From nature to cancer therapy: Evaluating the Streptomyces clavuligerus secondary metabolites for potential protein kinase inhibitors

The study aimed to evaluate the antioxidant, protein kinase inhibitory (PKIs) potential, cytotoxicity activity of Streptomyces clavuligerus extract. DPPH assay revealed a robust free radical scavenging capacity (IC50 28.90 ± 0.24 µg/mL) of organic extract with a maximum inhibition percentage of 61 ± 1.04%. PKIs assay revealed the formation of a whitish bald zone by S. clavuligerus extracts which indicates the presence of PKIs. The cytotoxicity activity of organic fraction of extract through Sulforhodamine B assay on MCF-7, Hop-62, SiHa, and PC-3 cell lines demonstrated the lowest GI50 value against the MCF-7 cell line followed by the PC-3 cell line, showing potent growth inhibitory potential against human breast cancer and human prostate cancer cell line. HR-LCMS analysis identified multiple secondary metabolites from the organic and aqueous extracts of S. clavuligerus when incubated at 30°C under 200 rpm for 3 days. All the secondary metabolites were elucidated for their potential to inhibit RTKs by molecular docking, molecular dynamic simulation, MM/GBSA calculations, and free energy approach. It revealed the superior inhibitory potential of epirubicin (Epi) and dodecaprenyl phosphate-galacturonic acid (DPGA) against fibroblast growth factors receptor (FGFR). Epi also exhibited excellent inhibitory activity against the platelet-derived growth factor receptor (PDGFR), while DPGA effectively inhibited the vascular endothelial growth factor receptor. Additionally, the presence Epi in S. clavuligerus extract was validated through the HPLC technique. Thus, our findings highlight a superior inhibitory potential of Epi against FGFR and PDGFR RTKs than the FDA-approved drug.

The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors

Targeting the signaling pathway of the Vascular endothelial growth factor receptor-2 is a promising approach that has drawn attention in the quest to develop novel anti-cancer drugs and cardiovascular disease treatments. We construct a screening pipeline using machine learning classification integrated with similarity checks of approved drugs to find new inhibitors. The statistical metrics reveal that the random forest approach has slightly better performance. By further similarity screening against several approved drugs, two candidates are selected. Analysis of absorption, distribution, metabolism, excretion, and toxicity, along with molecular docking and dynamics are performed for the two candidates with regorafenib as a reference. The binding energies of molecule1, molecule2, and regorafenib are - 89.1, - 95.3, and - 87.4 (kJ/mol), respectively which suggest candidate compounds have strong binding to the target. Meanwhile, the median lethal dose and maximum tolerated dose for regorafenib, molecule1, and molecule2 are predicted to be 800, 1600, and 393 mg/kg, and 0.257, 0.527, and 0.428 log mg/kg/day, respectively. Also, the inhibitory activity of these compounds is predicted to be 7.23 and 7.31, which is comparable with the activity of pazopanib and sorafenib drugs. In light of these findings, the two compounds could be further investigated as potential candidates for anti-angiogenesis therapy.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.