Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study

© 2018 The Royal Society of Chemistry. Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predispo...

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Main Authors: Naravut Suvannang, Likit Preeyanon, Aijaz Ahmad Malik, Nalini Schaduangrat, Watshara Shoombuatong, Apilak Worachartcheewan, Tanawut Tantimongcolwat, Chanin Nantasenamat
Other Authors: Mahidol University
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Published: 2019
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/45441
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spelling th-mahidol.454412019-08-23T17:51:55Z Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study Naravut Suvannang Likit Preeyanon Aijaz Ahmad Malik Nalini Schaduangrat Watshara Shoombuatong Apilak Worachartcheewan Tanawut Tantimongcolwat Chanin Nantasenamat Mahidol University Chemical Engineering Chemistry © 2018 The Royal Society of Chemistry. Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure-activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC50). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (RTr2 = 0.94, QCV2 = 0.73, and QExt2 = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα. 2019-08-23T10:46:12Z 2019-08-23T10:46:12Z 2018-01-01 Article RSC Advances. Vol.8, No.21 (2018), 11344-11356 10.1039/c7ra10979b 20462069 2-s2.0-85044628264 https://repository.li.mahidol.ac.th/handle/123456789/45441 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044628264&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Chemical Engineering
Chemistry
spellingShingle Chemical Engineering
Chemistry
Naravut Suvannang
Likit Preeyanon
Aijaz Ahmad Malik
Nalini Schaduangrat
Watshara Shoombuatong
Apilak Worachartcheewan
Tanawut Tantimongcolwat
Chanin Nantasenamat
Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
description © 2018 The Royal Society of Chemistry. Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure-activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC50). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (RTr2 = 0.94, QCV2 = 0.73, and QExt2 = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.
author2 Mahidol University
author_facet Mahidol University
Naravut Suvannang
Likit Preeyanon
Aijaz Ahmad Malik
Nalini Schaduangrat
Watshara Shoombuatong
Apilak Worachartcheewan
Tanawut Tantimongcolwat
Chanin Nantasenamat
format Article
author Naravut Suvannang
Likit Preeyanon
Aijaz Ahmad Malik
Nalini Schaduangrat
Watshara Shoombuatong
Apilak Worachartcheewan
Tanawut Tantimongcolwat
Chanin Nantasenamat
author_sort Naravut Suvannang
title Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
title_short Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
title_full Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
title_fullStr Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
title_full_unstemmed Probing the origin of estrogen receptor alpha inhibition: Via large-scale QSAR study
title_sort probing the origin of estrogen receptor alpha inhibition: via large-scale qsar study
publishDate 2019
url https://repository.li.mahidol.ac.th/handle/123456789/45441
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