Unbiased combination screening on repurposed drugs reveals synergistic potential of copanlisib and cerivastatin against chemoresistant high-grade serous ovarian cancer

This study demonstrates the application of multiplex drug combination screening to identify effective synergistic therapies. Co-targeting PI3-kinase and HMG-CoA reductase could be repurposed as a potent combination to treat chemoresistant HGSOC.

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Publication Details

Journal
Journal of Ovarian Research
Published
2025-11-06
DOI
https://doi.org/10.1186/s13048-025-01828-7
Citations
1
Primary Topic
Cancer, Lipids, and Metabolism
Type
article
Field-Weighted Citation Impact
0.94

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article

Unbiased combination screening on repurposed drugs reveals synergistic potential of copanlisib and cerivastatin against chemoresistant high-grade serous ovarian cancer

Paul B. Mullan, Ahlam Ali, Fiona Furlong, Niamh E. Buckley et al.
1 citations
Journal of Ovarian Research
Cancer, Lipids, and Metabolism
0.94
article

Unbiased combination screening on repurposed drugs reveals synergistic potential of copanlisib and cerivastatin against chemoresistant high-grade serous ovarian cancer

Paul B. Mullan, Ahlam Ali, Fiona Furlong, Niamh E. Buckley, Yuling Sun, Syed Umbreen, Yangyang Wang, Benjamin Pepperrell
article en
1 citations

Abstract

This study demonstrates the application of multiplex drug combination screening to identify effective synergistic therapies. Co-targeting PI3-kinase and HMG-CoA reductase could be repurposed as a potent combination to treat chemoresistant HGSOC.

Journal of Ovarian ResearchVol. 18(1)
University of Wolverhampton (GB), Queen's University Belfast (GB), PLA Electronic Engineering Institute (CN), Cancer Trials Ireland (IE), Hefei Urban Planning & Design Institute (CN), The Northern Ireland Cancer Centre (GB), Anhui Institute of Architectural Research and Design (CN)
Queen's University Belfast, Higher Education Authority, Public Health Agency, Queen's University, China Scholarship Council
Openalex Percentile: Top 19%
Cancer, Lipids, and Metabolism
0.94
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