Bile salt/phospholipid mixed micelle precursor pellets prepared by fluid-bed coating.

Bile salt/phospholipid mixed micelles (MMs) are potent carriers used for oral absorption of drugs that are poorly soluble in water; however, there are many limitations associated with liquid formulations. In the current study, the feasibility of preparing bile salt/phospholipid MM precursor (preMM) pellets with high oral bioavailability, using fluid-bed coating technology, was examined. In this study, fenofibrate (FB) and sodium deoxycholate (SDC) were used as the model drug and the bile salt, respectively. To prepare the MMs and to serve as the micellular carrier, a weight ratio of 4:6 was selected for the sodium deoxycholate/phospholipids based on the ternary phase diagram. Polyethylene glycol (PEG) 6000 was selected as the dispersion matrix for precipitation of the MMs onto pellets, since it can enhance the solubilizing ability of the MMs. Coating of the MMs onto the pellets using the fluid-bed coating technology was efficient and the pellets were spherical and intact. MMs could be easily reconstituted from preMM pellets in water. Although they existed in a crystalline state in the preMM pellets, FB could be encapsulated into the reconstituted MMs, and the MMs were redispersed better than solid dispersion pellets (FB:PEG = 1:3) and Lipanthyl®. The redispersibility of the preMM pellets increased with the increase of the FB/PEG/micellar carrier. PreMM pellets with a FB:PEG:micellar carrier ratio of 1:1.5:1.5 showed 284% and 145% bioavailability relative to Lipanthyl® and solid dispersion pellets (FB:PEG = 1:3), respectively. Fluid-bed coating technology has considerable potential for use in preparing sodium deoxycholate/phospholipid preMM pellets, with enhanced oral bioavailability for poorly water-soluble drugs.

[Study on the application of artificial neural network in analysing the risk factors of diabetes mellitus].

OBJECTIVE: To study the use of neural network in determining the risk factors of diseases. METHODS: With back-propagation neural network (BP network) as fitting model based upon data gathered from an epidemiological survey on diabetes mellitus and under the network structure of 22-6-1, the mean impact value (MIV) for each input variables and sequencing the factors according to their absolute MIVs were calculated. The results from BP network with multiple logistic regression analysis and log-linear model for united actions between factors were compared with optimizing Levenberg-Marquardt algorithm. RESULTS: By BP network analysis, the sequence of importance for the risk factors of diabetes mellitus became: faster pulse, diabetes mellitus family history, living longer in the investigated area, with medical record of nephropathy, having higher ratio for waist-to-hip, being male, with medical records of diseases as hyperlipoproteinmia, coronary heart disease, hypertension, high diastolic pressure, higher income, do no drink alcohol, age, higher systolic pressure, less educated, body mass index, with medical records of other diseases, physical exercise related to jobs smoking, occupation, with medical record for cerebrovascular disease, with medical record for liver disease etc. However, only 7 factors were statistically significant in multiple logistic regression analysis. The sequence of their importance appeared as: pulse, diabetes mellitus family history, the medical record of nephropathy, waist-to-hip ratio, the medical record of hypertension, work-place related exercise and age. The sequences of importance were almost the same between the two while the difference could partly be explained by the interaction among risk factors through log-linear model. CONCLUSION: Neural network could be used to analyze the risk factors of diseases and could assimilate more complicated relationships (main effects and interactions) between inputs and outputs, better than using the traditional methods.