Drug repositioning for idiopathic epilepsy using gene expression signature data.

None Connectivity map GABA receptor Gene expression omnibus Gene-expression study Molecular docking study Voltage-gated calcium channel homology modelling

Journal

Bioinformation
ISSN: 0973-2063
Titre abrégé: Bioinformation
Pays: Singapore
ID NLM: 101258255

Informations de publication

Date de publication:
2022
Historique:
received: 02 09 2022
revised: 06 10 2022
accepted: 06 10 2022
medline: 1 9 2023
pubmed: 1 9 2023
entrez: 1 9 2023
Statut: epublish

Résumé

Epilepsy is one of the most common neurological disorders, affecting millions of patients with a substantial economic and human burden. About 30-40% of epileptic patients remain un-treated after the therapeutic option. Genetic or idiopathic epilepsy count about 40% of total epilepsy patients, showing a maximum percentage for drug-resistant epilepsy. Since the last century basic approach to understanding disease progression and drug discovery has been through the prism, exploring all possible causes and treatment options. Here we report about the gene expression-based drug repositioning study for epilepsy. Epilepsy gene expression data was retrieved from the Gene Expression Omnibus database, while drugs-associated gene expression data was retrieved from the Connectivity map (CMAP). The study predicted309 drug compounds which can alter genetic epilepsy-mediated gene signature using an in-house developed R-script. These compounds were docked against identified epilepsy targets- Voltage-gated sodium channel subunit α2 (Nav1.2); GABA receptor α1-β1; and Voltage-gated calcium channel α1G (Cav3.1)using Carbamazepine, Clonazepam, and Pregabalin as standard drugs, respectively. Twenty-one predicted drug compounds showed better binding affinity than respective standards against the selected epileptic receptors. Among these drug compounds, Ergocalciferol, Oxaprozin, Flunarizine, Triprolidine and Cyproheptadine have been previously reported for anti-epileptic activities and can be potential hits to target idiopathic epilepsy.

Identifiants

pubmed: 37654844
doi: 10.6026/97320630018845
pii: 97320630018845
pmc: PMC10465761
doi:

Types de publication

Journal Article

Langues

eng

Pagination

845-852

Informations de copyright

© 2022 Biomedical Informatics.

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Auteurs

Pawan Kumar (P)

Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001.

Deepak Sheokand (D)

Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001.

Annu Grewal (A)

Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001.

Vandana Saini (V)

Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001.

Ajit Kumar (A)

Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001.

Classifications MeSH