A sensitive and efficient method for simultaneous trace detection and identification of triterpene acids and its application to pharmacokinetic study.

A sensitive and efficient method for simultaneous trace detection of seven triterpene acids was developed and validated for analysis of rat plasma samples. The required micro-sampling of only 20 μL blood reduced the difficulty in blood collection and the injury to animal. The whole pretreatment procedure was more conveniently finished within 26 min through the application of the semi-automated derivatization extraction method to biological samples. Seven analytes were rapidly separated within 30 min on reversed-phase Akasil-C18 column and quantified by fluorescence detector. Online ion trap MS with atmospheric pressure chemical ionization (APCI) source was used for further identification. The novel application of artificial neural network (ANN) combined with genetic algorithm (GA) to optimization of derivatization was performed and compared with the classical response surface methodology (RSM). Optimal derivatization condition was validated by multi-criteria and nonparametric tests and used successfully to achieve the rather high sensitivity (limit of detection: 0.67-1.08 ng/mL). The limit of reactant concentration (LORC) special for derivatization was studied and the lower values (2.53-4.03 ng/mL) ensured the trace detection. Results of validation demonstrated the advantages for pharmacokinetic study, such as higher sensitivity, better accuracy, easier pretreatment and shorter run-time. Pharmacokinetic study of triterpene acids after oral administration of Salvia miltiorrhiza extract to mice was conducted for the first time. The present method provided more sensitive and efficient alternative for the medical detection of bioactive constituents from herbal extract in the biological liquid.