Investigating Alternative Treatments for Dyslipidemia Using Bioactive Compounds Derived from Kiwifruit (Actinidia chinensis): A Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis

Introduction: Kiwi has many bioactive compounds that may improve blood lipid levels and help treat dyslipidemia, but its molecular mechanism is not fully understood. This study explores these mechanisms using pharmacological network analysis. materials and methods: Bioactive compounds of kiwi were obtained from the IMPPAT website, and molecular targets were identified using Swiss Target Prediction and PharmMapper. Genes associated with dyslipidemia were searched in the DISGENET database. Subsequently, an enrichment analysis was conducted, and a protein-protein interaction network was constructed. Hub genes were identified. Subsequently, a molecular docking analysis was performed, followed by a molecular dynamics simulation. Methods: Bioactive compounds of kiwi were obtained from the IMPPAT website, and molecular targets were identified using Swiss Target Prediction and PharmMapper. Genes associated with dyslipidemia were searched in the DISGENET database. Subsequently, an enrichment analysis was conducted, and a protein-protein interaction network was constructed. Hub genes were identified. Subsequently, a molecular docking analysis was performed, followed by a molecular dynamics simulation. results: Six bioactive compounds were identified in kiwifruit that exhibited good oral bioavailability and an adequate Quantitative Estimate of Drug-likeness. We identified thirty-five overlapping genes associated with dyslipidemia and kiwi fruit´s compound-related targets. According to functional enrichment analysis, the PPAR signaling pathway, lipid metabolism, and atherosclerosis were highlighted. It was identified that hub genes included ALB, PPARG, AKT1, MMP9, PPARA, HMGCR, GSK3B, NOS3, PPARD, ACE, JAK2, and DPP4, and their interactions with bioactive compounds were verified through molecular docking. Interactions among JAK2, ACE proteins, and kiwi´s bioactive compounds (quinic acid and citric acid) were spontaneously binding. A molecular dynamics simulation was performed on the top-scoring protein-bioactive compound complexes from the docking analysis to study their conformational stability, mobility, solvation, and compaction. Results: Six bioactive compounds in kiwifruit showed good oral bioavailability and drug-likeness. Thirty-five genes linked to dyslipidemia and kiwi's targets were identified. Enrichment analysis highlighted the PPAR signaling pathway, lipid metabolism, and atherosclerosis. Hub genes included ALB, PPARG, AKT1, MMP9, PPARA, HMGCR, GSK3B, NOS3, PPARD, ACE, JAK2, and DPP4, with their interactions verified by molecular docking. Interactions between JAK2, ACE, and bioactive compounds (quinic acid and citric acid) involved spontaneous binding. Molecular dynamics simulations assessed conformational stability, mobility, solvation, and compaction of top-scoring protein-compound complexes. Discussion: Bioactive compounds in kiwifruit can modulate dyslipidemia via PPAR pathways, JAK2, and ACE, supported by docking analyses and simulations. They show therapeutic potential, pending experimental validation. conclusion: Our findings inferred the molecular mechanism by which kiwi influences dyslipidemia. In silico analysis marks an initial step towards exploring compounds present in foods such as kiwi as potential alternative treatments for dyslipidemia. Conclusion: Our findings suggest how kiwi affects dyslipidemia. In silico analysis is a first step in exploring food compounds like kiwi as potential alternative treatments.