Du L, Lu H, Xiao Y, Guo Z, Li Y. Prediction and analysis of quality markers for Chuantieling gel patches based on HPLC fingerprinting and network pharmacology.
Biomed Chromatogr 2024;
38:e5773. [PMID:
38048642 DOI:
10.1002/bmc.5773]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 12/06/2023]
Abstract
The Chuantieling gel patch (CGP), a traditional Chinese medicine compound, is an external treatment for asthma. It has shown remarkable effectiveness in alleviating asthma-related airway hyperresponsiveness and inflammation. Nevertheless, there is currently no information available regarding the analysis of quality markers for CGP, and there is a need for further improvement in quality control research. In this study, we developed an HPLC fingerprinting method for CGP and conducted a comprehensive methodological investigation. We assessed the similarity among 10 batches of CGP, identified common peaks, and quantified the content of seven major quality markers. Furthermore, we built a network pharmacology-based 'active ingredients-targets-pathways-diseases' network to forecast the potential mechanisms of action for the primary active components in asthma treatment. Our findings demonstrated that the developed CGP fingerprinting and content determination methods were consistent and trustworthy. We verified the existence of 25 shared peaks and successfully identified 7 chromatographic peaks, including sinigrin thiocyanate, ephedrine hydrochloride, methyleugenol, imperatorin, cinnamaldehyde, emodin, and 6-gingerol, using reference standards. The network pharmacology analysis suggested that these seven active components may target proteins such as STAT3 (signal transducer and activator of transcription 3), MAPK3 (mitogen-activated protein kinase 3), and TP53 (tumor protein P53) and influence various diseases through pathways including cancer pathways, hepatitis B, and PI3K-Akt (phosphoinositide 3-kinase-protein kinase B) signaling. This study provides insight into the complex multicomponent composition of CGP, and the predictive analysis through network pharmacology sets the stage for uncovering the mechanisms responsible for the therapeutic effects of CGP.
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