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QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of pr...

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Detalles Bibliográficos
Autores principales: Nagai, Junko, Imamura, Mai, Sakagami, Hiroshi, Uesawa, Yoshihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631777/
https://www.ncbi.nlm.nih.gov/pubmed/30939759
http://dx.doi.org/10.3390/medicines6020045
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author Nagai, Junko
Imamura, Mai
Sakagami, Hiroshi
Uesawa, Yoshihiro
author_facet Nagai, Junko
Imamura, Mai
Sakagami, Hiroshi
Uesawa, Yoshihiro
author_sort Nagai, Junko
collection PubMed
description Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.
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spelling pubmed-66317772019-08-19 QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer Nagai, Junko Imamura, Mai Sakagami, Hiroshi Uesawa, Yoshihiro Medicines (Basel) Article Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs. MDPI 2019-04-01 /pmc/articles/PMC6631777/ /pubmed/30939759 http://dx.doi.org/10.3390/medicines6020045 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nagai, Junko
Imamura, Mai
Sakagami, Hiroshi
Uesawa, Yoshihiro
QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer
title QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer
title_full QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer
title_fullStr QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer
title_full_unstemmed QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer
title_short QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer
title_sort qsar prediction model to search for compounds with selective cytotoxicity against oral cell cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631777/
https://www.ncbi.nlm.nih.gov/pubmed/30939759
http://dx.doi.org/10.3390/medicines6020045
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