A Network Pharmacology and molecular docking-based study exploring the pharmacokinetics, safety and mechanism of action of Polyscias fulva bioactive compounds against uterine fibroids

Abstract

Abstract Uterine Fibroids (UF) also known as uterine leiomyomas are a significant reproductive health challenge among the female population, globally. Apart from surgery which has several complications, many available pharmacological therapeutic options reduce symptoms rather than being curative. The use of Polyscias fulva for the management of UF by Traditionally in Uganda implored the scientific validation process through network pharmacology and molecular docking approaches. Using scholarly literature search, known bioactive compounds of Polyscias fulva were retrieved from various databases. The SwissADME platform was used to evaluate drug likeliness and pharmacokinetic parameters of the compounds. The potential target genes of the compounds were predicted using the Swiss Target Prediction Database. Human genes associated with UF were obtained from GeneCards and OMIM databases. The interaction between the compounds and UF genes was established through protein–protein interaction, gene ontology, and KEGG pathway enrichment analysis. The binding affinities between the bioactive compounds of Polyscias fulva and the retrieved UF hub targets were determined using AutoDock tools. Here we show that Five Polyscias fulva bioactive compounds: pinoresinol, lichexanthone, methyl atarate, β-sitosterol and Cauloside A exhibited drug likeness properties with moderate safety profiles. β -sitosterol demonstrated stronger binding affinity with five human uterine fibroids targets i.e. HIF1A (-9.21 kcal/mol), ESR1 (-8.31kcal/mol), EGFR (-9.75kcal/mol), CASP3 (-7.13kcal/mol) and CCND1(-5.74kcal/mol) while the other four compounds strongly bound to three targets (HIF1A, ESR1, EGFR). In conclusion, Polyscias fulva contains bioactive compounds with potential anti-proliferative activity against UF with promising pharmacokinetic properties and safety profiles using computational predictive models.