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Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach

Authors :
Tomiko T. Oskotsky
Arohee Bhoja
Daniel Bunis
Brian L. Le
Alice S. Tang
Idit Kosti
Christine Li
Sahar Houshdaran
Sushmita Sen
Júlia Vallvé-Juanico
Wanxin Wang
Erin Arthurs
Arpita Govil
Lauren Mahoney
Lindsey Lang
Brice Gaudilliere
David K. Stevenson
Juan C. Irwin
Linda C. Giudice
Stacy L. McAllister
Marina Sirota
Source :
iScience, Vol 27, Iss 4, Pp 109388- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Summary: Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.

Subjects

Subjects :
Drugs
Transcriptomics
Science

Details

Language :
English
ISSN :
25890042
Volume :
27
Issue :
4
Database :
Directory of Open Access Journals
Journal :
iScience
Publication Type :
Academic Journal
Accession number :
edsdoj.7cb7fa771de148cfb0ba716157579323
Document Type :
article
Full Text :
https://doi.org/10.1016/j.isci.2024.109388