Issue 8, 2024

Analysing pharmacodynamic interactions of traditional Chinese medicine in treating acute pancreatitis based on OPLS method

Abstract

Acute pancreatitis (AP) is a surgical abdominal disease for which the Dachengqi Decoction (DCQD) of traditional Chinese medicine (TCM) is widely used in China. This study aims to analyse the pharmacodynamic interactions and quantitative relationship of DCQD in the treatment of AP based on orthogonal partial least squares (OPLS) analysis. The experimental data show organic chemical components as candidate pharmacodynamic substances (PS) in the blood and include pharmacodynamic indicators (PIs). Taking each PI as the target and using OPLS method to construct three types of mathematical equations, including the mathematical relationship between the pharmacodynamic substances and each target pharmacodynamic indicator (PS-TPI); the mathematical relationship between the pharmacodynamic substances, the pharmacodynamics indicators and each target pharmacodynamic indicator (PS, PI-TPI); and the mathematical relationship between the pharmacodynamic indicators and each target pharmacodynamic indicator (PI-TPI). Through analysis, we find that the R2Y(cum) values and VIP values indicate that PS and PI are the follow-up factors of TPI; the coefficient value indicates that there is a quantitative relationship between the PS and the TPI; and there also is a quantitative relationship between PI and TPI. The results demonstrated that PS and other PIs are the important influencing factors of TPI, and that there are interactions and quantitative relationships among the PIs.

Graphical abstract: Analysing pharmacodynamic interactions of traditional Chinese medicine in treating acute pancreatitis based on OPLS method

Article information

Article type
Paper
Submitted
24 Dec 2023
Accepted
22 Jan 2024
First published
07 Feb 2024

Anal. Methods, 2024,16, 1252-1260

Analysing pharmacodynamic interactions of traditional Chinese medicine in treating acute pancreatitis based on OPLS method

B. Nie, R. Yu, G. Xu, Y. Chen, C. Deng and J. Du, Anal. Methods, 2024, 16, 1252 DOI: 10.1039/D3AY02305B

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