Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model
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 inhibitor...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79377-0 |
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| author | Fumihiro Yamane Kenji Ikemura Masayoshi Kondo Manami Ueno Masahiro Okuda |
| author_facet | Fumihiro Yamane Kenji Ikemura Masayoshi Kondo Manami Ueno Masahiro Okuda |
| author_sort | Fumihiro Yamane |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6a540af88b749d3a6ee614b9476cba1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e6a540af88b749d3a6ee614b9476cba12025-01-26T12:30:14ZengNature PortfolioScientific Reports2045-23222025-01-011511810.1038/s41598-024-79377-0Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR modelFumihiro Yamane0Kenji Ikemura1Masayoshi Kondo2Manami Ueno3Masahiro Okuda4Department of Hospital Pharmacy, School of Pharmaceutical Sciences, Osaka UniversityDepartment of Pharmacy, Osaka University HospitalDepartment of Hospital Pharmacy, School of Pharmaceutical Sciences, Osaka UniversityDepartment of Hospital Pharmacy, School of Pharmaceutical Sciences, Osaka UniversityDepartment of Pharmacy, Osaka University HospitalAbstract 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.https://doi.org/10.1038/s41598-024-79377-0DequaliniumDrug repositioningMachine learningOrganic cation transporter 2Quantitative structure-activity relationship |
| spellingShingle | Fumihiro Yamane Kenji Ikemura Masayoshi Kondo Manami Ueno Masahiro Okuda Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model Scientific Reports Dequalinium Drug repositioning Machine learning Organic cation transporter 2 Quantitative structure-activity relationship |
| title | Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model |
| title_full | Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model |
| title_fullStr | Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model |
| title_full_unstemmed | Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model |
| title_short | Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model |
| title_sort | identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based qsar model |
| topic | Dequalinium Drug repositioning Machine learning Organic cation transporter 2 Quantitative structure-activity relationship |
| url | https://doi.org/10.1038/s41598-024-79377-0 |
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