Albendazole repurposing on VEGFR-2 for possible anticancer application: In-silico analysis.

Drug repurposing is the finding new activity of the existing drug. Recently, Albendazole's well-known antihelmintic has got the attention of an anticancer drug. Plausible evidence of the interaction of Albendazole with one of the types of tyrosine kinase protein receptor, vascular endothelial growth factor receptor-2 (VEGFR-2) is still not well understood. Inhibition of the VEGFR-2 receptor can prevent tumor growth. The current study investigated the interaction of Albendazole with VEGFR-2.It was found that the said interaction exhibited potent binding energy ΔG = -7.12 kcal/mol, inhibitory concentration (Ki) = 6.04 µM, and as positive control comparison with standard drug (42Q1170A) showed ΔG = -12.35 kcal/mol and Ki = 881 µM. The key residue Asp1046 was formed involved hydrogen bonding with Albendazole. The molecular dynamics simulation study revealed the stable trajectory of the VEGFR-2 receptor with Albendazole bound complex having significant high free energy of binding as calculated from Molecular Mechanics Generalized Born and Surface Area study ΔG = -42.07±2.4 kcal/mol. The binding energy is significantly high for greater stability of the complex. Principal component analysis of molecular docking trajectories exhibited ordered motion at higher modes, implying a high degree of VEGFR-2 and Albendazole complex stability as seen with the standard drug 42Q. Therefore, the current work suggests the role of Albendazole as a potent angiogenesis inhibitor as ascertained by its potential interaction with VEGFR-2. The findings of research will aid in the future development of Albendazole in anticancer therapy.

Wheat ergot fungus-derived and modified drug for inhibition of intracranial aneurysm rupture due to dysfunction of TLR-4 receptor in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a form of dementia that strikes elderly people more frequently than it does younger people. The cognitive skills and memory of Alzheimer's sufferers continue to deteriorate over time. Recent studies have shown that patients with AD have greater amounts of inflammatory markers in their bodies, which suggests that inflammation occurs early on in the progression of the disease. There is a possibility that Aß oligomers and fibrils can be recognised by TLRs, in addition to the microglial receptors CD14, CD36, and CD47. When Aß binds to either CD36 or TLR4, it sets off a chain reaction of inflammatory chemokines and cytokines that ultimately results in neurodegeneration. Diabetes and Alzheimer's disease have both been recently related to TLR4. The activation of TLR4 has been connected to a variety of clinical difficulties that are associated with diabetes, in addition to the internal environment of the body and the microenvironment of the brain. TLR4 inhibitors have been shown in clinical investigations to not only lessen the likelihood of getting sick but also to increase the average longevity. RESULT: In this work we used molecular docking and molecular dynamics modelling to investigate the effectiveness of FDA-approved antidiabetic plant derived drugs in combating the TLR4 receptor. Molecular docking experiments were used to make a prediction regarding the most important interactions involving 2-Bromoergocryptine Mesylate. With a binding affinity of -8.26 kcal/mol, it stood out from the other candidates as the one with the greatest potential. To verify the interaction pattern that takes place between 2-Bromoergocryptine Mesylate and the TLR4 receptor, a molecular dynamic simulation was run at a time scale of 150 nanoseconds. Because of this, 2-Bromoergocryptine Mesylate was able to make substantial contact with the active site, which led to increased structural stability during the process of the complex's dynamic development. CONCLUSION: As a result of this, the results of our research may be relevant for future research into the efficacy of 2-bromoergocryptine mesylate as a potential lead treatment for TLR4 receptors in intracranial aneurysm rupture in AD.

Recognizing novel drugs against Keap1 in Alzheimer's disease using machine learning grounded computational studies.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the world, affecting an estimated 50 million individuals. The nerve cells become impaired and die due to the formation of amyloid-beta (Aß) plaques and neurofibrillary tangles (NFTs). Dementia is one of the most common symptoms seen in people with AD. Genes, lifestyle, mitochondrial dysfunction, oxidative stress, obesity, infections, and head injuries are some of the factors that can contribute to the development and progression of AD. There are just a few FDA-approved treatments without side effects in the market, and their efficacy is restricted due to their narrow target in the etiology of AD. Therefore, our aim is to identify a safe and potent treatment for Alzheimer's disease. We chose the ursolic acid (UA) and its similar compounds as a compounds' library. And the ChEMBL database was adopted to obtain the active and inactive chemicals against Keap1. The best Quantitative structure-activity relationship (QSAR) model was created by evaluating standard machine learning techniques, and the best model has the lowest RMSE and greatest R2 (Random Forest Regressor). We chose pIC50 of 6.5 as threshold, where the top five potent medicines (DB06841, DB04310, DB11784, DB12730, and DB12677) with the highest predicted pIC50 (7.091184, 6.900866, 6.800155, 6.768965, and 6.756439) based on QSAR analysis. Furthermore, the top five medicines utilize as ligand molecules were docked in Keap1's binding region. The structural stability of the nominated medications was then evaluated using molecular dynamics simulations, RMSD, RMSF, Rg, and hydrogen bonding. All models are stable at 20 ns during simulation, with no major fluctuations observed. Finally, the top five medications are shown as prospective inhibitors of Keap1 and are the most promising to battle AD.