Computational pharmacology-based molecular mechanism investigation of cinnamaldehyde intervention in nephrotic syndrome

Nephrotic syndrome (NS) is a common glomerular ailment caused by various factors and ranks as the second most frequently seen kidney disease. The study aims to investigate the potential therapeutic mechanism of cinnamaldehyde (CA) intervention in NS by utilizing computational pharmacology. Genes linked to NS were gathered from databases and then were used to construct a PPI network, of which the node importance values (Nim) were calculated utilizing an optimized algorithm. Functional enrichment analyses were performed to build a pathway network for NS. Afterward, CA's potential targets were acquired from the Venn diagram by intersecting NS-related genes and CA-related genes. The predicted targets and pathways of CA intervention in NS were identified using a mathematical algorithm that evaluated the disruption of NS pathways by CA, considering Nim, the number of pathways, and other variables. Molecular docking and cellular experiments were included in validation. By the way, the research collected 687 genes related to NS and 195 genes related to CA, which were used to identify 26 potential targets of CA in NS treatment. The disruption of 166 NS pathways by 26 CA targets was evaluated, showing that the Antifolate resistance and NOD-like receptor pathway exhibited the highest disturbance scores. Besides, key targets that were identified through the algorithm included IL1B, TNF, CASP8, and MAPK1, which were subsequently validated through molecular docking. Experimental results demonstrated that CA inhibited LPS-induced IL-1 beta, IL-6, and TNF-alpha levels in R264.7 cells and reduced p-38MAPK, p-ERK, and p-Caspase8 protein expression. This study proposes an algorithm for evaluating drug molecule perturbations on pathways. By employing this optimized model, potential key targets for CA in the treatment of NS are identified, with the anti-inflammatory effect potentially mediated through the Caspase8/MAPK pathway.