Mechanistic insights into Da-Fang-Feng decoction for osteoarthritis: an integrated strategy combining machine learning, molecular docking, and molecular dynamics simulation
Abstract
Background. Osteoarthritis (OA) involves complex inflammatory and oxidative stress mechanisms that require multi-target therapeutic strategies. Da-Fang-Feng Decoction (DFFD), a classical herbal formulation, has shown potential anti-inflammatory and antioxidant activity, but its molecular basis remains unclear.
Methods. A multi-omics strategy combining network pharmacology, machine learning, molecular docking, and molecular dynamics (MD) simulation was used to elucidate the pharmacological mechanism of DFFD against OA. Candidate targets were identified through network analysis and prioritized using multiple machine learning algorithms. Pharmacokinetic evaluation (ADME) was performed for major active components. Key compound–target interactions were validated by molecular docking, MD simulation, and cellular experiments.
Results. Five representative compounds—naringenin, quercetin, kaempferol, baicalein, and wogonin—were identified as bioactive constituents of DFFD. Among 125 overlapping targets, AKT1 and IL1B emerged as critical nodes regulating inflammatory and oxidative pathways. All five compounds exhibited favorable pharmacokinetic characteristics and stable binding to AKT1 and IL1B in both docking and MD analyses. Cellular validation confirmed that DFFD reduced AKT1 and IL1B expression and mitigated reactive oxygen species (ROS) accumulation, demonstrating anti-inflammatory and antioxidant effects.
Conclusions. This integrative computational and experimental investigation reveals that DFFD alleviates OA by modulating AKT1/IL1B signaling and oxidative stress. The findings provide mechanistic insight and a scientific foundation for the clinical application of DFFD in OA management.