HTRF-based high-throughput PGE2 release prohibition model and application in discovering traditional Chinese medicine active ingredients

• Published in: Zhongguo zhongyao zazhi Vol. 41; no. 3; p. 509
• Main Authors: Bai, Zhi-Ru, Fei, Hong-Qiang, Li, Na, Cao, Liang, Zhang, Chen-Feng, Wang, Tuan-Jie, Ding, Gang, Wang, Zhen-Zhong, Xiao, Wei
• Format: Journal Article
• Language: Chinese; English
• Published: China 01.02.2016
• Subjects: Automation; Cell Line; Dinoprostone - chemistry; Drug Evaluation, Preclinical - instrumentation; Drug Evaluation, Preclinical - methods; Drugs, Chinese Herbal - chemistry; Fluoroimmunoassay; Humans; Medicine, Chinese Traditional; traditional Chinese medicine material base library; HTRF; prostaglandin E2; high throughput
• ISSN: 1001-5302
• DOI: 10.4268/cjcmm20160325

Prostaglandin (PG) E2 is an active substance in pathological and physiological mechanisms, such as inflammation and pain. The in vitro high-throughput assay for screening the inhibitors of reducing PEG2 production is a useful method for finding out antiphlogistic and analgesic candidates. The assay was based on LPS-induced PGE2 production model using a homogeneous time-resolved fluorescence(HTRF) PGE2 testing kit combined with liquid handling automation and detection instruments. The critical steps, including the cell density optimization and IC50 values determination of a positive compound, were taken to verify the stability and sensibility of the assay. Low intra-plate, inter-plate and day-to-day variability were observed in this 384-well, high-throughput format assay. Totally 5 121 samples were selected from the company's traditional Chinese medicine(TCM) material base library and used to screen PGE2 inhibitors. In this model, the cell plating density was 2 000 cells for each well; the average IC₅₀ value for positive compounds was (7.3±0.1) μmol; the Z' factor for test plates was more than 0.5 and averaged at 0.7. Among the 5 121 samples, 228 components exhibited a PGE2 production prohibition rate of more than 50%, and 23 components exhibited more than 80%. This model reached the expected standards in data stability and accuracy, indicating the reliability and authenticity of the screening results. The automated screening system was introduced to make the model fast and efficient, with a average daily screening amount exceeding 14 000 data points and provide a new model for discovering new anti-inflammatory and analgesic drug and quickly screening effective constituents of TCM in the early stage.