In-silico network-based analysis of drugs used against COVID-19: Human well-being study

Pharmacogenomic Landscape of Ivermectin and Selective Antioxidants: Exploring Gene Interplay in the Context of Long COVID

COVID-19 pandemic has caused widespread panic and fear among the global population. As such, repurposing drugs are being used as viable therapeutic options due to the limited effective treatments for Long COVID symptoms. Ivermectin is one of the emerging repurposed drugs that has been shown effective to have antiviral effects in clinical trials. In addition, antioxidant compounds are also gaining attention due to their capabilities of reducing inflammation and severity of symptoms. Due to the absence of knowledge in pharmacogenomics and modes of actions in the human body for these compounds, this study aims to provide a pharmacogenomic profile for the combination of ivermectin and six selected antioxidants (epigallocatechin gallate (EGCG), curcumin, sesamin, anthocyanins, quercetin, and N-acetylcysteine (NAC)) as potentially effective regimens for long COVID symptoms. Results showed that there were 12 interacting genes found among the ivermectin, 6 antioxidants, and COVID-19. For network pharmacology, the 12 common interacting genes/proteins had the highest associations with Pertussis pathway, AGE-RAGE signaling pathway in diabetic complications, and colorectal cancer in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Disease analyses also revealed that the top three relevant diseases with COVID-19 infections were diabetes mellitus, ischemia, reperfusion injury. We also identified 6 potential target microRNAs (miRNAs) of the 12 commonly curated genes used as molecular biomarkers for COVID-19 treatments. The established pharmacogenomic network, disease analyses, and identified miRNAs could facilitate developments of effective regimens for chronic sequelae of COVID-19 especially in this post-pandemic era. However, further studies and clinical trials are needed to substantiate the effectiveness and dosages for COVID-19 treatments.

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.