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Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based
Fumihiro Yamane1, Kenji Ikemura2, Masayoshi Kondo1
1Department of Hospital Pharmacy, School of Pharmaceutical Sciences, Osaka University, Suita, Osaka, 565-0871, Japan.
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.
Insights
Researchers developed a machine learning model to find new inhibitors for human organic cation transporter 2 (hOCT2). Dequalinium, an antimicrobial drug, was identified as a potent hOCT2 inhibitor, potentially reducing platinum-based chemotherapy side effects.
Area of Science:
- Pharmacology
- Computational Chemistry
- Drug Discovery
Background:
- Human organic cation transporter 2 (hOCT2/SLC22A2) is crucial for transporting organic cations.
- hOCT2 activity is linked to adverse effects from platinum-based anti-cancer drugs.
- hOCT2 inhibitors could prevent chemotherapy-induced toxicity.
Purpose of the Study:
- To build a machine learning-based quantitative structure-activity relationship (QSAR) model for hOCT2 inhibitors.
- To identify novel hOCT2 inhibitors from FDA-approved drugs using the QSAR model.
Main Methods:
- Utilized the ChEMBL database to train a machine learning QSAR model for hOCT2 inhibitors.
- Screened FDA-approved drugs from the DrugBank database using the developed QSAR model.
- Validated candidate inhibitors through manual selection and in vitro assays.
Main Results:
- Identified 162 potential hOCT2 inhibitors among FDA-approved drugs.
- Dequalinium, an antimicrobial agent, was confirmed as a potent hOCT2 inhibitor (IC50 = 88.16 ± 7.14 nM).
- Dequalinium demonstrated concentration-dependent inhibition of hOCT2-mediated transport of cisplatin and oxaliplatin.
Conclusions:
- Successfully developed a novel machine learning-based QSAR model for hOCT2 inhibitors.
- Identified dequalinium as a novel, potent hOCT2 inhibitor among FDA-approved drugs.
- Dequalinium shows promise as a prophylactic agent against platinum-based anti-cancer drug toxicity.
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