Abstract
BACKGROUND AND PURPOSE: Lumbar disc herniation (LDH) significantly impacts individuals, particularly those aged 40-45. Traditional Chinese Medicine (TCM) formulations such as Taohong Siwu Decoction (TSD), Yaotong Jizheng Decoction (YJD), and Panlong Qi Tablet (PQT) are widely used for treatment. This study introduces dose-weighted network pharmacology, a novel approach that incorporates drug dosage as a quantitative factor into network analysis to evaluate better and compare the therapeutic potential of TCM formulations. METHODS: This study combines drug dosage with the PPI network to propose a theoretical algorithm for comparing the therapeutic efficacy of different traditional Chinese medicine formulations. The VIKOR method was used to assess the importance of therapeutic targets, with weights assigned based on both drug and disease perspectives. TSD, YJD, and PQT were evaluated in animal experiments, and the algorithm's feasibility was validated through GO and KEGG pathway analysis, Thermal Hyperalgesia Test, H&E staining, Western blotting (WB), RT-PCR, and ELISA assays. RESULTS: The computational model indicated that YJD and PQT had higher predicted efficacy compared to TSD. These predictions were confirmed in animal studies, where YJD demonstrated the greatest reduction in thermal hyperalgesia and the most significant decrease in inflammatory markers, surpassing both TSD and PQT. GO and KEGG pathway analyses highlighted key pathways related to oxidative stress and inflammation, providing mechanistic insights into the effectiveness of the treatments. CONCLUSION: Incorporating dosage as a reference factor into network pharmacology research proved feasible and effective, emphasizing the importance of precise dosage control in TCM formulations for treating LDH. The new algorithm provided reliable predictions, demonstrating its potential to enhance the design and evaluation of TCM formulations. Future improvements, such as establishing a target acceptance rate database, could further refine the algorithm, expanding its application in personalized medicine and targeted therapy.