Thirty-five metabolites of Justicia gendarussa (JG) leaves and its preparations were identified using LC-HR-MS/MS. Although alkaloids were detected in the leaves they were not identified in JG preparations using Smart Formula 3D software. This showed that an acidified extraction process used at the first stage of the purification procedure is able to remove the toxic alkaloids from the crude drug. The LC-MS/MS analyses showed that the main components of JG preparations were fatty acids and apigenin glycosides; it seemed that the fatty acids can be used for enhancing the dissolution of the polar glycosides. T-test calculation using Profile Analysis software showed that the acidified crude drugs, extract, granules of JG, and gendarussa capsules showed very similar LC-MS/MS profiles, which means that the biochemical components of JG are relatively stable during processing. Due to the lack of quality markers for these JG preparations, the application of metabolite profiling is recommended as the QC tool for commercial production by the pharmaceutical industry.
Metabolite profiling can be applied as a reliable quality control (QC) tool for herbal drug preparations, especially if the specifications of the quality markers have not yet been determined. Some herbal drug preparations of Justicia gendarussa (JG), ie acidified dried crude drugs, ethanol extracts, laboratory-made granule preparations, and industrial-scale produced granules, could be clearly differentiated by using a combination of ultra-performance liquid chromatography ultra-high-resolution quadrupole-time-of-flight-mass spectrometry and multivariate analysis; unfortunately, attenuated total reflection-Fourier transform-infra-red spectroscopy could not be applied. The differences in the metabolite profiles were caused mostly by the variations in the concentrations of some fatty acid components. This work showed that classification analysis can be applied for reliable QC purposes of the J. gendarussa herbal drug preparations; the industrial-made granules of JG were located in the correct cluster of the score plot of the training set.
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