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  • Title: Multi-Dimensional Screening Strategy for Drug Repurposing with Statistical Framework-A New Road to Influenza Drug discovery.
    Author: Rohini K, Ramanathan K, Shanthi V.
    Journal: Cell Biochem Biophys; 2019 Dec; 77(4):319-333. PubMed ID: 31559538.
    Abstract:
    Influenza virus is known for its intermittent outbreaks affecting billions of people worldwide. Several neuraminidase inhibitors have been used in practice to overcome this situation. However, advent of new resistant mutants has limited its clinical utilization. In the recent years drug repurposing technique has attained the limelight as it is cost effective and reduces the time consumed for drug discovery. Here, we present multi-dimensional repurposing strategy that integrates the results of ligand-, energy-, receptor cavity, and shape-based pharmacophore algorithm to effectively identify novel drug candidate for influenza. The pharmacophore hypotheses were generated by utilizing the PHASE module of Schrödinger. The generated hypotheses such as AADP, AADDD, and DDRRNH, respectively, for ligand-, e-pharmacophore and receptor cavity based approach alongside shape of oseltamivir were successfully utilized to screen the DrugBank database. Subsequently, these models were evaluated for their differentiating ability using Enrichment calculation. Receiver operating curve and enrichment factors from the analysis indicate that the models possess better capability to screen actives from decoy set of molecules. Eventually, the hits retrieved from different hypotheses were subjected to molecular docking using Glide module of Schrödinger Suite. The results of different algorithms were then combined to eliminate false positive hits and to demonstrate reliable prediction performance than existing approaches. Of note, Pearson's correlation coefficients were calculated to examine the extent of correlation between the glide score and IC50 values. Further, the interaction profile, pharmacokinetic, and pharmacodynamics properties were analyzed for the hit compounds. The results from our analysis showed that alprostadil (DB00770) exhibits better binding affinity toward NA protein than the existing drug molecules. The biological activity of the hit was also predicted using PASS algorithm that renders the antiviral activity of the compound. Further, the results were validated using mutation analysis and molecular dynamic simulation studies. Indeed, this integrative filtering is able to exceed accuracy of other state-of-the-art methods for the drug discovery.
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