Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/32909
Title: Deciphering the missing links between Friedreich ataxia and multiple sclerosis for targeted drug development
Authors: Kwa, F
Anjomani Virmouni, S
Ramchunder, Z
Kendal, E
Xiao, J
Keywords: drug repurposing;Friedreich ataxia;inflammation;mitochondrial dysfunction;multiple sclerosis;oxidative stress;targeted therapy
Issue Date: 16-Mar-2026
Publisher: Elsevier
Citation: Kwa, F. et al. (2026) 'Deciphering the missing links between Friedreich ataxia and multiple sclerosis for targeted drug development', Drug Discovery Today, 33 (3), pp. 1–11. doi: 10.1016/j.drudis.2026.104644.
Abstract: Neurodegenerative diseases (NDDs), such as Friedreich ataxia (FA) and multiple sclerosis (MS), are marked by progressive neurodegeneration and heterogeneous pathologies. Despite distinct aetiologies, FA and MS appear to share some overlapping molecular mechanisms, including iron and lipid dysregulation, mitochondrial dysfunction, oxidative stress, and neuroinflammation. Recent research, including comparative transcriptomic analyses, offers valuable insights into shared disease pathways, with implications for potential biomarkers and therapeutic targets. In this review, we explore the shared pathological features and disease mechanisms in FA and MS, highlighting how delineating these shared pathways could inform early diagnostic strategies and support the development of targeted, mechanism-based interventions, including opportunities for drug repurposing.
Description: Data availability: No data was used for the research described in the article.
URI: https://bura.brunel.ac.uk/handle/2438/32909
DOI: https://doi.org/10.1016/j.drudis.2026.104644
ISSN: 1359-6446
Other Identifiers: ORCiD: Faith A. A. Kwa https://orcid.org/0000-0002-9702-0563
ORCiD: Sara Anjomani-Virmouni https://orcid.org/0000-0001-5831-780X
Appears in Collections:Department of Life Sciences Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC- ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).732.36 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons