INTEGRATED IN-SILICO AND MACHINE LEARNING – BASED PREDICTION OF OTOPROTECTIVE ACTIVITY OF Mentha spicata
Year: 2026
Type: article
Abstract: Aminoglycoside antibiotics such as neomycin are widely used clinically but cause irreversible sensorineural hearing loss through oxidative stress, mitochondrial dysfunction, and apoptosis of cochlear hair cells. This study investigated the otoprotective potential of Mentha spicata phytoconstituents, particularly rosmarinic acid, against neomycin-induced ototoxicity using an integrated in silico approach. The ethanolic leaf extract was evaluated for antioxidant activity by DPPH free radical scavenging assay. Network pharmacology analysis was performed using SwissTargetPrediction, GeneCards, Venny 2.1, STRING, and Cytoscape to identify common drug–disease targets and enriched pathways. Molecular docking was carried out using AutoDock to assess binding affinity, followed by SwissADME profiling and AI-based prediction. The extract exhibited dose-dependent antioxidant activity, with scavenging rising from 19% to 69% across 1–50 mg/ml. Nine common targets, including CASP3, CASP8, TNF, PTGS2, and PPARG, were identified, with KEGG enrichment highlighting apoptosis, TNF, IL17, and Toll-like receptor signaling pathways.
Language: English
Subfield: Sensory Systems
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7224019894