Multi-Dimensional Screening Strategy for Drug Repurposing with Statistical Framework-A New Road to Influenza Drug...

Area of Science:

• Computational chemistry
• Drug discovery
• Virology

Background:

• Influenza outbreaks pose a global health challenge, with existing neuraminidase inhibitors facing limitations due to drug resistance.
• Drug repurposing offers a cost-effective and time-efficient alternative for discovering new therapeutic agents.

Purpose of the Study:

• To develop and validate a multi-dimensional drug repurposing strategy for identifying novel influenza drug candidates.
• To screen the DrugBank database for compounds with potential antiviral activity against influenza.

Main Methods:

• Integrated ligand-, energy-, receptor cavity-, and shape-based pharmacophore algorithms using Schrödinger software.
• Screened the DrugBank database and evaluated model performance using enrichment calculations and ROC analysis.
• Validated promising hits through molecular docking, mutation analysis, and molecular dynamics simulations.

Main Results:

• The developed pharmacophore models demonstrated high accuracy in distinguishing active compounds from decoys.
• Alprostadil (DB00770) was identified as a potent inhibitor, exhibiting superior binding affinity to the influenza neuraminidase (NA) protein compared to existing drugs.
• PASS algorithm predicted significant antiviral activity for alprostadil, further supported by validation studies.

Conclusions:

• The integrative computational approach effectively identifies novel drug candidates for influenza.
• Alprostadil represents a promising repurposed drug candidate for influenza treatment, warranting further clinical investigation.
• This strategy significantly enhances the accuracy and reliability of drug discovery compared to existing methods.