Aminoglutethimide for COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARSCoV 2) is a coronavirus variation responsible for COVID19, the respiratory disease that triggered the COVID19 pandemic. The primary aim of our study is to elucidate the complex network of interactions between SARS CoV 2, tuberculosis, and lung cancer employing a bioinformatics and network biology approach. Lung cancer is the leading cause of significant illness and death connected to cancer worldwide. Tuberculosis (TB) is a prevalent medical condition induced by the Mycobacterium bacteria. It mostly affects the lungs but may also have an influence on other areas of the body. Coronavirus disease (COVID19) causes a risk of respiratory complications between lung cancer and tuberculosis. SARSCoV 2 impacts the lower respiratory system and causes severe pneumonia, which can significantly increase the mortality risk in individuals with lung cancer. We conducted transcriptome analysis to determine molecular biomarkers and common pathways in lung cancer, TB, and COVID19, which provide understanding into the association of SARSCoV 2 to lung cancer and tuberculosis. Based on the compatible RNA-seq data, our research employed GREIN and NCBI's Gene Expression Omnibus (GEO) to perform differential gene expression analysis. Our study exploited three RNAseq datasets from the Gene Expression Omnibus (GEO) GSE171110, GSE89403, and GSE81089 to identify distinct relationships between differentially expressed genes (DEGs) in SARSCoV 2, tuberculosis, and lung cancer. We identified 30 common genes among SARSCoV 2, tuberculosis, and lung cancer (25 upregulated genes and 5 downregulated genes). We analyzed the following five databases: WikiPathway, KEGG, Bio Carta, Elsevier Pathway and Reactome. Using Cytohubba's MCC and Degree methods, We determined the top 15 hub genes resulting from the PPI interaction. These hub genes can serve as potential biomarkers, leading to novel treatment strategies for disorders under investigation. Transcription factors (TFs) and microRNAs (miRNAs) were identified as the molecules that control the differentially expressed genes (DEGs) of interest, either during transcription or after transcription. We identified 35 prospective therapeutic compounds that form significant differentially expressed genes (DEGs) in SARSCoV 2, lung cancer, and tuberculosis, which could potentially serve as medications. We hypothesized that the potential medications that emerged from this investigation may have therapeutic benefits.

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.