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

アクセス数:399件（2025-07-26 11:50 集計）

固定URL: https://hdl.handle.net/11094/100338

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タイトル	Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model
著者	著者名 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 著者名 Ikemura, Kenji
	著者名 Kondo, Masayoshi
	著者名 Ueno, Manami
	NRID 1000070252426 1000070252426 researchmap permalink read0042639 read0042639 Scopus Author ID 56856467300 56856467300 著者名 Okuda, Masahiro
キーワード等	Dequalinium
	Drug repositioning
	Machine learning
	Organic cation transporter 2
	Quantitative structure-activity relationship
内容	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.
抄録	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.
出版者	Nature Portfolio
収録物名	Scientific reports
巻(号)	15(1)
ページ	2581
刊行年月	2025-01-20
言語	英語
ハンドルURL	https://hdl.handle.net/11094/100338
eISSN	20452322
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1038/s41598-024-79377-0
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
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	英語
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	英語
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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