Integrating Network Pharmacology, Quantitative Metabolic Network Analysis, In Vitro Experiments, and Molecular Dynamics to Explore the Mechanism of Angelica Sinensis for Regulating Bone Metabolism.

BACKGROUND: Bone metabolic diseases are serious health issues worldwide. Angelica sinensis (AS) is traditionally used in Chinese medicine for treating bone metabolism diseases clinically. However, the mechanism of AS in regulating bone metabolism remains uncertain. OBJECTIVE: The current investigation was structured to elucidate the potential mechanisms of AS for modulating bone metabolism. METHODS: Firstly, targets of AS regulating bone metabolism were collected by network pharmacology. Then, the transcriptional regulation of RUNX2 was enriched as one of the key pathways for AS to regulate bone metabolism, constructing its metabolic network. Secondly, combining molecular docking, network efficiency, and network flux analyses, we conducted a quantitative evaluation of the metabolic network to reveal the potential mechanisms and components of AS regulating bone metabolism. Finally, we explored the effect of AS on the differentiation of osteoclasts from M-CSF and RANKL-induced RAW264.7 cells, as well as its impact on the osteogenic induction of MC3T3-E1 cells. We verified the mechanism and key targets of AS on bone metabolism using qRT-PCR. Furthermore, the key component was preliminarily validated through molecular dynamics simulation. RESULTS: Quantitative metabolic network of the transcriptional regulation of RUNX2 was constructed to illustrate the potential mechanism of AS for regulating bone metabolism, indicating that ferulic acid may be a pharmacological component of AS that interferes with bone metabolism. AS suppressed osteoclast differentiation in M-CSF and RANKL-induced RAW264.7 cells and reversed the expressions of osteoclastic differentiation markers, including RUNX2 and SRC. Additionally, AS induced osteogenic generation in MC3T3-E1 cells and reversed the expressions of markers associated with osteoblastic generation, such as RUNX2 and HDAC4. Molecular dynamics simulation displayed a strong binding affinity among ferulic acid, HDAC4 and SRC. CONCLUSION: This study reveals a systematic perspective on the intervention bone mechanism of AS by transcriptive regulation by RUNX2, guiding the clinical use of AS in treating diseases of the skeletal system.

Identification of a novel farnesoid X receptor agonist, kaempferol-7-O-rhamnoside, a compound ameliorating drug-induced liver injury based on virtual screening and in vitro validation.

Farnesoid X receptor (FXR), a bile acid receptor, plays an essential role in maintaining bile acid and liver homeostasis and has been recognized as an essential target for drug-induced liver injury (DILI). This study aimed to identify potential FXR agonists by virtual screening, molecular dynamics (MD) simulation, and biological assays. First, an in-house Traditional Chinese medicine compound database was screened using a virtual approach based on molecular docking to reveal potential FXR agonists. Secondly, MD was applied to analyze the process of agonist binding. Finally, the acetaminophen (APAP)-induced L02 cells model evaluated the pharmacodynamic activity of agonists treating DILI. Virtual screening results showed that kaempferol-7-O-rhamnoside was confirmed as the FXR agonist. MD results showed that kaempferol-7-O-rhamnoside could stably bind the FXR. In addition, in vitro cell-based assay showed that kaempferol-7-O-rhamnoside could promote the expression of the FXR gene and inhibit the Cyp7a1 gene expression in APAP-induced cells, significantly reducing the activities of AST, AKP and ROS, and enhancing the expression of GSH. The current study confirmed that kaempferol-7-O-rhamnoside might improve liver function by promoting proliferation, ameliorating oxidative stress, and regulating FXR target genes as observed in vitro. Therefore, in this study, discovering the FXR agonist, kaempferol-7-O-rhamnoside, provides valuable guidance for developing novel drugs against DILI.

A precise efficacy determination strategy of traditional Chinese herbs based on Q-markers: Anticancer efficacy of Astragali radix as a case.

BACKGROUND: As a "multi-components and multi-efficacy" complex system, traditional Chinese herbs are universally distributed and applied in treating clinical diseases. However, the efficacy deviation and ambiguous clinical location are affected by different effects and content of components caused by uncertain factors in the production process. It further restricts resource allocation and clinical medication and hinders modernization and globalization. In this study, a precise efficacy determination strategy was innovatively proposed, aiming to quantitatively predict the efficacy of herbs and obtain precise medicinal materials. Quality-markers (Q-markers) characterizing the efficacy are conducive to achieving precise efficacy determination. PURPOSE: With the anticancer efficacy of Astragali radix (AR) as a case, the present study was designed to establish a methodology for precise efficacy determination based on Q-markers characterizing specific efficacy. METHODS: Guided by the basic principles of Q-markers, the potential Q-markers characterizing the anticancer efficacy of AR were screened through molecular simulation and network pharmacology. The activity of Q-markers was evaluated on MDA-MB-231 cells, and the content of Q-markers was determined by HPLC. A quantitative efficacy prediction model of the relationship between the influencing factors and anticancer efficacy was further constructed through the effect-constituents index (ECI) and machine learning and verified by biotechnology, which can be directly applied to predict the efficacy in numerous samples. RESULTS: Astragaloside I, astragaloside II, and astragaloside III inhibited the proliferation of MDA-MB-231 cells and were successfully quantified in AR samples, reflecting the effectiveness and measurability of Q-markers. Gradient Boost Regression showed the best performance in the quantitative efficacy prediction model with EVtest= 0.815, R2test= 0.802. The results of precise efficacy determination indicated that 1-2-3 (Wuzhai, Shanxi, two years, C segment) sample performed best in 54 batches of AR samples with biased anticancer efficacy. Furthermore, AR samples with higher ECI had higher anticancer efficacy and vice versa. CONCLUSION: The precise efficacy determination strategy established in the present study is reliable and proved in the AR case, which is expected to support resource allocation optimization, efficacy stability improvement, and precise clinical medication achievement.