSN in adsorption as well as release studies. Owing to the increasing antibiotic resistance, the need for developing materials for antibiotic removal from wastewaters is important and mesoporous materials already proved remarkable performances in environmental applications, including removal or even degradation of hazardous agents such as antibiotics and pesticides.
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14
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Kienzler A, Connors KA, Bonnell M, Barron MG, Beasley A, Inglis CG, Norberg‐King TJ, Martin T, Sanderson H, Vallotton N, Wilson P, Embry MR. Mode of Action Classifications in the EnviroTox Database: Development and Implementation of a Consensus MOA Classification. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2019; 38:2294-2304. [PMID: 31269286 PMCID: PMC6851772 DOI: 10.1002/etc.4531] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 04/29/2019] [Accepted: 06/25/2019] [Indexed: 05/24/2023]
Abstract
Multiple mode of action (MOA) frameworks have been developed in aquatic ecotoxicology, mainly based on fish toxicity. These frameworks provide information on a key determinant of chemical toxicity, but the MOA categories and level of specificity remain unique to each of the classification schemes. The present study aimed to develop a consensus MOA assignment within EnviroTox, a curated in vivo aquatic toxicity database, based on the following MOA classification schemes: Verhaar (modified) framework, Assessment Tool for Evaluating Risk, Toxicity Estimation Software Tool, and OASIS. The MOA classifications from each scheme were first collapsed into one of 3 categories: non-specifically acting (i.e., narcosis), specifically acting, or nonclassifiable. Consensus rules were developed based on the degree of concordance among the 4 individual MOA classifications to attribute a consensus MOA to each chemical. A confidence rank was also assigned to the consensus MOA classification based on the degree of consensus. Overall, 40% of the chemicals were classified as narcotics, 17% as specifically acting, and 43% as unclassified. Sixty percent of chemicals had a medium to high consensus MOA assignment. When compared to empirical acute toxicity data, the general trend of specifically acting chemicals being more toxic is clearly observed for both fish and invertebrates but not for algae. EnviroTox is the first approach to establishing a high-level consensus across 4 computationally and structurally distinct MOA classification schemes. This consensus MOA classification provides both a transparent understanding of the variation between MOA classification schemes and an added certainty of the MOA assignment. In terms of regulatory relevance, a reliable understanding of MOA can provide information that can be useful for the prioritization (ranking) and risk assessment of chemicals. Environ Toxicol Chem 2019;38:2294-2304. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
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Affiliation(s)
- Aude Kienzler
- European Commission, Joint Research Centre, IspraItaly
| | | | - Mark Bonnell
- Environment and Climate Change Canada, GatineauQuebecCanada
| | - Mace G. Barron
- Gulf Ecology DivisionUS Environmental Protection Agency, Gulf BreezeFlorida
| | | | | | | | - Todd Martin
- US Environmental Protection Agency, CinncinatiOhio
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15
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Khan PM, Roy K, Benfenati E. Chemometric modeling of Daphnia magna toxicity of agrochemicals. CHEMOSPHERE 2019; 224:470-479. [PMID: 30831498 DOI: 10.1016/j.chemosphere.2019.02.147] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 06/09/2023]
Abstract
Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.
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Affiliation(s)
- Pathan Mohsin Khan
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India; Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy.
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
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16
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Van den Berg SJP, Baveco H, Butler E, De Laender F, Focks A, Franco A, Rendal C, Van den Brink PJ. Modeling the Sensitivity of Aquatic Macroinvertebrates to Chemicals Using Traits. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:6025-6034. [PMID: 31008596 PMCID: PMC6535724 DOI: 10.1021/acs.est.9b00893] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/15/2019] [Accepted: 04/22/2019] [Indexed: 05/31/2023]
Abstract
In this study, a trait-based macroinvertebrate sensitivity modeling tool is presented that provides two main outcomes: (1) it constructs a macroinvertebrate sensitivity ranking and, subsequently, a predictive trait model for each one of a diverse set of predefined Modes of Action (MOAs) and (2) it reveals data gaps and restrictions, helping with the direction of future research. Besides revealing taxonomic patterns of species sensitivity, we find that there was not one genus, family, or class which was most sensitive to all MOAs and that common test taxa were often not the most sensitive at all. Traits like life cycle duration and feeding mode were identified as important in explaining species sensitivity. For 71% of the species, no or incomplete trait data were available, making the lack of trait data the main obstacle in model construction. Research focus should therefore be on completing trait databases and enhancing them with finer morphological traits, focusing on the toxicodynamics of the chemical (e.g., target site distribution). Further improved sensitivity models can help with the creation of ecological scenarios by predicting the sensitivity of untested species. Through this development, our approach can help reduce animal testing and contribute toward a new predictive ecotoxicology framework.
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Affiliation(s)
- Sanne J. P. Van den Berg
- Aquatic
Ecology and Water Quality Management Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands
- Department
of Biology, University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium
| | - Hans Baveco
- Wageningen
Environmental Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
| | - Emma Butler
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK441LQ, United Kingdom
| | - Frederik De Laender
- Department
of Biology, University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium
| | - Andreas Focks
- Wageningen
Environmental Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
| | - Antonio Franco
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK441LQ, United Kingdom
| | - Cecilie Rendal
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK441LQ, United Kingdom
| | - Paul J. Van den Brink
- Aquatic
Ecology and Water Quality Management Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands
- Wageningen
Environmental Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
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17
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Boone KS, Di Toro DM. Target site model: Predicting mode of action and aquatic organism acute toxicity using Abraham parameters and feature-weighted k-nearest neighbors classification. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2019; 38:375-386. [PMID: 30506854 DOI: 10.1002/etc.4324] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 08/20/2018] [Accepted: 11/22/2018] [Indexed: 06/09/2023]
Abstract
A database of 1480 chemicals with 47 associated modes of action compiled from the literature encompasses a wide range of chemical classes (alkanes, polycyclic aromatic hydrocarbons, pesticides, and polar compounds) and includes toxicity data for 79 different aquatic genera. The data were split into a calibration group and a validation group (80/20) to apply k-nearest neighbors (k-NN) methodology to predict the toxic mode of action for the compound. Other approaches were tested (support vector machines and linear discriminant analysis) as well as variations in the k-NN technique (distance weighting, feature weighting). Best-prediction results were found with k = 3, in a voting platform with optimized feature weighting. Using the predicted mode of action, the appropriate polyparameter target site model for that mode of action is applied to calculate the 50% lethal concentration (LC50). Predicted LC50s for the validation database resulted in a root-mean squared error (RMSE) of 0.752. This can be compared to an RMSE of 0.655 for the same validation set using the reference mode of action labels. The complete database resulted in an RMSE of 0.793 for reference mode of action labels. This confirms that the classification model has sufficient accuracy for predicting the mode of action and for determining toxicity using the target site model. Environ Toxicol Chem 2019;38:375-386. © 2018 SETAC.
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Affiliation(s)
- Kathleen S Boone
- Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
| | - Dominic M Di Toro
- Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
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18
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Boone KS, Di Toro DM. Target site model: Application of the polyparameter target lipid model to predict aquatic organism acute toxicity for various modes of action. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2019; 38:222-239. [PMID: 30255636 DOI: 10.1002/etc.4278] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 09/18/2018] [Accepted: 09/19/2018] [Indexed: 06/08/2023]
Abstract
A database of 2049 chemicals with 47 associated modes of action (MoA) was compiled from the literature. The database includes alkanes, polycyclic aromatic hydrocarbons, pesticides, inorganic, and polar compounds. Brief descriptions of some critical MoA classification groups are provided. The MoA from the 14 sources were assigned using a variety of reliable experimental and modeling techniques. Toxicity information, chemical parameters, and solubility limits were combined with the MoA label information to create the data set used for model development. The model database was used to generate linear free energy relationships for each specific MoA using multilinear regression analysis. The model uses chemical-specific Abraham solute parameters estimated from AbSolv to determine MoA-specific solvent parameters. With this procedure, critical target site concentrations are determined for each genus. Statistical analysis showed a wide range in values of the solvent parameters for the significant MoA. Environ Toxicol Chem 2019;38:222-239. © 2018 SETAC.
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Affiliation(s)
- Kathleen S Boone
- Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
| | - Dominic M Di Toro
- Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
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19
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Myatt GJ, Ahlberg E, Akahori Y, Allen D, Amberg A, Anger LT, Aptula A, Auerbach S, Beilke L, Bellion P, Benigni R, Bercu J, Booth ED, Bower D, Brigo A, Burden N, Cammerer Z, Cronin MTD, Cross KP, Custer L, Dettwiler M, Dobo K, Ford KA, Fortin MC, Gad-McDonald SE, Gellatly N, Gervais V, Glover KP, Glowienke S, Van Gompel J, Gutsell S, Hardy B, Harvey JS, Hillegass J, Honma M, Hsieh JH, Hsu CW, Hughes K, Johnson C, Jolly R, Jones D, Kemper R, Kenyon MO, Kim MT, Kruhlak NL, Kulkarni SA, Kümmerer K, Leavitt P, Majer B, Masten S, Miller S, Moser J, Mumtaz M, Muster W, Neilson L, Oprea TI, Patlewicz G, Paulino A, Lo Piparo E, Powley M, Quigley DP, Reddy MV, Richarz AN, Ruiz P, Schilter B, Serafimova R, Simpson W, Stavitskaya L, Stidl R, Suarez-Rodriguez D, Szabo DT, Teasdale A, Trejo-Martin A, Valentin JP, Vuorinen A, Wall BA, Watts P, White AT, Wichard J, Witt KL, Woolley A, Woolley D, Zwickl C, Hasselgren C. In silico toxicology protocols. Regul Toxicol Pharmacol 2018; 96:1-17. [PMID: 29678766 DOI: 10.1016/j.yrtph.2018.04.014] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 03/16/2018] [Accepted: 04/16/2018] [Indexed: 10/17/2022]
Abstract
The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.
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Affiliation(s)
- Glenn J Myatt
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA.
| | - Ernst Ahlberg
- Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden
| | - Yumi Akahori
- Chemicals Evaluation and Research Institute, 1-4-25 Kouraku, Bunkyo-ku, Tokyo 112-0004 Japan
| | - David Allen
- Integrated Laboratory Systems, Inc., Research Triangle Park, NC, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lennart T Anger
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Aynur Aptula
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | | | | | - Joel Bercu
- Gilead Sciences, 333 Lakeside Drive, Foster City, CA, USA
| | - Ewan D Booth
- Syngenta, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK
| | - Dave Bower
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Natalie Burden
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Zoryana Cammerer
- Janssen Research & Development, 1400 McKean Road, Spring House, PA 19477, USA
| | - Mark T D Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Kevin P Cross
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Laura Custer
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Krista Dobo
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA
| | - Kevin A Ford
- Global Blood Therapeutics, South San Francisco, CA 94080, USA
| | - Marie C Fortin
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 170 Frelinghuysen Rd, Piscataway, NJ 08855, USA
| | | | - Nichola Gellatly
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | | | - Kyle P Glover
- Defense Threat Reduction Agency, Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD 21010, USA
| | - Susanne Glowienke
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH-4057, Basel, Switzerland
| | - Jacky Van Gompel
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Steve Gutsell
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Barry Hardy
- Douglas Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C, CH-4057 Basel / Basel-Stadt, Switzerland
| | - James S Harvey
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Jedd Hillegass
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Jui-Hua Hsieh
- Kelly Government Solutions, Research Triangle Park, NC 27709, USA
| | - Chia-Wen Hsu
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Kathy Hughes
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | | | - Robert Jolly
- Toxicology Division, Eli Lilly and Company, Indianapolis, IN, USA
| | - David Jones
- Medicines and Healthcare Products Regulatory Agency, 151 Buckingham Palace Road, London, SW1W 9SZ, UK
| | - Ray Kemper
- Vertex Pharmaceuticals Inc., Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, USA
| | - Michelle O Kenyon
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA
| | - Marlene T Kim
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Naomi L Kruhlak
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Sunil A Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | - Klaus Kümmerer
- Institute for Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Scharnhorststraße 1/C13.311b, 21335 Lüneburg, Germany
| | - Penny Leavitt
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Scott Masten
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Scott Miller
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Janet Moser
- Chemical Security Analysis Center, Department of Homeland Security, 3401 Ricketts Point Road, Aberdeen Proving Ground, MD 21010-5405, USA; Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43210, USA
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Louise Neilson
- British American Tobacco, Research and Development, Regents Park Road, Southampton, Hampshire, SO15 8TL, UK
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, Health Sciences Center, The University of New Mexico, NM, USA
| | - Grace Patlewicz
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, NC 27711, USA
| | - Alexandre Paulino
- SAPEC Agro, S.A., Avenida do Rio Tejo, Herdade das Praias, 2910-440 Setúbal, Portugal
| | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland
| | - Mark Powley
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - Andrea-Nicole Richarz
- European Commission, Joint Research Centre, Directorate for Health, Consumers and Reference Materials, Chemical Safety and Alternative Methods Unit, Via Enrico Fermi 2749, 21027 Ispra, VA, Italy
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Benoit Schilter
- Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland
| | | | - Wendy Simpson
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Lidiya Stavitskaya
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - David T Szabo
- RAI Services Company, 950 Reynolds Blvd., Winston-Salem, NC 27105, USA
| | | | | | | | | | - Brian A Wall
- Colgate-Palmolive Company, Piscataway, NJ 08854, USA
| | - Pete Watts
- Bibra, Cantium House, Railway Approach, Wallington, Surrey, SM6 0DZ, UK
| | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Joerg Wichard
- Bayer Pharma AG, Investigational Toxicology, Muellerstr. 178, D-13353 Berlin, Germany
| | - Kristine L Witt
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Adam Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
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Devillers J, Devillers H, Bro E, Millot F. Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:889-911. [PMID: 29206499 DOI: 10.1080/1062936x.2017.1402449] [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: 10/23/2017] [Accepted: 11/04/2017] [Indexed: 06/07/2023]
Abstract
A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD50) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.
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Affiliation(s)
| | - H Devillers
- b Micalis Institute, INRA, University Paris-Saclay , Jouy-en-Josas , France
| | - E Bro
- c Research Department , National Game and Wildlife Institute (ONCFS) , Auffargis , France
| | - F Millot
- c Research Department , National Game and Wildlife Institute (ONCFS) , Auffargis , France
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21
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Li T, Liu SS, Qu R, Liu HL. Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2017; 144:475-481. [PMID: 28667859 DOI: 10.1016/j.ecoenv.2017.06.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 06/13/2017] [Accepted: 06/15/2017] [Indexed: 05/03/2023]
Abstract
The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC50 ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC30, and EC70 ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs.
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Affiliation(s)
- Tong Li
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
| | - Rui Qu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hai-Ling Liu
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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22
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Kienzler A, Barron MG, Belanger SE, Beasley A, Embry MR. Mode of Action (MOA) Assignment Classifications for Ecotoxicology: An Evaluation of Approaches. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2017; 51:10203-10211. [PMID: 28759717 DOI: 10.1021/acs.est.7b02337] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
The mode of toxic action (MOA) is recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, MOA classification has never been standardized in ecotoxicology, and a comprehensive comparison of classification tools and approaches has never been reported. Here we critically evaluate three MOA classification methodologies using an aquatic toxicity data set of 3448 chemicals, compare the approaches, and assess utility and limitations in screening and early tier assessments. The comparisons focused on three commonly used tools: Verhaar prediction of toxicity MOA, the U.S. Environmental Protection Agency (EPA) ASsessment Tool for Evaluating Risk (ASTER) QSAR (quantitative structure activity relationship) application, and the EPA Mode of Action and Toxicity (MOAtox) database. Of the 3448 MOAs predicted using the Verhaar scheme, 1165 were classified by ASTER, and 802 were available in MOAtox. Of the subset of 432 chemicals with MOA assignments for each of the three schemes, 42% had complete concordance in MOA classification, and there was no agreement for 7% of the chemicals. The research shows the potential for large differences in MOA classification between the five broad groups of the Verhaar scheme and the more mechanism-based assignments of ASTER and MOAtox. Harmonization of classification schemes is needed to use MOA classification in chemical hazard and risk assessment more broadly.
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Affiliation(s)
- A Kienzler
- Joint Research Centre , Directorate F-Health, Consumers, and Reference Materials, F.3 Chemicals Safety & Alternative Methods, TP 126, Via E. Fermi, 2749, I-21027 Ispra, Italy
| | - M G Barron
- United States Environmental Protection Agency , 1 Sabine Island Drive, Gulf Breeze, Florida 32561, United States
| | - S E Belanger
- The Procter & Gamble Company , Mason Business Center, 8700 S Mason-Montgomery Road, Mason, Ohio 45040, United States
| | - A Beasley
- TERC Toxicology and Environmental Research and Consulting, The Dow Chemical Company , 1803 Building, Midland, Michigan 48674, United States
| | - M R Embry
- International Life Sciences Institute Health and Environmental Sciences Institute (HESI) . 1156 15th Street, NW, Suite 200, Washington, District of Columbia 20005, United States
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23
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Martin TM, Lilavois CR, Barron MG. Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:525-539. [PMID: 28703021 PMCID: PMC5796665 DOI: 10.1080/1062936x.2017.1343204] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Accepted: 06/14/2017] [Indexed: 05/25/2023]
Abstract
Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r2 = 0.50) than the LDA method (r2 ~ 0.47). A single model fit to the entire training set yielded the poorest results (r2 = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.
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Affiliation(s)
- Todd M Martin
- U.S. Environmental Protection Agency, Office of Research and Development, Sustainable Technology Division, Cincinnati, OH 45220
| | - Crystal R Lilavois
- U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL 32561
| | - Mace G Barron
- U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL 32561
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24
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Abbasitabar F, Zare-Shahabadi V. In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach. CHEMOSPHERE 2017; 172:249-259. [PMID: 28081509 DOI: 10.1016/j.chemosphere.2016.12.095] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2016] [Revised: 11/29/2016] [Accepted: 12/19/2016] [Indexed: 05/27/2023]
Abstract
Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for Rtraining2 and Rtest2, respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for Rtraining2 and Rtest2, respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (Rtraining2 and Rtest2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.
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Affiliation(s)
- Fatemeh Abbasitabar
- Department of Chemistry, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
| | - Vahid Zare-Shahabadi
- Department of Chemistry, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
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25
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Carriger JF, Martin TM, Barron MG. A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2016; 180:11-24. [PMID: 27640153 DOI: 10.1016/j.aquatox.2016.09.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/06/2016] [Accepted: 09/07/2016] [Indexed: 05/20/2023]
Abstract
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by identifying a subset of the key chemical descriptors associated with broad aquatic toxicity MoAs, and by providing a computational chemistry-based network classification model with reasonable prediction accuracy.
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Affiliation(s)
- John F Carriger
- U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561, United States
| | - Todd M Martin
- U.S. Environmental Protection Agency, Office of Research and Development, Sustainable Technology Division, Cincinnati, OH, 45220, United States
| | - Mace G Barron
- U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561, United States.
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26
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Ellison CM, Piechota P, Madden JC, Enoch SJ, Cronin MTD. Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:3995-4007. [PMID: 26889772 DOI: 10.1021/acs.est.5b05918] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure-activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems.
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Affiliation(s)
- Claire M Ellison
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool, L3 3AF England
| | - Przemyslaw Piechota
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool, L3 3AF England
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool, L3 3AF England
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool, L3 3AF England
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool, L3 3AF England
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27
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Wu X, Zhang Q, Hu J. QSAR study of the acute toxicity to fathead minnow based on a large dataset. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:147-164. [PMID: 26911563 DOI: 10.1080/1062936x.2015.1137353] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Acute fathead minnow toxicity is an important basis of hazard and risk assessment for compounds in the aquatic environment. In this paper, a large dataset consisting of 963 organic compounds with acute toxicity towards fathead minnow was studied with a QSAR approach. All molecular structures of compounds were optimized by the hybrid density functional theory method. Dragon molecular descriptors and log Kow were selected to describe molecular information. Genetic algorithm and multiple linear regression analysis were combined to develop models. A global prediction model for compounds without known mode of action and two local models for organic compounds that exhibit narcosis toxicity and excess toxicity were developed, respectively. For all developed models, internal validations were performed by cross-validation and external validations were implemented by the setting of validation set. In addition, applicability domains of models were evaluated using a leverage method and outliers were listed and checked using toxicological knowledge.
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Affiliation(s)
- X Wu
- a Environment Research Institute, Shandong University , Jinan , P.R. China
| | - Q Zhang
- a Environment Research Institute, Shandong University , Jinan , P.R. China
| | - J Hu
- a Environment Research Institute, Shandong University , Jinan , P.R. China
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28
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Ellison CM, Madden JC, Cronin MTD, Enoch SJ. Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from Toxtree ver. 2.6. CHEMOSPHERE 2015; 139:146-154. [PMID: 26092094 DOI: 10.1016/j.chemosphere.2015.06.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 06/02/2015] [Accepted: 06/04/2015] [Indexed: 06/04/2023]
Abstract
Assessment of the potential of compounds to cause harm to the aquatic environment is an integral part of the REACH legislation. To reduce the number of vertebrate and invertebrate animals required for this analysis alternative approaches have been promoted. Category formation and read-across have been applied widely to predict toxicity. A key approach to grouping for environmental toxicity is the Verhaar scheme which uses rules to classify compounds into one of four mechanistic categories. These categories provide a mechanistic basis for grouping and any further predictive modelling. A computational implementation of the Verhaar scheme is available in Toxtree v2.6. The work presented herein demonstrates how modifications to the implementation of Verhaar between version 1.5 and 2.6 of Toxtree have improved performance by reducing the number of incorrectly classified compounds. However, for the datasets used in this analysis, version 2.6 classifies more compounds as outside of the domain of the model. Further amendments to the classification rules have been implemented here using a post-processing filter encoded as a KNIME workflow. This results in fewer compounds being classified as outside of the model domain, further improving the predictivity of the scheme. The utility of the modification described herein is demonstrated through building quality, mechanism-specific Quantitative Structure Activity Relationship (QSAR) models for the compounds within specific mechanistic categories.
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Affiliation(s)
- Claire M Ellison
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom.
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29
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Steinmetz FP, Madden JC, Cronin MTD. Data Quality in the Human and Environmental Health Sciences: Using Statistical Confidence Scoring to Improve QSAR/QSPR Modeling. J Chem Inf Model 2015; 55:1739-46. [DOI: 10.1021/acs.jcim.5b00294] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fabian P. Steinmetz
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England
| | - Judith C. Madden
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England
| | - Mark T. D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England
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