Penicinoline

Six undescribed 4-quinolone alkaloids, including four racemic mixtures, (±)-oxypenicinolines A-D, and two related ones, penicinolines F and G, together with seven known analogues, were isolated from the mangrove-derived fungus Penicillium steckii SCSIO 41025 (Trichocomaceae). The racemates were separated by HPLC using chiral columns. Their structures including absolute configurations were elucidated by extensive spectroscopic analysis, electronic circular dichroism (ECD) experiments, and single-crystal X-ray diffraction analysis. Structurally, (±)-oxypenicinolines A-D shared with an unusual 6/6/5/5 tetracyclic system incorporating a rare tetrahydro-pyrrolyl moiety. A plausible biosynthetic pathway for pyrrolyl 4-quinolone alkaloids is proposed. (±)-oxypenicinoline A and quinolactacide displayed α-glucosidase inhibitory activity with the IC50 values of 317.8 and 365.9 μΜ, respectively, which were more potent than that of acarbose (461.0 μM). Additionally, penicinoline and penicinoline E showed weak inhibitions toward acetylcholinesterase (AChE).

One new pyrrolyl 4-quinolinone alkaloid, penicinoline E (1), together with three known deriverites, methyl-penicinoline (2), penicinoline (3), and quinolactacide (4), were isolated from the marine-derived fungus Penicillium sp. ghq208. The structures of these isolated compounds were elucidated by spectroscopic methods. Compounds (2, 3) exhibited moderate cytotoxicities against the HepG2 cell line with IC(50) values of 11.3 and 13.2 µM, respectively.

Non-small cell lung cancer (NSCLC) is the most dominating and lethal type of lung cancer triggering more than 1.3 million deaths per year. The most effective line of treatment against NSCLC is to target epidermal growth factor receptor (EGFR) activating mutation. The present study aims to identify the novel anti-lung cancer compounds form nature against EGFR 696-1022 T790M by using in silico approaches. A library of 419 compounds from several natural resources was subjected to pre-screen through machine learning model using Random Forest classifier resulting 63 screened molecules with active potential. These molecules were further screened by molecular docking against the active site of EGFR 696-1022 T790M protein using AutoDock Vina followed by rescoring using X-Score. As a result 4 compounds were finally screened namely Granulatimide, Danorubicin, Penicinoline and Austocystin D with lowest binding energy which were -6.5 kcal/mol, -6.1 kcal/mol, -6.3 kcal/mol and -7.1 kcal/mol respectively. The drug likeness of the screened compounds was evaluated using FaF-Drug3 server. Finally toxicity of the hit compounds was predicted in cell line using the CLC-Pred server where their cytotoxic ability against various lung cancer cell lines was confirmed. We have shown 4 potential compounds, which could be further exploited as efficient drug candidates against lung cancer.