Five-descriptor model to predict the chromatographic sequence of natural compounds.

Despite the recent introduction of mass detection techniques, ultraviolet detection is still widely applied in the field of the chromatographic analysis of natural medicines. Here, a neural network cascade model consisting of nine small artificial neural network units was innovatively developed to predict the chromatographic sequence of natural compounds by integrating five molecular descriptors as the input. A total of 117 compounds of known structure were collected for model building. The order of appearance of each compound was determined in gradient chromatography. Strong linear correlation was found between the predicted and actual chromatographic position orders (Spearman's rho = 0.883, p < 0.0001). Application of the model to the external validation set of nine natural compounds was shown to dramatically increase the prediction accuracy of the real chromatographic order of multiple compounds. A case study shows that chromatographic sequence prediction based on a neural network cascade facilitated compound identification in the chromatographic fingerprint of Radix Salvia miltiorrhiza. For natural medicines of known compound composition, our method provides a feasible means for identifying the constituents of interest when only ultraviolet detection is available.

Construction and in vitro/in vivo evaluation of 17-allylamino-17-demethoxygeldanamycin (17AAG)-loaded PEGylated nanostructured lipid carriers.

In this study, the PEGylated nanostructured lipid carriers (PEG-NLC) were constructed for the intravenous delivery of 17-allylamino-17-demethoxygeldanamycin (17AAG). 17AAG-PEG-NLC was successfully prepared by the method of emulsion evaporation at a high temperature and solidification at a low temperature using a mixture of glycerol monostearate and PEG2000-stearate as solid lipids, and medium-chain triglyceride as the liquid lipid. The results revealed that the morphology of the NLC was spheroidal. The particle size, zeta potential and entrapment efficiency for 17AAG-PEG-NLC were observed as 189.4 nm, -20.2 mV and 83.42%, respectively. X-ray diffraction analysis revealed that 17AAG existed as amorphous structures in the nanoparticles. In the in vitro release study, the 17AAG from 17AAG-PEG-NLC exhibited a biphasic release pattern with burst release initially and sustained release afterwards. In addition, 17AAG-PEG-NLC showed a significantly higher in vitro antitumor efficacy and longer retention time in vivo than 17AAG solution. These results indicated that 17AAG-PEG-NLC may offer a promising alternative to the current 17AAG formulations for the treatment of solid tumors.