Establishment of an anti-inflammation-based bioassay for the quality control of the 13-component TCM formula (Lianhua Qingwen)

Abstract

Context

Owing to the complexity of chemical ingredients in traditional Chinese medicine (TCM), it is difficult to maintain quality and efficacy by relying only on chemical markers.

Objective

Lianhua Qingwen capsule (LHQW) was selected as an example to discuss the feasibility of a bioassay for quality control.

Materials and methods

Network pharmacology was used to screen potential targets in LHQW with respect to its anti-inflammatory effects. An in vitro cell model was used to validate the prediction. An anti-inflammatory bioassay was established for the quality evaluation of LHQW in 40 batches of marketed products and three batches of destructed samples.

Results

The tumor necrosis factor/interleukin-6 (TNF/IL-6) pathway via macrophage was selected as the potential target of LHQW. The IC₅₀ value of LHQW on RAW 264.7 was 799.8 μg/mL. LHQW had significant inhibitory effects on the expression of IL-6 in a dose-dependent manner (p < 0.05). The anti-inflammatory biopotency of LHQW was calculated based on the inhibitory bioactivity on IL-6. The biopotency of 40 marketed samples ranged from 404 U/μg to 2171 U/μg, with a coefficient of variation (CV) of 37.91%. By contrast, the contents of forsythin indicated lower CV (28.05%) than the value of biopotency. Moreover, the biopotencies of destructed samples declined approximate 50%, while the contents of forsythin did not change. This newly established bioassay revealed a better ability to discriminate the quality variations of LHQW as compared to the routine chemical determination.

Conclusions

A well-established bioassay may have promising ability to reveal the variance in quality of TCM.

Keywords:

• Biopotency
• macrophage
• network pharmacology
• quality variation
• interleukin-6

Author contribution

All data were generated in-house. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the [National Key R&D Program of China] under Grant [number 2018YFC1707000]; [Beijing-Tianjin-Hebei Collaborative Innovation Promotion Project] under Grant [number Z171100004517014]; [PLA Youth Training Project for Medical Science] under Grant [number 16QNP151]; and [Medical Big Data and Artificial Intelligence Project of Chinese PLA General Hospital] under Grant [number 2019MBD-045].