Preparation and Characterization of Lurasidone Hydrochloride Solid Dispersion and Study on the in vitro Dissolution Behavior / 中国药房

OBJECTIVE:To prepare the lurasidone hydrochloride solid dispersion,and improve its dissolution rate. METH-ODS:Taking povidone K30 as the carrier,solvent method was used to prepare the lurasidone hydrochloride solid dispersion with different drug-load ratios(1:0.5,1:1,1:2). The in vitro dissolution rates of 3 kinds of lurasidone hydrochloride solid dispersion with physical mixture (lurasidone hydrochloride-povidone K30) and original preparation were compared. X-ray powder diffraction method was adopted to analyze the crystal structures of raw material of lurasidone hydrochloride,povidone K30 and accessories, physical mixture (1:2) and accessories,and lurasidone hydrochloride solid dispersion (1:2) and accessories. RESULTS:Com-pared with physical mixture,the dissolution rate of lurasidone hydrochloride solid dispersion with drug-load ratios of 1:0.5,1:1, 1:2 was significantly improved,and the dissolution rate of solid dispersion was increased as the increase of the carrier ratio. The in vitro dissolution rates of lurasidone hydrochloride solid dispersion with drug-load ratio of 1:2 and original preparation were respec-tively 101.2%and 100.2%in 20 min. X-ray powder diffraction showed,there were characteristic absorption peaks of lurasidone hy-drochloride and accessories in physical mixture;the characteristic absorption peak of lurasidone hydrochloride in solid dispersion disappeared basically,and the characteristic absorption peak of accessories still existed. CONCLUSIONS:The in vitro dissolution of lurasidone hydrochloride solid dispersion with drug-load ratio of 1:2 is similar to original preparation,and lurasidone hydrochlo-ride exists in the solid dispersion as amorphous form.

Study on diagnostic methods of breathing disorders based on fuzzy logic inference and the neural network / 中国医疗器械杂志

This paper descries a new non-invasive method for diagnosis of breathing disorders based on adaptive-network-based fuzzy inference system (ANFIS). In this method, PetCO2, SpO2 and HR are chosen as inputs, and the breathing condition is selected as output ofANFIS. The inputs and output are then classified into fuzzy subsets by experts' knowledge. After, the fuzzy IF-THEN rules are built up according to the corresponding membership functions by set up of fuzzy subsets. The neural network was finally established and the membership functions and fuzzy rules were optimized by training. The results of experiment shows that ANFIS is more effective than BP Network regarding the diagnosis of breathing disorders.