Preparative resolution of gatifloxacin enantiomers with pre-column esterification strategy and comparing their enantioselectivity to bacteria and antibody.

Gatifloxacin (GFX) is a kind of chiral fluoroquinolones compound due to the methyl group at the C-3 position of the piperazine ring[1]. Although the enantiomers of GFX show similar levels of antimicrobial activity and pharmacokinetics[2], the other biological activities (i.e., toxicity or enantioselective recognition to various receptors in vivo) of GFX enantiomers have not yet been studied. With this in mind, we developed a rapid and cost-effective high performance liquid chromatographic (HPLC) separation procedure for GFX enantiomers with a pre-column esterification strategy. With significant enhancement of drug solubility and optimization for chromatographic conditions, the proposed method was scaled up to preparative HPLC to obtain optical active S-(-)- and R-(+)-GFX. The antibacterial activities of GFX enantiomers after preparative separation were further verified by measuring the Minimum Inhibitory Concentration (MIC) values against Escherichia coli ATCC 25922. In addition, the binding selectivity of GFX enantiomers to protein receptor were evaluated by antibody using enzyme-linked immunosorbent assay (ELISA) for the first time.

[Adaptive network-based fuzzy inference system for the forecast of spectra].

Because of the function of independent adaptation and study of NN and the best nonlinear speculating ability of fuzzy system, the adaptive network-based fuzzy inference system (ANFIS) which is composed of them absorbs their virtue. When applying it to the data analysis and model construction, the authors got the good nonlinear forecast by learning data, such as fluorescence spectrum. Fluorescence spectrometry is an important means of researching inside structure of molecule, which works easy, and features rapidness and high precision. So its forecast is more important. In the present paper, the authors utilize the spectra of the N2 to calculate, and prove that the means can forecast important spectrum information, the error is very small, less than 1. 66 percent, which is conformed to meet the demand of the experiment. In short, the approach is workable.