Articulating target-mining techniques to disinter Alzheimer's specific targets for drug repurposing.

BACKGROUND AND OBJECTIVES: Alzheimer's Disease (AD), an extremely progressive neurodegenerative disorder is an amalgamation of numerous intricate pathological networks. This century old disease is still an unmet medical condition owing to the modest efficacy of existing therapeutic agents in antagonizing the multi-targeted pathological pathways underlying AD. Given the paucity in AD specific drugs, fabricating comprehensive research strategies to envision disease specific targets to channelize and expedite drug discovery are mandated. However, the dwindling approval rates and stringent regulatory constraints concerning the approval of a new chemical entity is daunting the pharmaceutical industries from effectuating de novo research. To bridge the existing gaps in AD drug research, a promising contemporary way out could be drug repurposing. This drug repurposing investigation is intended to envisage AD specific targets and create drug libraries pertinent to the shortlisted targets via a series of avant-garde bioinformatics and computational strategies. METHODS: Transcriptomic analysis of three AD specific datasets viz., GSE122063, GSE15222 and GSE5281 revealed significant Differentially Expressed Genes (DEGs) and subsequent Protein-Protein Interactions (PPI) network analysis captured crucial AD targets. Later, homology model was constructed through I-TASSER for a shortlisted target protein which lacked X-ray crystallographic structure and the built protein model was validated by molecular dynamic simulations. Further, drug library was created for the shortlisted target based on structural and side effect similarity with respective standard drugs. Finally, molecular docking, binding energy calculations and molecular dynamics studies were carried out to unravel the interactions exhibited by drugs from the created library with amino acids in active binding pocket of RGS4. RESULTS: SST and RGS4 were shortlisted as potentially significant AD specific targets, however, the less explored target RGS4 was considered for further sequential analysis. Homology model constructed for RGS4 displayed best quality when validated through Ramachandran plot and ERRAT plot. Subsequent docking and molecular dynamics studies showcased substantial affinity demonstrated by three drugs viz., Ziprasidone, Melfoquine and Metaxalone from the created drug libraries, towards RGS4. CONCLUSION: This virtual analysis forecasted the repurposable potential of Ziprasidone, Melfoquine and Metaxalone against AD based on their affinity towards RGS4, a key AD-specific target.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor.

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC50 of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

Untangling huge literature to disinter genetic underpinnings of Alzheimer's Disease: A systematic review and meta-analysis.

Drug discovery for Alzheimer's Disease (AD) is channeled towards unravelling key disease specific drug targets/genes to predict promising therapeutic candidates. Though enormous literature on AD genetics is available, there exists dearth in data pertinent to drug targets and crucial pathological pathways intertwined in disease progression. Further, the research findings revealing genetic associations failed to demonstrate consistency across different studies. This scenario prompted us to initiate a systematic review and meta-analysis with an aim of unearthing significant genetic hallmarks of AD. Initially, a Boolean search strategy was developed to retrieve case-control studies from PubMed, Cochrane, ProQuest, Europe PMC, grey literature and HuGE navigator. Subsequently, certain inclusion and exclusion criteria were framed to shortlist the relevant studies. These studies were later critically appraised using New Castle Ottawa Scale and Q-Genie followed by data extraction. Later, meta-analysis was performed only for those Single Nucleotide Polymorphisms (SNPs) which were evaluated in at least two different ethnicities from two different reports. Among, 204,351 studies retrieved, 820 met our eligibility criteria and 117 were processed for systematic review after critical appraisal. Ultimately, meta-analysis was performed for 23 SNPs associated with 15 genes which revealed significant associations of rs3865444 (CD33), rs7561528 (BIN1) and rs1801133 (MTHFR) with AD risk.