Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model

Yamane, Fumihiro; Ikemura, Kenji; Kondo, Masayoshi; Ueno, Manami; Okuda, Masahiro

doi:https://doi.org/10.1038/s41598-024-79377-0

Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model

Yamane, Fumihiro; Ikemura, Kenji; Kondo, Masayoshi et al.

Scientific reports, 2025, 15(1), 2581

Number of Access:402（2025-07-26 21:56 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/100338

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Title	Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model
Creator	CreatorName Yamane, Fumihiro
	NRID 1000070513935 1000070513935 ORCID 0000-0003-1870-9571 0000-0003-1870-9571 researchmap permalink Kenji_Ikemura Kenji_Ikemura Scopus Author ID 36783115100 36783115100 CreatorName Ikemura, Kenji
	CreatorName Kondo, Masayoshi
	CreatorName Ueno, Manami
	NRID 1000070252426 1000070252426 researchmap permalink read0042639 read0042639 Scopus Author ID 56856467300 56856467300 CreatorName Okuda, Masahiro
Subject	Dequalinium
	Drug repositioning
	Machine learning
	Organic cation transporter 2
	Quantitative structure-activity relationship
Description	Yamane F., Ikemura K., Kondo M., et al. Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model. Scientific reports 15, 2581 (2025); https://doi.org/10.1038/s41598-024-79377-0.
Abstract	Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cations. Because hOCT2 is responsible for the development of adverse effects caused by platinum-based anti-cancer agents, drugs with OCT2 inhibitory effects may serve as prophylactic agents against the toxicity of platinum-based anti-cancer agents. In the present study, we established a machine learning-based quantitative structure-activity relationship (QSAR) model for hOCT2 inhibitors based on the public ChEMBL database and explored novel hOCT2 inhibitors among the FDA-approved drugs. Using our QSAR model, we identified 162 candidate hOCT2 inhibitors among the FDA-approved drugs registered in the DrugBank database. After manual selection and in vitro assays, we found that dequalinium, a quaternary ammonium cation antimicrobial agent, is a potent hOCT2 inhibitor (IC50 = 88.16 ± 7.14 nM). Moreover, dequalinium inhibited hOCT2-mediated transport of platinum anti-cancer agents (cisplatin and oxaliplatin) in a concentration-dependent manner. Our study is the first to demonstrate the construction of a novel machine learning-based QSAR model for hOCT2 inhibitors and identify a novel hOCT2 inhibitor among FDA-approved drugs using this model.
Publisher	Nature Portfolio
Source Title	Scientific reports
Volume (Issue)	15(1)
Page	2581
Date of Issued	2025-01-20
Language	English
Handle URL	https://hdl.handle.net/11094/100338
eISSN	20452322
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1038/s41598-024-79377-0
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	Scientific reports. 2025, 15(1), p. 2581
DC.title	Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model
DC.creator	Yamane, Fumihiro
	Ikemura, Kenji
	Kondo, Masayoshi
	Ueno, Manami
	Okuda, Masahiro
DC.publisher	Nature Portfolio
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-01-20
DC.identifier	https://doi.org/10.1038/s41598-024-79377-0
DC.subject	Dequalinium
	Drug repositioning
	Machine learning
	Organic cation transporter 2
	Quantitative structure-activity relationship
DC.description	Yamane F., Ikemura K., Kondo M., et al. Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model. Scientific reports 15, 2581 (2025); https://doi.org/10.1038/s41598-024-79377-0.
DCTERMS.abstract	Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cations. Because hOCT2 is responsible for the development of adverse effects caused by platinum-based anti-cancer agents, drugs with OCT2 inhibitory effects may serve as prophylactic agents against the toxicity of platinum-based anti-cancer agents. In the present study, we established a machine learning-based quantitative structure-activity relationship (QSAR) model for hOCT2 inhibitors based on the public ChEMBL database and explored novel hOCT2 inhibitors among the FDA-approved drugs. Using our QSAR model, we identified 162 candidate hOCT2 inhibitors among the FDA-approved drugs registered in the DrugBank database. After manual selection and in vitro assays, we found that dequalinium, a quaternary ammonium cation antimicrobial agent, is a potent hOCT2 inhibitor (IC50 = 88.16 ± 7.14 nM). Moreover, dequalinium inhibited hOCT2-mediated transport of platinum anti-cancer agents (cisplatin and oxaliplatin) in a concentration-dependent manner. Our study is the first to demonstrate the construction of a novel machine learning-based QSAR model for hOCT2 inhibitors and identify a novel hOCT2 inhibitor among FDA-approved drugs using this model.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
DC.format	100338
citation_title	Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model
citation_author	Yamane, Fumihiro
	Ikemura, Kenji
	Kondo, Masayoshi
	Ueno, Manami
	Okuda, Masahiro
citation_publisher	Nature Portfolio
citation_language	English
citation_date	2025-01-20
citation_journal_title	Scientific reports
citation_volume	15
citation_issue	1
citation_firstpage	2581
citation_doi	https://doi.org/10.1038/s41598-024-79377-0
citation_public_url	https://hdl.handle.net/11094/100338

Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model

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