Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis
Thomas Papikinos1, Marios G Krokidis2, Aris Vrahatis2
1Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece. c20papi@ionio.gr.
Advances in Experimental Medicine and Biology
|July 24, 2023
Summary
Drug repurposing for Amyotrophic Lateral Sclerosis (ALS) identified potential therapeutics by analyzing gene expression data. These compounds may reverse disease signatures and warrant further experimental investigation.
Area of Science:
- Neuroscience
- Genomics
- Pharmacology
Background:
- Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with limited treatment options.
- Drug repurposing offers a promising strategy to identify new therapies for ALS.
Purpose of the Study:
- To identify existing drugs that could be repurposed for ALS treatment.
- To leverage gene expression signatures and computational databases for drug discovery.
Main Methods:
- Utilized Connectivity Map (CMAP) and L1000CDS2 databases containing gene expression profiles.
- Obtained ALS gene expression signature from muscle biopsy specimens.
- Queried databases with differentially expressed genes to find compounds reversing the ALS signature.
Main Results:
- Identified potent compounds and compound classes predicted to reverse ALS-associated gene expression patterns.
- Clustered identified compounds based on chemical structure and known treatments.
- Predicted that most compounds affect pathways crucial to ALS pathogenesis.
Conclusions:
- Identified promising drug candidates for ALS through computational analysis.
- These candidates are suitable for further in vitro and in vivo experimental validation.
- This study supports drug repurposing as a viable strategy for ALS therapeutics.
