Molecular docking analysis of mefluhybenamine with lung cancer targets

Abstract

Lung cancer is the most prevalent type of cancer worldwide, with 2.21 million cases and 1.80 million fatalities in 2020. The main factor influencing lung cancer is smoking, and the most common form of lung cancer, non-small cell lung cancer (NSCLC), accounts for around 80% of instances compared to small cell carcinoma, and about 75% of patients are already in an advanced stage when they are detected. Despite significant early detection and therapy improvements, the five-year survival rate for NSCLC is not encouraging. Therefore, it is essential to look into the molecular origins of non-small cell lung cancer to develop more effective therapeutic strategies—the binding affinities and energy landscape with the proteins. Cyclin Dependent kinase 2 (CDK2) and Insulin-like Growth Factor 1 Receptor (IGF1) were more substantial and sustained in lung cancer that was chosen as the two primary target proteins in this study. We screened the entire Drug Bank-prepared library of 1,55,888 compounds and found (2R,3R)-7-(Methylsulfonyl)-3-(2,4,5-trifluorophenyl)-1,2,3,4-tetrahydropyrido[1,2-a] benzimidazol-2-aminium (Mefluhybenamine) to be a significant inhibitor. Mefluhybenamine showed strong hydrogen bonding and other bonding topologies, such as van der Waals force, in its high docking scores of -6.168 Kcal/mol and -5.26 Kcal/mol, and ADMET results showed excellent bioavailability, remarkable solubility, no side effects, and toxicity. The molecular dynamic simulation confirmed the compound's stability and interaction pattern for 100 ns in an SPC water medium with the slightest deviation and fluctuation. Data shows that Mefluhybenamine is a potential candidate. However, validation of the compound is essential.

Cancer-wide in silico analyses using differentially expressed genes demonstrate the functions and clinical relevance of JAG, DLL, and NOTCH

“…The diversity of the composition and expression of DEGs for each gene and tumor influenced the different trends in DEG enrichment for each gene. Moreover, the Notch signal interacts with other oncogenic signals, and they also affect the regulation of the expression of target genes of the Notch signal [ 31 , 32 ]. Since the signaling mechanisms activated in each tumor are different, the aspect of their DEG enrichment is observed in various trends [ 33 , 34 ].…”

“…There were no differences in all gene expression before and after anti-cancer therapy in SCC, but JAG1-, JAG2-, and NOTCH2-DEG enrichment significantly increased or decreased after anti-cancer therapy (S17F Fig in S1 File ). DEG enrichment can be significantly changed depending on activating or inhibiting other signals that interact with the Notch signaling after anti-cancer therapy [ 31 , 32 ].…”

Identification of Novel STAT3 Dimerization Inhibitor Through Structure-Based Virtual Screening for Cancer Management

“…Computational drug discovery is an effective strategy for speeding up and streamlining the drug discovery and development pipeline, demonstrating efficacy in increasing efficiency and cost-effectiveness [21][22][23]. In docking analysis, if a chemical has a lower binding affinity than the control, it indicates that the molecule is more active [24,25]. Interestingly, hits (ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537) had higher negative binding affinity values relative to the control (STX-0119), indicating that these hits resulted in tighter binding and could be used as STAT3 inhibitors.…”