Discovery of Active Compounds in Qigui Jiangzhi Formula for Metabolic Dysfunction-Associated Steatotic Liver Disease by a Multidimensional Phenotypic Analysis.

BACKGROUND

The incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) is rising steadily. Clinically, the Qigui Jiangzhi Formula (QGJZF) has been proven to be capable of effectively improving the conditions of patients with hyperlipidemia and those with metabolic-associated fatty liver disease by regulating the pathways of glucose and lipid metabolism.

PURPOSE: This study aims to establish a strategy based on multidimensional phenotypic characteristics to screen for active compounds in QGJZF that can improve abnormal glucose and lipid metabolism.

METHODS

A multidimensional phenotypic method for liver cells based on high-content imaging has been developed. This method can correlate biological processes with morphological data, such as glucose metabolism, lipid metabolism, energy metabolism, and oxidative stress in MASLD. The chemical-phenotypic correlation analysis has identified the active compounds in QGJZF by activity score and cluster score. These scores were obtained by combining the mass spectrometry ion data of each fraction of QGJZF with the multidimensional phenotypic data. The data of active compounds were combined with network pharmacology for analysis, thereby elucidating the mechanism of QGJZF in improving MASLD.

RESULTS

Among the 199 cell phenotypic parameters obtained by multidimensional phenotypic method, 99, 51, 131, and 76 were respectively associated with glucose metabolism disorders, lipid metabolism disorders, energy metabolism disorders, and oxidative stress in MASLD. Subsequently, 23 active compounds were identified in QGJZF that improve glucose metabolism homeostasis, ameliorate lipid metabolism disorders, restore energy metabolism balance, and alleviate oxidative damage. The combined analysis of active compounds and network pharmacology results indicates that their mechanism of action may involve the AGE-RAGE, MASLD-related, AMPK, and HIF-1 signaling pathways.

CONCLUSIONS

This study overcame the limitations of single-indicator screening methods for MASLD by proposing a multidimensional phenotypic method. Using this method, the active compounds in QGJZF and the key pathways through which they improve MASLD were identified.