Deciphering the polypharmacology of a traditional Chinese formula for MASLD: Network proximity to identifying...

Network proximity analysis quantifies the functional relationships between drug targets and disease modules, offering a promising approach for deciphering complex herbal formulas. However, the biological implications of the central components identified by this network remain unclear.

Taking Huangqi Guizhi Wuwu Decoction (HGWWD) and metabolic-dysfunction-associated steatotic liver disease (MASLD) as a paradigm, this study aimed to evaluate the utility of network proximity analysis in identifying pivotal components and to experimentally validate the pharmacological roles of these components.

MASLD and metabolic-dysfunction-associated steatohepatitis (MASH) mouse models were established using high-fat diet (HFD) and Gubra-Amylin NASH (GAN) diet, respectively. Metabolic phenotypes, liver pathology, and serum biochemical indices were assessed. Furthermore, integrated LC-MS/MS components, ADMET screening, network proximity analysis, molecular docking, and molecular dynamics (MD) simulations were performed to identify the core active ingredient.

HGWWD remarkably improved glucose tolerance, reduced fasting blood glucose levels, and attenuated lipid accumulation by inhibiting gluconeogenesis and lipogenesis through the activation of Akt phosphorylation and suppression of FoxO1, as well as the phosphorylation of CREB1 and expression of Fasn. Network proximity analysis identified catechin as the component with the closest functional proximity to the MASLD module. Catechin recapitulated the formula's effects by upregulating phosphorylated Akt and downregulating lipogenic genes. However, catechin monotherapy failed to alleviate steatosis and fibrosis in a mild-to-moderate MASH model.

Our study demonstrates that network proximity analysis can effectively pinpoint functional hub components within herbal formulas, with catechin serving as a notable example. The observed discrepancy between the hub component and the whole formula underscores the importance of synergistic multi-target modulation for achieving full therapeutic efficacy in complex diseases. This work provides a framework for interpreting network pharmacology data and rationalizing the composition of herbal medicines.