Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5

Thomas R. Lane; Ana C. Puhl; Patricia A. Vignaux; Keith R. Pennypacker; Sean Ekins

doi:10.1016/j.molpha.2024.100010

Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5

Thomas R. Lane, Ana C. Puhl, Patricia A. Vignaux, Keith R. Pennypacker, Sean Ekins

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Chemokine receptors CCR3, CCR4, and CCR5 are G protein–coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration–approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R² = 0.8), CCR4 (ROC, 0.98; R² = 0.57), and CCR5 (ROC, 0.96; R² = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration–approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC₅₀, 0.7 μM) and CCR4 (IC₅₀, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC₅₀, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein–coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. Significance Statement: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein–coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.

Original language	English
Article number	100010
Journal	Molecular Pharmacology
Volume	107
Issue number	1
DOIs	https://doi.org/10.1016/j.molpha.2024.100010
State	Published - Jan 2025

Bibliographical note

Publisher Copyright:
© 2024 American Society for Pharmacology and Experimental Therapeutics

Funding

We greatly acknowledge earlier input of our colleagues Eni Minerali, Daniel H. Foil, Kimberley M. Zorn. Dr Mohamed Nasr is thanked for assistance with obtaining the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database. We would also like to thank Mr Ean Spielvogel and Drs Shuntai Zhou and Kristina De Paris from the Department of Microbiology and Immunology at University of North Carolina Chapel Hill for their contributions (HIV inhibition testing). Provisional patents have been filed on this work. Research reported in this publication was supported by the National Institute of Environmental Health Sciences (award R44ES031038-02) of the National Institutes of Health (award R44GM122196-02A1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors declare that all the data supporting the findings of this study are available within the paper and its Supplemental Material. Participated in research design: Pennypacker, Ekins. Conducted experiments: Lane, Puhl. Performed data analysis: Lane, Puhl, Vignaux. Wrote or contributed to the writing of the manuscript: Lane, Ekins. \u201CResearch reported in this publication was supported by the National Institute of Environmental Health Sciences (award R44ES031038-02) of the National Institutes of Health (award R44GM122196-02A1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u201D SE, TRL, ACP and PAV are employees of CPI. No other authors have an actual or perceived conflict of interest with the contents of this article

Funders	Funder number
Department of Microbiology and Immunology at University of North Carolina
Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases
Mr Ean Spielvogel
National Institutes of Health/National Institute of Environmental Health Sciences	R44ES031038-02
National Institutes of Health/National Institute of Environmental Health Sciences
National Institutes of Health (NIH)	R44GM122196-02A1
National Institutes of Health (NIH)

Keywords

CCR3
CCR4
CCR5
Lapatinib
Machine learning
Repurposing

ASJC Scopus subject areas

Molecular Medicine
Pharmacology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.molpha.2024.100010

Cite this

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title = "Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5",

abstract = "Chemokine receptors CCR3, CCR4, and CCR5 are G protein–coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration–approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R2 = 0.8), CCR4 (ROC, 0.98; R2 = 0.57), and CCR5 (ROC, 0.96; R2 = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration–approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC50, 0.7 μM) and CCR4 (IC50, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC50, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein–coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. Significance Statement: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein–coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.",

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author = "Lane, \{Thomas R.\} and Puhl, \{Ana C.\} and Vignaux, \{Patricia A.\} and Pennypacker, \{Keith R.\} and Sean Ekins",

note = "Publisher Copyright: {\textcopyright} 2024 American Society for Pharmacology and Experimental Therapeutics",

year = "2025",

month = jan,

doi = "10.1016/j.molpha.2024.100010",

language = "English",

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AU - Ekins, Sean

PY - 2025/1

Y1 - 2025/1

N2 - Chemokine receptors CCR3, CCR4, and CCR5 are G protein–coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration–approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R2 = 0.8), CCR4 (ROC, 0.98; R2 = 0.57), and CCR5 (ROC, 0.96; R2 = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration–approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC50, 0.7 μM) and CCR4 (IC50, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC50, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein–coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. Significance Statement: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein–coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.

AB - Chemokine receptors CCR3, CCR4, and CCR5 are G protein–coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration–approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R2 = 0.8), CCR4 (ROC, 0.98; R2 = 0.57), and CCR5 (ROC, 0.96; R2 = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration–approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC50, 0.7 μM) and CCR4 (IC50, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC50, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein–coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. Significance Statement: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein–coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.

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Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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