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Jeong J, Kim J, Choi J. Identification of molecular initiating events (MIE) using chemical database analysis and nuclear receptor activity assays for screening potential inhalation toxicants. Regul Toxicol Pharmacol 2023; 141:105391. [PMID: 37068727 DOI: 10.1016/j.yrtph.2023.105391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/13/2022] [Accepted: 04/13/2023] [Indexed: 04/19/2023]
Abstract
An adverse outcome pathway (AOP) framework can facilitate the use of alternative assays in chemical regulations by providing scientific evidence. Previously, an AOP, peroxisome proliferative-activating receptor gamma (PPARγ) antagonism that leads to pulmonary fibrosis, was developed. Based on a literature search, PPARγ inactivation has been proposed as a molecular initiating event (MIE). In addition, a list of candidate chemicals that could be used in the experimental validation was proposed using toxicity database and deep learning models. In this study, the screening of environmental chemicals for MIE was conducted using in silico and in vitro tests to maximize the applicability of this AOP for screening inhalation toxicants. Initially, potential inhalation exposure chemicals that are active in three or more key events were selected, and in silico molecular docking was performed. Among the chemicals with low binding energy to PPARγ, nine chemicals were selected for validation of the AOP using in vitro PPARγ activity assay. As a result, rotenone, triorthocresyl phosphate, and castor oil were proposed as PPARγ antagonists and stressor chemicals of the AOP. Overall, the proposed tiered approach of the database-in silico-in vitro can help identify the regulatory applicability and assist in the development and experimental validation of AOP.
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Affiliation(s)
- Jaeseong Jeong
- School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, Republic of Korea
| | - Jiwan Kim
- School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, Republic of Korea
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, Republic of Korea.
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2
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M Pauzi NA, Cheema MS, Ismail A, Ghazali AR, Abdullah R. Safety assessment of natural products in Malaysia: current practices, challenges, and new strategies. REVIEWS ON ENVIRONMENTAL HEALTH 2022; 37:169-179. [PMID: 34582637 DOI: 10.1515/reveh-2021-0072] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/06/2021] [Indexed: 06/13/2023]
Abstract
The belief that natural products are inherently safe is a primary reason for consumers to choose traditional medicines and herbal supplements for health maintenance and disease prevention. Unfortunately, some natural products on the market have been found to contain toxic compounds, such as heavy metals and microbes, as well as banned ingredients such as aristolochic acids. It shows that the existing regulatory system is inadequate and highlights the importance of thorough safety evaluations. In Malaysia, the National Pharmaceutical Regulatory Agency is responsible for the regulatory control of medicinal products and cosmetics, including natural products. For registration purpose, the safety of natural products is primarily determined through the review of documents, including monographs, research articles and scientific reports. One of the main factors hampering safety evaluations of natural products is the lack of toxicological data from animal studies. However, international regulatory agencies such as the European Food Safety Authority and the United States Food and Drug Administration are beginning to accept data obtained using alternative strategies such as non-animal predictive toxicological tools. Our paper discusses the use of state-of-the-art techniques, including chemometrics, in silico modelling and omics technologies and their applications to the safety assessments of natural products.
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Affiliation(s)
- Nur Azra M Pauzi
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
- Ministry of Health, Kompleks E, Pusat Pentadbiran Kerajaan Persekutuan, Putrajaya, Malaysia
| | - Manraj S Cheema
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
| | - Amin Ismail
- Department of Nutrition, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
| | - Ahmad Rohi Ghazali
- Biomedical Sciences Programmes, Faculty of Health Sciences, Universiti Kebangsaan Malaysia Kuala Lumpur, Malaysia
| | - Rozaini Abdullah
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
- Natural Medicines and Products Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, Selangor, Malaysia
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3
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Pan Z, Liu Q, Xu J, Li W, Lin H. Microplastic contamination in seafood from Dongshan Bay in southeastern China and its health risk implication for human consumption. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 303:119163. [PMID: 35305345 DOI: 10.1016/j.envpol.2022.119163] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
Microplastic (MP) pollution has been a considerable concern due to its ubiquity in the environment and its potential to harm human health. Unfortunately, the exact levels of MP in various species of seafood species have not been established. It is also unclear whether or not consuming seafood contaminated with MPs directly jeopardizes human health. Here, eight popular species of seafood in Dongshan Bay, China were investigated to determine the presence of MP pollution and its implications on human health. The abundance, color, size, shape, type, surface morphology, danger of the MPs extracted from the seafood were analyzed. Results showed that the average MP abundance in the shellfish and fish was 1.88 ± 1.44 and 1.98 ± 1.98 items individual-1, respectively. The heavy presence of fibers may be attributed to the shellfish and fish's feeding behaviors as well as their habitat and environment. The sizes of MPs found were below 1.0 mm. The main types of MP found in the shellfish were PES and PET, whereas the main types found in the fish were PS and PES. Risk assessment suggested that MPs in the shellfish (risk Level V) posed a greater and more direct threat to human health if the shellfish is eaten whole. The MPs in the gastrointestinal tracts (GITs) of fish (risk Level IV) have a relatively limited effect on human health since GITs are seldom consumed by humans unless the fish is heavily processed (canned or dried). MPs-induced health risk is predicted using a technique called molecular docking. The results of this study not only establish levels of MP pollution in popular seafood species but also help understand the implications of consuming MP-contaminated seafood on human health.
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Affiliation(s)
- Zhong Pan
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China; Fujian Provincial Station for Field Observation and Research of Island and Costal Zone in Zhangzhou, Zhangzhou, 363216, China; Observation and Research Station of Island and Coastal Ecosystem in the Western Taiwan Strait, Ministry of Natural Resources, Xiamen, 361005, China.
| | - Qianlong Liu
- College of Ocean and Earth Science, Xiamen University, Xiamen, 361102, China
| | - Jing Xu
- College of Ocean and Earth Science, Xiamen University, Xiamen, 361102, China
| | - Weiwen Li
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Hui Lin
- Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
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Katsumura S, Siddiqui N, Goldsmith MR, Cheah JH, Fujikawa T, Minegishi G, Yamagata A, Yabuki Y, Kobayashi K, Shirouzu M, Inagaki T, Huang THM, Musi N, Topisirovic I, Larsson O, Morita M. Deadenylase-dependent mRNA decay of GDF15 and FGF21 orchestrates food intake and energy expenditure. Cell Metab 2022; 34:564-580.e8. [PMID: 35385705 PMCID: PMC9386786 DOI: 10.1016/j.cmet.2022.03.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 10/26/2021] [Accepted: 03/14/2022] [Indexed: 12/11/2022]
Abstract
Hepatokines, secretory proteins from the liver, mediate inter-organ communication to maintain a metabolic balance between food intake and energy expenditure. However, molecular mechanisms by which hepatokine levels are rapidly adjusted following stimuli are largely unknown. Here, we unravel how CNOT6L deadenylase switches off hepatokine expression after responding to stimuli (e.g., exercise and food) to orchestrate energy intake and expenditure. Mechanistically, CNOT6L inhibition stabilizes hepatic Gdf15 and Fgf21 mRNAs, increasing corresponding serum protein levels. The resulting upregulation of GDF15 stimulates the hindbrain to suppress appetite, while increased FGF21 affects the liver and adipose tissues to induce energy expenditure and lipid consumption. Despite the potential of hepatokines to treat metabolic disorders, their administration therapies have been challenging. Using small-molecule screening, we identified a CNOT6L inhibitor enhancing GDF15 and FGF21 hepatokine levels, which dramatically improves diet-induced metabolic syndrome. Our discovery, therefore, lays the foundation for an unprecedented strategy to treat metabolic syndrome.
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Affiliation(s)
- Sakie Katsumura
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Nadeem Siddiqui
- Department of Biochemistry and Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada
| | | | - Jaime H Cheah
- High Throughput Sciences Facility, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Teppei Fujikawa
- Center for Hypothalamic Research, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Genki Minegishi
- Laboratory of DDS Design and Drug Disposition, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan
| | - Atsushi Yamagata
- RIKEN Center for Biosystems Dynamics Research, Yokohama, Kanagawa 230-0045, Japan
| | - Yukako Yabuki
- RIKEN Center for Biosystems Dynamics Research, Yokohama, Kanagawa 230-0045, Japan
| | - Kaoru Kobayashi
- Department of Biopharmaceutics, Graduate School of Clinical Pharmacy, Meiji Pharmaceutical University, Kiyose-shi, Tokyo 204-8588, Japan
| | - Mikako Shirouzu
- RIKEN Center for Biosystems Dynamics Research, Yokohama, Kanagawa 230-0045, Japan
| | - Takeshi Inagaki
- Laboratory of Epigenetics and Metabolism, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi-shi, Gunma 371-8512, Japan
| | - Tim H-M Huang
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Nicolas Musi
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA; San Antonio Geriatric Research, Education, and Clinical Center, South Texas Veterans Health Care System, San Antonio, TX 78229, USA
| | - Ivan Topisirovic
- Lady Davis Institute, Sir Mortimer B. Davis Jewish General Hospital, Montreal, QC H3A 1A3, Canada; Gerald Bronfman Department of Oncology, Division of Experimental Medicine and Department of Biochemistry, McGill University, Montreal, QC H3A 1A3, Canada
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, 171 65 Stockholm, Sweden
| | - Masahiro Morita
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA; Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
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5
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Koh DH, Song WS, Kim EY. Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists. CHEMOSPHERE 2022; 286:131540. [PMID: 34346341 DOI: 10.1016/j.chemosphere.2021.131540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/08/2021] [Accepted: 07/10/2021] [Indexed: 06/13/2023]
Abstract
In discovering the potential antagonist of peroxisome proliferator-activated receptor gamma (PPARγ), the structure-activity relationship (SAR) is a useful in silico method. However, it is difficult for conventional SAR approaches to predict the activities of antagonists owing to the large structural diversity of antagonistic compounds. This study provides evidence that multi-step SAR screening is applicable for predicting PPARγ antagonists by combining different complementary methodologies. We constructed three models: read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. To provide user-customized prediction results, our multi-step SAR screening model combined the three SAR models in a stepwise manner, which subdivided them according to potential levels of the PPARγ antagonist. The read-across-like SAR, which considered specific antagonist scaffolds, revealed the highest positive predictive value (PPV). The docking-simulation-interpreting SAR, which considered the molecular surface features, revealed high statistics for the PPV and the true-positive rate (TPR). The deep-learning-based SAR showed the highest TPR at the last classification step. This multi-step SAR screening covered the antagonists of high reliability provided by a read-across-like SAR, as well as the antagonists of diverse scaffolds provided by docking-simulation-interpreting SAR and deep-learning-based SAR. Therefore, to predict PPARγ antagonists, multi-step SAR screening could be as a useful tool.
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Affiliation(s)
- Dong-Hee Koh
- Department of Life and Nanopharmaceutical Science, South Korea
| | - Woo-Seon Song
- Department of Life and Nanopharmaceutical Science, South Korea
| | - Eun-Young Kim
- Department of Life and Nanopharmaceutical Science, South Korea; Department of Biology, Kyung Hee University, Hoegi-Dong, Dongdaemun-Gu, Seoul, 130-701, South Korea.
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6
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Fenton SE, Ducatman A, Boobis A, DeWitt JC, Lau C, Ng C, Smith JS, Roberts SM. Per- and Polyfluoroalkyl Substance Toxicity and Human Health Review: Current State of Knowledge and Strategies for Informing Future Research. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2021; 40:606-630. [PMID: 33017053 PMCID: PMC7906952 DOI: 10.1002/etc.4890] [Citation(s) in RCA: 694] [Impact Index Per Article: 231.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/29/2020] [Accepted: 09/20/2020] [Indexed: 01/09/2023]
Abstract
Reports of environmental and human health impacts of per- and polyfluoroalkyl substances (PFAS) have greatly increased in the peer-reviewed literature. The goals of the present review are to assess the state of the science regarding toxicological effects of PFAS and to develop strategies for advancing knowledge on the health effects of this large family of chemicals. Currently, much of the toxicity data available for PFAS are for a handful of chemicals, primarily legacy PFAS such as perfluorooctanoic acid and perfluorooctane sulfonate. Epidemiological studies have revealed associations between exposure to specific PFAS and a variety of health effects, including altered immune and thyroid function, liver disease, lipid and insulin dysregulation, kidney disease, adverse reproductive and developmental outcomes, and cancer. Concordance with experimental animal data exists for many of these effects. However, information on modes of action and adverse outcome pathways must be expanded, and profound differences in PFAS toxicokinetic properties must be considered in understanding differences in responses between the sexes and among species and life stages. With many health effects noted for a relatively few example compounds and hundreds of other PFAS in commerce lacking toxicity data, more contemporary and high-throughput approaches such as read-across, molecular dynamics, and protein modeling are proposed to accelerate the development of toxicity information on emerging and legacy PFAS, individually and as mixtures. In addition, an appropriate degree of precaution, given what is already known from the PFAS examples noted, may be needed to protect human health. Environ Toxicol Chem 2021;40:606-630. © 2020 SETAC.
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Affiliation(s)
- Suzanne E. Fenton
- National Toxicology Program Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Alan Ducatman
- West Virginia University School of Public Health, Morgantown, West Virginia, USA
| | - Alan Boobis
- Imperial College London, London, United Kingdom
| | - Jamie C. DeWitt
- Department of Pharmacology and Toxicology, Brody School of Medicine, East Carolina University, Greenville, North Carolina, USA
| | - Christopher Lau
- Public Health and Integrated Toxicology Division, Center for Public Health and Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Carla Ng
- Departments of Civil and Environmental Engineering and Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James S. Smith
- Navy and Marine Corps Public Health Center, Portsmouth, Virginia, USA
| | - Stephen M. Roberts
- Center for Environmental & Human Toxicology, University of Florida, Gainesville, Florida, USA
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7
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Kamerlin N, Delcey MG, Manzetti S, van der Spoel D. Toward a Computational Ecotoxicity Assay. J Chem Inf Model 2020; 60:3792-3803. [PMID: 32648756 DOI: 10.1021/acs.jcim.0c00574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.
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Affiliation(s)
- Natasha Kamerlin
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Mickaël G Delcey
- Department of Chemistry-Ångström Laboratory, Uppsala University, SE-75120 Uppsala, Sweden
| | - Sergio Manzetti
- Institute for Science and Technology, Fjordforsk A.S., Midtun, 6894 Vangsnes, Norway
| | - David van der Spoel
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
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8
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QSAR, molecular docking approach on the estrogenic activities of persistent organic pollutants using quantum chemical disruptors. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-1624-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
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Jeong J, Kim H, Choi J. In Silico Molecular Docking and In Vivo Validation with Caenorhabditis elegans to Discover Molecular Initiating Events in Adverse Outcome Pathway Framework: Case Study on Endocrine-Disrupting Chemicals with Estrogen and Androgen Receptors. Int J Mol Sci 2019; 20:ijms20051209. [PMID: 30857347 PMCID: PMC6429066 DOI: 10.3390/ijms20051209] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 03/05/2019] [Accepted: 03/06/2019] [Indexed: 12/25/2022] Open
Abstract
Molecular docking is used to analyze structural complexes of a target with its ligand for understanding the chemical and structural basis of target specificity. This method has the potential to be applied for discovering molecular initiating events (MIEs) in the Adverse Outcome Pathway framework. In this study, we aimed to develop in silico–in vivo combined approach as a tool for identifying potential MIEs. We used environmental chemicals from Tox21 database to identify potential endocrine-disrupting chemicals (EDCs) through molecular docking simulation, using estrogen receptor (ER), androgen receptor (AR) and their homology models in the nematode Caenorhabditis elegans (NHR-14 and NHR-69, respectively). In vivo validation was conducted on the selected EDCs with C. elegans reproductive toxicity assay using wildtype N2, nhr-14, and nhr-69 loss-of-function mutant strains. The chemicals showed high binding affinity to tested receptors and showed the high in vivo reproductive toxicity, and this was further confirmed using the mutant strains. The present study demonstrates that the binding affinity from the molecular docking potentially correlates with in vivo toxicity. These results prove that our in silico–in vivo combined approach has the potential to be applied for identifying MIEs. This study also suggests the potential of C. elegans as useful in the in vivo model for validating the in silico approach.
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Affiliation(s)
- Jaeseong Jeong
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Korea.
| | - Hunbeen Kim
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Korea.
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Korea.
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10
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Lu L, Zhan T, Ma M, Xu C, Wang J, Zhang C, Liu W, Zhuang S. Thyroid Disruption by Bisphenol S Analogues via Thyroid Hormone Receptor β: in Vitro, in Vivo, and Molecular Dynamics Simulation Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:6617-6625. [PMID: 29763311 DOI: 10.1021/acs.est.8b00776] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Bisphenol S (4-hydroxyphenyl sulfone, BPS) is increasingly used as a bisphenol A (BPA) alternative. The global usage of BPS and its analogues (BPSs) resulted in the frequent detection of their residues in multiple environmental media. We investigated their potential endocrine-disrupting effects toward thyroid hormone receptor (TR) β. The molecular interaction of BPSs toward TRβ ligand binding domain (LBD) was probed by fluorescence spectroscopy and molecular dynamics (MD) simulations. BPSs caused the static fluorescence quenching of TRβ LBD. The 100 ns MD simulations revealed that the binding of BPSs caused significant changes in the distance between residue His435 at helix 11(H11) and residue Phe459 at H12 in comparison to no ligand-bound TRβ LBD, indicating relative repositioning of H12. The recombinant two-hybrid yeast assay showed that tetrabromobisphenol S (TBBPS) and tetrabromobisphenol A (TBBPA) have potent antagonistic activity toward TRβ, with an IC10 of 10.1 and 21.1 nM, respectively. BPS and BPA have the antagonistic activity with IC10 of 312 and 884 nM, respectively. BPSs significantly altered the expression level of mRNA of TRβ gene in zebrafish embryos. BPS and TBBPS at environmentally relevant concentrations have antagonistic activity toward TRβ, implying that BPSs are not safe BPA alternatives in many BPA-free products. Future health risk assessments for TR disruption and other adverse effects should focus more on the structure-activity relationship in the design of environmentally benign BPA alternatives.
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Affiliation(s)
- Liping Lu
- College of Environmental and Resource Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Tingjie Zhan
- College of Environmental and Resource Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Mei Ma
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences , Chinese Academy of Sciences , Beijing 100085 , China
- College of Resources and Environment , University of Chinese Academy of Sciences , Beijing 100085 , China
| | - Chao Xu
- College of Environment , Zhejiang University of Technology , Hangzhou 310032 , China
| | - Jingpeng Wang
- College of Environmental and Resource Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Chunlong Zhang
- Department of Biological and Environmental Sciences , University of Houston-Clear Lake , 2700 Bay Area Boulevard , Houston , Texas 77058 , United States
| | - Weiping Liu
- College of Environmental and Resource Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Shulin Zhuang
- College of Environmental and Resource Sciences , Zhejiang University , Hangzhou 310058 , China
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11
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Al Sharif M, Tsakovska I, Pajeva I, Alov P, Fioravanzo E, Bassan A, Kovarich S, Yang C, Mostrag-Szlichtyng A, Vitcheva V, Worth AP, Richarz AN, Cronin MT. The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation. Toxicology 2017; 392:140-154. [DOI: 10.1016/j.tox.2016.01.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 01/17/2016] [Accepted: 01/24/2016] [Indexed: 12/18/2022]
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12
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Yang X, Lyakurwa F, Xie H, Chen J, Li X, Qiao X, Cai X. Different binding mechanisms of neutral and anionic poly-/perfluorinated chemicals to human transthyretin revealed by In silico models. CHEMOSPHERE 2017; 182:574-583. [PMID: 28525871 DOI: 10.1016/j.chemosphere.2017.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/02/2017] [Accepted: 05/03/2017] [Indexed: 06/07/2023]
Abstract
Chemical forms-dependent binding interactions between phenolic compounds and human transthyretin (hTTR) have been elaborated previously. However, it is not known whether the binding interactions between ionizable halogenated alphatic compounds and hTTR also have the same manner. In this study, poly-/perfluorinated chemicals (PFCs) were selected as model compounds and molecular dynamic simulation was performed to investigate the binding mechanisms between PFCs and hTTR. Results show the binding interactions between the halogenated aliphatic compounds and hTTR are related to the chemical forms. The ionized groups of PFCs can form electrostatic interactions with the -NH+3 groups of Lys 15 residues in hTTR and form hydrogen bonds with the residues of hTTR. By analyzing the molecular orbital energies of PFCs, we also found that the anionic groups (nucleophile) in PFCs could form electron donor - acceptor interactions with the -NH+3 groups (electrophile) in Lys 15. The aforementioned orientational interactions make the ionized groups of the PFCs point toward the entry port of the binding site. The roles of fluorine atoms in the binding interactions were also explored. The fluorine atoms can influence the binding interactions via inductive effects. Appropriate molecular descriptors were selected to characterize these interactions, and two quantitative structure-activity relationship models were developed.
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Affiliation(s)
- Xianhai Yang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China; Nanjing Institute of Environmental Science, Ministry of Environmental Protection, Nanjing 210042, China
| | - Felichesmi Lyakurwa
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Hongbin Xie
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xianliang Qiao
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xiyun Cai
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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13
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Zhang J, Begum A, Brännström K, Grundström C, Iakovleva I, Olofsson A, Sauer-Eriksson AE, Andersson PL. Structure-Based Virtual Screening Protocol for in Silico Identification of Potential Thyroid Disrupting Chemicals Targeting Transthyretin. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:11984-11993. [PMID: 27668830 DOI: 10.1021/acs.est.6b02771] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Thyroid disruption by xenobiotics is associated with a broad spectrum of severe adverse outcomes. One possible molecular target of thyroid hormone disrupting chemicals (THDCs) is transthyretin (TTR), a thyroid hormone transporter in vertebrates. To better understand the interactions between TTR and THDCs, we determined the crystallographic structures of human TTR in complex with perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and 2,2',4,4'-tetrahydroxybenzophenone (BP2). The molecular interactions between the ligands and TTR were further characterized using molecular dynamics simulations. A structure-based virtual screening (VS) protocol was developed with the intention of providing an efficient tool for the discovery of novel TTR-binders from the Tox21 inventory. Among the 192 predicted binders, 12 representatives were selected, and their TTR binding affinities were studied with isothermal titration calorimetry, of which seven compounds had binding affinities between 0.26 and 100 μM. To elucidate structural details in their binding to TTR, crystal structures were determined of TTR in complex with four of the identified compounds including 2,6-dinitro-p-cresol, bisphenol S, clonixin, and triclopyr. The compounds were found to bind in the TTR hormone binding sites as predicted. Our results show that the developed VS protocol is able to successfully identify potential THDCs, and we suggest that it can be used to propose THDCs for future toxicological evaluations.
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Affiliation(s)
- Jin Zhang
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - Afshan Begum
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - Kristoffer Brännström
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - Christin Grundström
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - Irina Iakovleva
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - Anders Olofsson
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - A Elisabeth Sauer-Eriksson
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
| | - Patrik L Andersson
- Department of Chemistry and ‡Department of Medical Biochemistry and Biophysics, Umeå University , SE-901 87 Umeå, Sweden
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Jamal S, Arora S, Scaria V. Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers. PLoS One 2016; 11:e0083032. [PMID: 27622288 PMCID: PMC5021286 DOI: 10.1371/journal.pone.0083032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 10/30/2013] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries. METHODS In the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules. RESULTS We show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.
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Affiliation(s)
- Salma Jamal
- CSIR Open Source Drug Discovery Unit (CSIR-OSDD), Anusandhan Bhawan, Delhi, India
| | - Sonam Arora
- Delhi Technological University, Delhi, India
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- * E-mail:
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Yang X, Liu H, Liu J, Li F, Li X, Shi L, Chen J. Rational Selection of the 3D Structure of Biomacromolecules for Molecular Docking Studies on the Mechanism of Endocrine Disruptor Action. Chem Res Toxicol 2016; 29:1565-70. [PMID: 27556396 DOI: 10.1021/acs.chemrestox.6b00245] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Molecular modeling has become an essential tool in predicting and simulating endocrine disrupting effects of chemicals. A key prerequisite for successful application of molecular modeling lies in the correctness of 3D structure for biomacromolecules to be simulated. To date, there are several databases that can provide the experimentally-determined 3D structures. However, commonly, there are many challenges or disadvantageous factors, e.g., (a) lots of 3D structures for a given biomacromolecular target in the protein database; (b) the quality variability for those structures; (c) belonging to different species; (d) mutant amino acid residue in key positions, and so on. Once an inappropriate 3D structure of a target biomacromolecule was selected in molecular modeling, the accuracy and scientific nature of the modeling results could be inevitably affected. In this article, based on literature survey and an analysis of the 3D structure characterization of biomacromolecular targets belonging to the endocrine system in protein databases, six principles were proposed to guide the selection of the appropriate 3D structure of biomacromolecules. The principles include considering the species diversity, the mechanism of action, whether there are mutant amino acid residues, whether the number of protein chains is correct, the degree of structural similarity between the ligand in 3D structure and the target compounds, and other factors, e.g., the experimental pH conditions of the structure determined process and resolution.
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Affiliation(s)
- Xianhai Yang
- Nanjing Institute of Environmental Science , Ministry of Environmental Protection, Nanjing 210042, China
| | - Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology , Nanjing 210094, China
| | - Jining Liu
- Nanjing Institute of Environmental Science , Ministry of Environmental Protection, Nanjing 210042, China
| | - Fei Li
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences , Yantai 264003, China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology , Dalian 116024, China
| | - Lili Shi
- Nanjing Institute of Environmental Science , Ministry of Environmental Protection, Nanjing 210042, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology , Dalian 116024, China
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Zhang J, Li Y, Gupta AA, Nam K, Andersson PL. Identification and Molecular Interaction Studies of Thyroid Hormone Receptor Disruptors among Household Dust Contaminants. Chem Res Toxicol 2016; 29:1345-54. [DOI: 10.1021/acs.chemrestox.6b00171] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Jin Zhang
- Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
| | - Yaozong Li
- Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
| | - Arun A. Gupta
- Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
| | - Kwangho Nam
- Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
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17
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18
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Kar S, Roy K. Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping. Mol Inform 2013; 32:693-705. [DOI: 10.1002/minf.201300018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Accepted: 04/17/2013] [Indexed: 11/12/2022]
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19
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Zou X, Zhou X, Lin Z, Deng Z, Yin D. A docking-based receptor library of antibiotics and its novel application in predicting chronic mixture toxicity for environmental risk assessment. ENVIRONMENTAL MONITORING AND ASSESSMENT 2013; 185:4513-4527. [PMID: 23143826 DOI: 10.1007/s10661-012-2885-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2011] [Accepted: 09/11/2012] [Indexed: 06/01/2023]
Abstract
As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure-activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R (2)) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.
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Affiliation(s)
- Xiaoming Zou
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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Abstract
Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo efforts. From a molecular biophysical ansatz, we describe how 3D molecular modeling methods used to numerically evaluate the classical pair-wise potential at the chemical/biological interface can inform mechanism of action and the dose-response paradigm of modern toxicology. With an emphasis on molecular docking, 3D-QSAR and pharmacophore/toxicophore approaches, we demonstrate how these methods can be integrated with chemoinformatic and toxicogenomic efforts into a tiered computational toxicology workflow. We describe generalized protocols in which 3D computational molecular modeling is used to enhance our ability to predict and model the most relevant toxicokinetic, metabolic, and molecular toxicological endpoints, thereby accelerating the computational toxicology-driven basis of modern risk assessment while providing a starting point for rational sustainable molecular design.
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21
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Penchovsky R, Stoilova CC. Riboswitch-based antibacterial drug discovery using high-throughput screening methods. Expert Opin Drug Discov 2012; 8:65-82. [DOI: 10.1517/17460441.2013.740455] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Robert Penchovsky
- Sofia University “St. Kliment Ohridski”, Department of Genetics, Faculty of Biology,
8 Dragan Tzankov Blvd, 1164 Sofia, Bulgaria ;
| | - Cvetelina C Stoilova
- Sofia University “St. Kliment Ohridski”, Department of Genetics, Faculty of Biology,
8 Dragan Tzankov Blvd, 1164 Sofia, Bulgaria ;
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22
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Cohen Hubal EA, Richard A, Aylward L, Edwards S, Gallagher J, Goldsmith MR, Isukapalli S, Tornero-Velez R, Weber E, Kavlock R. Advancing exposure characterization for chemical evaluation and risk assessment. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2010; 13:299-313. [PMID: 20574904 DOI: 10.1080/10937404.2010.483947] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
A new generation of scientific tools has emerged to rapidly measure signals from cells, tissues, and organisms following exposure to chemicals. High-visibility efforts to apply these tools for efficient toxicity testing raise important research questions in exposure science. As vast quantities of data from high-throughput screening (HTS) in vitro toxicity assays become available, this new toxicity information must be translated to assess potential risks to human health from environmental exposures. Exposure information is required to link information on potential toxicity of environmental contaminants to real-world health outcomes. In the immediate term, tools are required to characterize and classify thousands of environmental chemicals in a rapid and efficient manner to prioritize testing and assess potential for risk to human health. Rapid risk assessment requires prioritization based on both hazard and exposure dimensions of the problem. To address these immediate needs within the context of longer term objectives for chemical evaluation and risk management, a translation framework is presented for incorporating toxicity and exposure information to inform public health decisions at both the individual and population levels. Examples of required exposure science contributions are presented with a focus on early advances in tools for modeling important links across the source-to-outcome paradigm. ExpoCast, a new U.S. Environmental Protection Agency (EPA) program aimed at developing novel approaches and metrics to screen and evaluate chemicals based on the potential for biologically relevant human exposures is introduced. The goal of ExpoCast is to advance characterization of exposure required to translate findings in computational toxicology to information that can be directly used to support exposure and risk assessment for decision making and improved public health.
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Affiliation(s)
- Elaine A Cohen Hubal
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA.
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Rabinowitz JR, Little SB, Laws SC, Goldsmith MR. Molecular Modeling for Screening Environmental Chemicals for Estrogenicity: Use of the Toxicant-Target Approach. Chem Res Toxicol 2009; 22:1594-602. [DOI: 10.1021/tx900135x] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- James R. Rabinowitz
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Stephen B. Little
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Susan C. Laws
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Michael-Rock Goldsmith
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
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24
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Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. ENVIRONMENTAL HEALTH PERSPECTIVES 2009; 117:1257-64. [PMID: 19672406 PMCID: PMC2721870 DOI: 10.1289/ehp.0800471] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2008] [Accepted: 04/03/2009] [Indexed: 05/05/2023]
Abstract
BACKGROUND Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. OBJECTIVE A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. METHODS AND RESULTS A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. CONCLUSIONS The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.
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Affiliation(s)
- Hao Zhu
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Lin Ye
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ann Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Alexander Golbraikh
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Ivan Rusyn
- Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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