Withania somnifera commonly known as Ashwagandha in India is used in many herbal formulations to treat various cardiovascular diseases. The key metabolite of this plant, Withaferin A was analyzed for its molecular mechanism through docking studies on different targets of cardiovascular disease. Six receptor proteins associated with cardiovascular disease were selected and interaction studies were performed with Withaferin A using AutoDock Vina. CORINA was used to model the small molecules and HBAT to compute the hydrogen bonding. Among the six targets, β1- adrenergic receptors, HMG-CoA and Angiotensinogen-converting enzyme showed significant interaction with Withaferin A. Pharmacophore modeling was done using PharmaGist to understand the pharmacophoric potential of Withaferin A. Clustering of Withaferin A with different existing drug molecules for cardiovascular disease was performed with ChemMine based on structural similarity and physicochemical properties. The ability of natural active component, Withaferin A to interact with different receptors associated with cardiovascular disease was elucidated with various modeling techniques. These studies conclusively revealed Withaferin A as a potent lead compound against multiple targets associated with cardiovascular disease.

Pharmaceutics present in the environment are of growing concern for their potential consequences on human and ecosystem health. In long-term monitoring investigations of sewage and surface water samples, diclofenac was identified as one of the most important pharmaceutically active compounds. Pangasius sp. were exposed to determine the lethal concentration (LC50) of diclofenac. The histopathological studies in a fish hepatopancreas sample showed significant changes like nucleation, differentiation, and hepatocytes. The large lesions and infiltration in hepatocytes were distinctly seen due to the toxic effect of diclofenac. In HPLC analysis, the control fish liver tissue showed no significant changes, whereas the diclofenac-exposed fish liver tissue showed a significant peak with a quicker run time. The studies confirmed that the diclofenac compound affects hepatocytes to a noteworthy extent. Therefore, this calls for reduction of diclofenac usage and also to have a checkpoint especially for diclofenac in the pharmaceutical effluent released into the water bodies.

COVID-19 has become a global challenge as there are very few treatment options available. This has proved to impact several physiological implications like immunological injury, myocardial infarction, micro-thrombus formation, neurological complications and multi-organ dysfunction. A combination therapy or a systems pharmacology approach can be adopted to fight against COVID-19. Here, we have proposed withaferin A as a system pharmacophore employing molecular docking strategy using AutoDock Vina and utilising different bioinformatics tools like PharmMapper, STRING database and PANTHER Pathway enrichment analysis. Docking results show that withaferin A exhibits a significant binding affinity with P2Y12 receptor, vitamin D-binding protein and annexin A5, hence implying that it could play a role in anti-thrombosis. Protein–protein interaction network showed its importance in innate immune system. Results also show that this molecule may have significant potential to modulate T cell activation too. Text mining results showed association of STAT3 with withaferin A. Our studies propose that withaferin A might also conquer the cytokine storm via STAT3. This study concludes that two strong targets of withaferin A, i.e. vitamin D-binding protein and STAT3, have been identified and that withaferin A can be used as a system pharmacophore for drug development in order to combat COVID-associated complicacies.

In recent years, modern medicine has grown exponentially. Many drugs are being developed and reused for different diseases. Although these drugs help cure these diseases, there are some side effects on the human body. The most affected organ in the human body. Is the liver these side effects in the liver were studied using marine toxicity studies using several species of marine organisms. This study aimed to investigate the toxicity mechanisms of xenobiotics using a network pharmacological approach. Xenobiotics are chemicals that are foreign to an organism and exposure to them can cause adverse health effects. Network pharmacology is a systems-level approach that integrates information about interactions between drugs, proteins, and biological pathways. The study involved using publicly available data to construct a human xenobiotic-protein interaction network using sites such as PubChem, PharmMapper, the KEGG/PANTHER pathway, and the Cytoscape software. The network was analysed to identify key proteins and pathways involved in xenobiotic toxicity. The study also explores the potential for repurposing existing drugs to minimize xenobiotic toxicity. Study results reveal several key targets and pathways involved in xenobiotic toxicity, including oxidative stress, inflammation, and apoptosis. The study also identified several existing drugs that could potentially be repurposed to minimize xenobiotic toxicity. Taken together, this study provides insight into the mechanisms underlying xenobiotictoxicity and highlights the potential for using network pharmacological approaches toidentify novel therapies for the treatment of xenobiotic diseases.

Amyloid deposits formed by misfolding and aggregation of human islet amyloid polypeptide (hIAPP) are one of the key pathophysiological features of type 2 diabetes mellitus (T2DM) and have been associated with the loss of function and viability of the pancreatic β-cells. The molecular processes by which hIAPP induces cytotoxicity in these cells are not well understood. To the best of our knowledge, this is the first report describing findings from the combined analysis of Affymetrix microarray and high-throughput sequencing (HTS) Gene Expression Omnibus (GEO) datasets of h IAPP -transgenic (Tg) mice islets. In brief, using GEO data, we compared in silico the pancreatic islets obtained from h IAPP -Tg and wild-type mice. Affymetrix microarray datasets (GSE84423, GSE85380, and GSE94672) and HTS datasets (GSE135276 and GSE148809) were chosen. Weighted gene coexpression network analysis was performed using GSE135276 to identify the coexpressed gene networks and establish a correlation pattern between gene modules and h IAPP overexpression under hyperglycemic conditions. Subsequently, we analyzed differential gene expression with the remaining datasets. Network analysis was performed to identify hub genes and the associated pathways using Cytoscape. Key findings from the present study include identification of seven hub genes, namely, Ins2 , Agt , Jun , Fos , CD44 , Igf1 , and Ppar-γ , significantly involved in the process(es) of insulin synthesis and secretion, development of insulin resistance, oxidative stress, inflammation, mitophagy, and apoptosis. In conclusion, we propose that these hub genes can help explain T2DM pathogenesis and can be potentially utilized to develop therapeutic interventions targeting hIAPP for clinical management of T2DM.