Identification of Potent VEGF Inhibitors for the Clinical Treatment of Glioblastoma, A Virtual Screening Approach.

Vascular endothelial growth factor (VEGF) expression could be found in all glioblastomas. VEGF takes part in numerous changes including the endothelial cell proliferation, the vasculature of solid tumor: its survival invasion, and migration, chemotaxis of bone marrow-derived progenitor cells, vasodilation and vascular permeability. VEGF inhibition can be a smart therapeutic strategy because it is extremely specific and less toxic than cytotoxic therapy. To establish better inhibition of VEGF than the current inhibitors, present study approach is by molecular docking, virtual screening to illustrate the inhibitor with superior affinity against VEGF to have a cautious pharma profile. To retrieve the best established and high-affinity high affinity molecule, Molegro Virtual Docker software was executed. The high-affinity scoring compounds were subjected to further similarity search to retrieve the drugs with similar properties from pubchem database. The completion of virtual screening reveals that PubChem compound SCHEMBL1250485 (PubChem CID: 66965667) has the highest affinity. The study of the drug-likeness was verified using OSIRIS Property Explorer software which supported the virtual screened result. Further ADMET study and drug comparative study strongly prove the superiority of the new established inhibitor with lesser rerank score and toxicity. Overall, the new inhibitor has higher potential to stop the expression of VEGF in glioblastoma and positively can be further analysed through In vitro studies.

FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia.

Acute Myeloid Leukaemia (AML) is a blood cancer, which affects the red blood cells in the bone marrow. Of the possible proteins that are affected in AML, fms-like tyrosine kinase 3 (FLT3) has long been recognized as a potential therapeutic target as it affects the other signaling pathways and leads to a cascade of events. First-generation inhibitors sorafenib and midostaurin, as well as secondgeneration agents such as quizartinib and crenolanib are known. It is of interest to identify new compounds against FLT3 with improved activity using molecular docking and virtual screening. Molecular docking of existing inhibitors selected a top scoring bestestablished candidate Quizartinib having PubChem CID: 24889392. Similarity searching resulted in compound XGIQBUNWFCCMASUHFFFAOYSA-NPubChemCID: 44598530 which shows higher affinity scores. A comparative study of both the compounds using a drug-drug comparison, ADMET studies, boiled egg plot and pharmacophore parameters and properties confirmed the result and predicted the ligand to be an efficient inhibitor of FLT3.

Design of PD-L1 inhibitors for lung cancer.

The progression of lung cancer is associated with inactivation of programmed cell death protein 1, abbreviated as PD- 1 which regulates the suppression of the body's immune system by suppressing T- cell inflammatory activity and is responsible for preventing cancer cell growth. It is of interest to identify inhibitors for PD-L1 dimeric structure through molecular docking and virtual screening. The virtual screened compound XGIQBUNWFCCMAS-UHFFFAOYSA-N (PubChem CID: 127263272) displays a high affinity with the target protein. ADMET analysis and cytotoxicity studies further add weight to this compound as a potential inhibitor of PD-L1. The established compound BMS-202 still shows the high re-rank score, but the virtual screened drug possesses a better ADMET profile with a higher intestinal absorption value and lower toxicity.

Structural basis for the in vitro known acyl-depsipeptide 2 (ADEP2) inhibition to Clp 2 protease from Mycobacterium tuberculosis.

Inhibition of Mycobacterium tuberculosis Clp 2 protease has emerged as an attractive therapeutic option for treatment. Acyldepsipeptides (ADEPs) is known as an inhibitor for Clp 2 protease. Therefore, it is of interest to document its affinity, enzyme activity and ADME profiles. We report the predicted binding affinity of all known Clp 2 inhibitors like IDR-10001 and IDR-10011 against Clp2 protease using MolDock algorithm aided molecular docking. The predicted activity (using Molinspiration server) and ADMET properties (AdmetSAR server) were estimated for these compounds. This data suggest ADEP2 having improved binding features with Mtb Clp 2 having acceptable ADMET properties. This is in agreement with known in vitro data for ADEP2 inhibition with Mtb Clp 2 protease.