Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets.

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Identification of promising small-molecule inhibitors targeting STK17B for cancer therapeutics: molecular docking and molecular dynamics investigations.

Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells, leading to the formation of tumours. STK17B, a member of the DAPK family, has been implicated in various cancers and is considered a potential therapeutic target. However, no drug in the market has been approved for the treatment of STK17 B-associated cancer disease. This research aimed to identify direct inhibitors of STK17B using computational techniques. Ligand-based virtual screening and molecular docking were performed, resulting in the selection of three lead compounds (CID_135698391, CID_135453100, CID_136599608) with superior binding affinities compared to the reference compound dovitinib. While molecular docking simulation revealed specific interactions between the lead compounds and key amino acid residues at the binding pocket of STK17B, molecular dynamics simulations demonstrated that CID_135453100 and CID_136599608 exhibit stable conformations and comparable flexibility to dovitinib. However, CID_135698391 did not perform well using this metric as it displayed poor stability. Overall, small-molecule compounds CID_135453100 and CID_136599608 showed promising binding interactions and stability, suggesting their potential as direct inhibitors of STK17B. These findings could contribute to the exploration of novel therapeutic options targeting STK17B in cancer treatment.Communicated by Ramaswamy H. Sarma.

Target-based drug discovery, ADMET profiling and bioactivity studies of antibiotics as potential inhibitors of SARS-CoV-2 main protease (Mpro).

A recent outbreak of a new strain of Coronavirus (SARS-CoV-2) has become a global health burden, which has resulted in deaths. No proven drug has been found to effectively cure this fast-spreading infection, hence the need to explore old drugs with the known profile in tackling this pandemic. A computer-aided drug design approach involving virtual screening was used to obtain the binding scores and inhibiting efficiencies of previously known antibiotics against SARS-CoV-2 main protease (Mpro). The drug-likeness analysis of the repurposed drugs were done using the Molinspiration chemoinformatics tool, while the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was carried out using ADMET SAR-2 webserver. Other analyses performed include bioactivities of the repurposed drug as a probable anti-SARS-CoV-2 agent and oral bioavailability analyses among others. The results were compared with those of drugs currently involved in clinical trials in the ongoing pandemic. Although antibiotics have been speculated to be of no use in the treatment of viral infections, literature has emerged lately to reveal the antiviral potential and immune-boosting ability of antibiotics. This study identified Tarivid and Ciprofloxacin with binding affinities of - 8.3 kcal/mol and - 8.1 kcal/mol, respectively as significant inhibitors of SARS-CoV-2 (Mpro) with better pharmacokinetics, drug-likeness and oral bioavailability, bioactivity properties, ADMET properties and inhibitory strength compared to Remdesivir (- 7.6 kcal/mol) and Azithromycin (- 6.3 kcal/mol). These observations will provide insight for further research (clinical trial) in the cure and management of COVID-19.