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Quan W, Wang Y, Chen YH, Shao Q, Gong YZ, Hu JW, Liu WH, Wu ZJ, Wang J, Ma SB, Li XQ. Screening of rosmarinic acid from Salvia miltiorrhizae acting on the novel target TRPC1 based on the 'homology modelling-virtual screening-molecular docking-affinity assay-activity evaluation' method. PHARMACEUTICAL BIOLOGY 2023; 61:155-164. [PMID: 36604840 PMCID: PMC9828776 DOI: 10.1080/13880209.2022.2160769] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 11/14/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
CONTEXT Salvia miltiorrhizae Bunge (Lamiaceae) is a traditional Chinese medicine (TCM) for the treatment of 'thoracic obstruction'. Transient receptor potential canonical channel 1 (TRPC1) is a important target for myocardial injury treatment. OBJECTIVE This work screens the active component acting on TRPC1 from Salvia miltiorrhizae. MATERIALS AND METHODS TCM Systems Pharmacology Database and Analysis Platform (TCMSP) was used to retrieve Salvia miltiorrhiza compounds for preliminary screening by referring to Lipinski's rule of five. Then, the compound group was comprehensively scored by AutoDock Vina based on TRPC1 protein. Surface plasmon resonance (SPR) was used to determine the affinity of the optimal compound to TRPC1 protein. Western blot assay was carried out to observe the effect of the optimal compound on TRPC1 protein expression in HL-1 cells, and Fura-2/AM detection was carried out to observe the effect of the optimal compound on calcium influx in HEK293 cells. RESULTS Twenty compounds with relatively good characteristic parameters were determined from 202 compounds of Salvia miltiorrhiza. Rosmarinic acid (RosA) was obtained based on the molecular docking scoring function. RosA had a high binding affinity to TRPC1 protein (KD value = 1.27 µM). RosA (50 μM) could reduce the protein levels (417.1%) of TRPC1 after oxygen-glucose deprivation/reperfusion (OGD/R) in HL-1 cells and it could inhibit TRPC1-mediated Ca2+ influx injury (0.07 ΔRatio340/380) in HEK293 cells. DISCUSSION AND CONCLUSIONS We obtained the potential active component RosA acting on TRPC1 from Salvia miltiorrhizae, and we speculate that RosA may be a promising clinical candidate for myocardial injury therapy.
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Affiliation(s)
- Wei Quan
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
- Department of Pharmacology, School of Pharmacy, Air Force Medical University, Xi’an, China
| | - Yuan Wang
- Department of Neurosurgery, Wuhan No.1 Hospital, Wuhan, China
| | - Yu-han Chen
- Department of Pharmacy, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Qing Shao
- Xi’an Mental Health Center, School of Medicine, Xi’an Jiaotong University, Xi’an, China
| | - Yang-ze Gong
- Xi’an Mental Health Center, School of Medicine, Xi’an Jiaotong University, Xi’an, China
| | - Jie-wen Hu
- Xi’an Mental Health Center, School of Medicine, Xi’an Jiaotong University, Xi’an, China
| | - Wei-hai Liu
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Zi-jun Wu
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Jie Wang
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Shan-bo Ma
- Department of Pharmacy, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Xiao-qiang Li
- Department of Pharmacology, School of Pharmacy, Air Force Medical University, Xi’an, China
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Vignaux PA, Shriwas P, Revnew A, Agarwal G, Lane TR, McElroy CA, Ekins S. Human CYP2C19 Substrate and Inhibitor Characterization of Organophosphate Pesticides. Chem Res Toxicol 2023; 36:1451-1455. [PMID: 37650603 DOI: 10.1021/acs.chemrestox.3c00188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
CYP2C19 is an important enzyme for organophosphate pesticide (OPP) metabolism. Because the OPPs can be both substrates and inhibitors of CYP2C19, we screened 45 OPPs for their ability to inhibit the activity of this enzyme and investigated the role of CYP2C19 in the metabolism of 22 of these molecules. We identified several nanomolar inhibitors of CYP2C19 as well as determined that thions, in general, are more potent inhibitors than oxons. We also determined that thions are readily metabolized by CYP2C19, although we saw no relationship between IC50 values and intrinsic clearance rates. This study may have implications for mitigating the risk of OPP poisoning.
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Affiliation(s)
- Patricia A Vignaux
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina 27606, United States
| | - Pratik Shriwas
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, Ohio 43210, United States
| | - Andre Revnew
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, Ohio 43210, United States
| | - Garima Agarwal
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, Ohio 43210, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina 27606, United States
| | - Craig A McElroy
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, Ohio 43210, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina 27606, United States
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Hu S, Liu G, Zhang J, Yan J, Zhou H, Yan X. Linking electron ionization mass spectra of organic chemicals to toxicity endpoints through machine learning and experimentation. JOURNAL OF HAZARDOUS MATERIALS 2022; 431:128558. [PMID: 35228074 DOI: 10.1016/j.jhazmat.2022.128558] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/25/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Quantitative structure-activity relationship (QSAR) modeling has been widely used to predict the potential harm of chemicals, in which the prediction heavily relies on the accurate annotation of chemical structures. However, it is difficult to determine the accurate structure of an unknown compound in many cases, such as in complex water environments. Here, we solved the above problem by linking electron ionization mass spectra (EI-MS) of organic chemicals to toxicity endpoints through various machine learning methods. The proposed method was verified by predicting 50% growth inhibition of Tetrahymena pyriformis (T. pyriformis) and liver toxicity. The optimal model performance obtained an R2 > 0.7 or balanced accuracy > 0.72 for both the training set and test set. External experimentation further verified the application potential of our proposed method in the toxicity prediction of unknown chemicals. Feature importance analysis allowed us to identify critical spectral features that were responsible for chemical-induced toxicity. Our approach has the potential for toxicity prediction in such fields that it is difficult to determine accurate chemical structures.
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Affiliation(s)
- Song Hu
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China; School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China
| | - Guohong Liu
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Jin Zhang
- School of Public Health, Guizhou Medical University, Guiyang 550025, China
| | - Jiachen Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Hongyu Zhou
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Xiliang Yan
- Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.
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Park H, Brahma R, Shin J, Cho K. Prediction of human cytochrome
P450
inhibition using bio‐selectivity induced deep neural network. B KOREAN CHEM SOC 2021. [DOI: 10.1002/bkcs.12445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Hyejin Park
- AzothBio, Rm. DA724 Hyundai Knowledge Industry Center Hanam‐si Gyeonggi‐do Republic of Korea
| | - Rahul Brahma
- School of Systems Biomedical Science Soongsil University Seoul Republic of Korea
| | - Jae‐Min Shin
- AzothBio, Rm. DA724 Hyundai Knowledge Industry Center Hanam‐si Gyeonggi‐do Republic of Korea
| | - Kwang‐Hwi Cho
- School of Systems Biomedical Science Soongsil University Seoul Republic of Korea
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Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents. Oncotarget 2018; 9:16899-16916. [PMID: 29682193 PMCID: PMC5908294 DOI: 10.18632/oncotarget.24458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 02/01/2018] [Indexed: 12/21/2022] Open
Abstract
The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.
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Ghadari R. The role of human CYP2C8 in the metabolizing of montelukast-like compounds: a computational study. RESEARCH ON CHEMICAL INTERMEDIATES 2017. [DOI: 10.1007/s11164-017-2911-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Challenges in assignment of allosteric effects in cytochrome P450-catalyzed substrate oxidations to structural dynamics in the hemoprotein architecture. J Inorg Biochem 2017; 167:100-115. [DOI: 10.1016/j.jinorgbio.2016.11.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 10/17/2016] [Accepted: 11/22/2016] [Indexed: 12/19/2022]
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8
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Danielson ML, Hu B, Shen J, Desai PV. In Silico ADME Techniques Used in Early-Phase Drug Discovery. TRANSLATING MOLECULES INTO MEDICINES 2017. [DOI: 10.1007/978-3-319-50042-3_4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Hong H, Rua D, Sakkiah S, Selvaraj C, Ge W, Tong W. Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13100958. [PMID: 27690075 PMCID: PMC5086697 DOI: 10.3390/ijerph13100958] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 09/16/2016] [Accepted: 09/20/2016] [Indexed: 11/16/2022]
Abstract
Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The "active" ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.
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Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Diego Rua
- Division of Nonprescription Drug Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
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Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13070705. [PMID: 27420082 PMCID: PMC4962246 DOI: 10.3390/ijerph13070705] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 07/01/2016] [Accepted: 07/05/2016] [Indexed: 01/23/2023]
Abstract
Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.
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11
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Ye H, Luo H, Ng HW, Meehan J, Ge W, Tong W, Hong H. Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data. ENVIRONMENT INTERNATIONAL 2016; 89-90:81-92. [PMID: 26826365 DOI: 10.1016/j.envint.2016.01.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 01/08/2016] [Accepted: 01/13/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. OBJECTIVES We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. METHODS We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. RESULTS Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. CONCLUSIONS Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals.
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Affiliation(s)
- Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Heng Luo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Joe Meehan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
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12
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Hong H, Shen J, Ng HW, Sakkiah S, Ye H, Ge W, Gong P, Xiao W, Tong W. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:372. [PMID: 27023588 PMCID: PMC4847034 DOI: 10.3390/ijerph13040372] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 03/10/2016] [Accepted: 03/22/2016] [Indexed: 11/21/2022]
Abstract
Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.
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Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Jie Shen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA.
| | - Wenming Xiao
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
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Abstract
Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.
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Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
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Sevrioukova IF, Poulos TL. Current Approaches for Investigating and Predicting Cytochrome P450 3A4-Ligand Interactions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2015; 851:83-105. [PMID: 26002732 DOI: 10.1007/978-3-319-16009-2_3] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cytochrome P450 3A4 (CYP3A4) is the major and most important drug-metabolizing enzyme in humans that oxidizes and clears over a half of all administered pharmaceuticals. This is possible because CYP3A4 is promiscuous with respect to substrate binding and has the ability to catalyze diverse oxidative chemistries in addition to traditional hydroxylation reactions. Furthermore, CYP3A4 binds and oxidizes a number of substrates in a cooperative manner and can be both induced and inactivated by drugs. In vivo, CYP3A4 inhibition could lead to undesired drug-drug interactions and drug toxicity, a major reason for late-stage clinical failures and withdrawal of marketed pharmaceuticals. Owing to its central role in drug metabolism, many aspects of CYP3A4 catalysis have been extensively studied by various techniques. Here, we give an overview of experimental and theoretical methods currently used for investigation and prediction of CYP3A4-ligand interactions, a defining factor in drug metabolism, with an emphasis on the problems addressed and conclusions derived from the studies.
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Affiliation(s)
- Irina F Sevrioukova
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, 92697, USA,
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McPhail B, Tie Y, Hong H, Pearce BA, Schnackenberg LK, Ge W, Fuscoe JC, Tong W, Buzatu DA, Wilkes JG, Fowler BA, Demchuk E, Beger RD. Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes. Molecules 2012; 17:3383-406. [PMID: 22421792 PMCID: PMC6268752 DOI: 10.3390/molecules17033383] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Revised: 02/27/2012] [Accepted: 02/28/2012] [Indexed: 02/07/2023] Open
Abstract
An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutant—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.
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Affiliation(s)
- Brooks McPhail
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
| | - Yunfeng Tie
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
| | - Huixiao Hong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Bruce A. Pearce
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Laura K. Schnackenberg
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Weigong Ge
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - James C. Fuscoe
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Weida Tong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Dan A. Buzatu
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Jon G. Wilkes
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Bruce A. Fowler
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
| | - Eugene Demchuk
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
- Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506-9530, USA
- Author to whom correspondence should be addressed; ; Tel.: +1-770-488-3327; Fax: +1-404-248-4142
| | - Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
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