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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6631777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT nagaijunko qsarpredictionmodeltosearchforcompoundswithselectivecytotoxicityagainstoralcellcancer AT imamuramai qsarpredictionmodeltosearchforcompoundswithselectivecytotoxicityagainstoralcellcancer AT sakagamihiroshi qsarpredictionmodeltosearchforcompoundswithselectivecytotoxicityagainstoralcellcancer AT uesawayoshihiro qsarpredictionmodeltosearchforcompoundswithselectivecytotoxicityagainstoralcellcancer |