1.
Comparative pharmacokinetic study on three formulations of Astragali Radix by an LC-MS/MS method for determination of formononetin in human plasma.
Rao, T, Gong, YF, Peng, JB, Wang, YC, He, K, Zhou, HH, Tan, ZR, Lv, LZ
Biomedical chromatography : BMC. 2019;(9):e4563
Abstract
Astragali Radix (AR) is a widely used traditional Chinese medicine for healing the cardiovascular, liver and immune systems. Recently, superfine pulverizing technology has been applied to developing novel formulations to improve bioavailability of the active constituents in herbs, such as ultrafine granular powder of AR. In this study, a universal and sensitive quantitative method based on LC-MS/MS was employed for determining formononetin, the main flavonoid in AR, in human plasma for comparative pharmacokinetics of three oral formulations of AR. Formononetin and IS (quercetin) were extracted by ethyl acetate from human plasma and were separated on a C18 column with a mobile phase consisting of acetonitrile and 0.1% formic acid. Positive-ion electrospray-ionization mode was applied in mass spectrometric detection. The quantitative method was validated with regards to selectivity, linearity, accuracy and precision, matrix effect, extraction recovery and stability, and was applied to comparing the pharmacokinetics of ultrafine granular powder (UGP), ultrafine powder (UP) and traditional decoction pieces (TDP) of AR after oral administration. The peak concentration and areas under the concentration-time curve of formononetin in UGP and UP were significantly higher than those of TDP. UGP and UP could significantly improve the bioavailability of AR in human compared with TDP after oral administration.
2.
Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC.
Jansen, MA, Kiwata, J, Arceo, J, Faull, KF, Hanrahan, G, Porter, E
Analytical and bioanalytical chemistry. 2010;(6):2367-74
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Abstract
Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network-genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer's desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples.