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Article: In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining

TitleIn-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining
Authors
Keywordsdrug-drug interaction
herbal bioinformatics
in-silico toxicity prediction
ligand-based virtual screening
synergism
triple-negative breast cancer
Issue Date2-Sep-2022
PublisherMDPI
Citation
International Journal of Molecular Sciences, 2022, v. 23, n. 17 How to Cite?
Abstract

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as “Acute Effects”, “NIOSH Toxicity Data”, “Interactions”, “Hepatotoxicity”, “Carcinogenicity”, “Symptoms”, and “Human Toxicity Values” sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.


Persistent Identifierhttp://hdl.handle.net/10722/340793
ISSN
2023 Impact Factor: 4.9
2023 SCImago Journal Rankings: 1.179
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Feng-
dc.contributor.authorGanesan, Kumar-
dc.contributor.authorLi, Yan-
dc.contributor.authorChen, Jianping-
dc.date.accessioned2024-03-11T10:47:10Z-
dc.date.available2024-03-11T10:47:10Z-
dc.date.issued2022-09-02-
dc.identifier.citationInternational Journal of Molecular Sciences, 2022, v. 23, n. 17-
dc.identifier.issn1661-6596-
dc.identifier.urihttp://hdl.handle.net/10722/340793-
dc.description.abstract<p>Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as “Acute Effects”, “NIOSH Toxicity Data”, “Interactions”, “Hepatotoxicity”, “Carcinogenicity”, “Symptoms”, and “Human Toxicity Values” sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.</p>-
dc.languageeng-
dc.publisherMDPI-
dc.relation.ispartofInternational Journal of Molecular Sciences-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectdrug-drug interaction-
dc.subjectherbal bioinformatics-
dc.subjectin-silico toxicity prediction-
dc.subjectligand-based virtual screening-
dc.subjectsynergism-
dc.subjecttriple-negative breast cancer-
dc.titleIn-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining-
dc.typeArticle-
dc.identifier.doi10.3390/ijms231710056-
dc.identifier.scopuseid_2-s2.0-85137581454-
dc.identifier.hkuros341364-
dc.identifier.volume23-
dc.identifier.issue17-
dc.identifier.eissn1422-0067-
dc.identifier.isiWOS:000852801400001-
dc.identifier.issnl1422-0067-

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