ABSTRACT
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84â µM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.
Subject(s)
Antineoplastic Agents , Cell Proliferation , Deep Learning , Drug Discovery , Protein Kinase Inhibitors , Vascular Endothelial Growth Factor Receptor-2 , Vascular Endothelial Growth Factor Receptor-2/antagonists & inhibitors , Vascular Endothelial Growth Factor Receptor-2/metabolism , Humans , Protein Kinase Inhibitors/pharmacology , Protein Kinase Inhibitors/chemistry , Protein Kinase Inhibitors/chemical synthesis , Cell Proliferation/drug effects , Antineoplastic Agents/pharmacology , Antineoplastic Agents/chemistry , Antineoplastic Agents/chemical synthesis , Drug Screening Assays, Antitumor , Structure-Activity Relationship , Cell Line, Tumor , Molecular Structure , Molecular Docking Simulation , Dose-Response Relationship, DrugABSTRACT
Cumulative escalation in antibiotic-resistant pathogens necessitates the quest for novel antimicrobial agents, as current options continue to diminish bacterial resistance. Herein, we report the synthesis of di-heterocyclic benzazole structures (12-19) and their in vitro evaluation for some biological activities. Compounds 16 and 17 demonstrated potent antibacterial activity (MIC = 7.81 µg/mL) against Staphylococcus aureus, along with significant anti-biofilm activity. Noteworthy is the capability of Compound 17 to inhibit biofilm formation by at least 50% at sub-MIC (3.90 µg/mL) concentration. Furthermore, both compounds exhibited the potential to inhibit preformed biofilm by at least 50% at the MIC concentration (7.81 µg/mL). Additionally, Compounds 16 and 17 were examined for cytotoxic effects in HFF-1 cells, using the MTT method, and screened for binding interactions within the active site of S. aureus DNA gyrase using in silico molecular docking technique, employing AutoDock 4.2.6 and Schrödinger Glidse programs. Overall, our findings highlight Compounds 16 and 17 as promising scaffolds warranting further optimization for the development of effective antibacterial and anti-biofilm agents.