Abstract

Targeting pro-inflammatory cytokines and their production is found to be of therapeutic benefit for the regulation of inflammation in various chronic autoimmune diseases. Our continued efforts to discover small molecular-weight pro-inflammatory cytokine inhibitors resulted in identifying a novel natural lignan molecule named polonilignan, isolated from the culture broth extract of an endophytic fungus Penicillium polonicum. An in silico study (molecular docking, ADME predictions, binding free energy calculation and molecular dynamics simulation) of the polonilignan over the pro-inflammatory cytokines proteins TNF-α, IL-6 and IL-1β was performed using Schrodinger LLC software to understand the binding interactions, drug-like properties, and stability of the ligand-protein complex. Further, in-vitro testing of inhibition of TNF-α, IL-6 and IL-1β by polonilignan was carried out using ELISA and RT-PCR on LPS-induced RAW 264.7 cell lines along with the testing of nitrite production effect (Griess assay) and cytotoxicity (MTT) analysis. Under the computational study, polonilignan revealed good docking scores, binding interactions, and stability under MDS and desirable in silico ADME results over the proteins TNF-α, IL-1β and IL-6. Poloniligan showed significant inhibition of IL-1β, IL-6 and TNF-α with IC50 values of 2.01 μM, 6.59 μM and 42.10 μM, respectively. Also, it reduced the translocation of the NF-κB subunit p65 to the nucleus (confocal microscopy). The mRNA expression levels of pro-inflammatory markers IL-1β, TNF-α and IL-6 levels were lowered significantly (p < .001) by the compound, and the diminution was higher with IL-1β. Further, the lignan was non-cytotoxic and effective in attenuating nitrite release (IC50 48.56 μM). Thus, polonilignan has been identified as a new pan-cytokine and NO inhibitor, it is recommended to optimise a method for the synthesis of this small molecular weight lignan and explore its pharmacokinetic characteristics, toxicity and therapeutic effect under various chronic inflammatory disease models.

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