Interleukin-8 (IL8/CXCL8) is present in decidua and trophoblast, which also expresses the IL8 receptors, CXCR1 and CXCR2. IL8 was shown to stimulate trophoblast migration. Matrix metalloproteinase (MMP)2, MMP9, and integrins a 5 b 1 and a 1 b 1 were found to play important roles in trophoblast invasion. We hypothesized that IL8 would increase this cell migration and invasion by HTR-8/SVneo cells through the activity of MMPs and integrins. Isolated first trimester of pregnancy cytotrophoblast (CT) and HTR-8/SVneo cell line were used. Migration was studied by monolayer wounding test, and invasion by Matrigel invasion test. The effects of IL8 on MMPs and integrin subunit expression were determined in HTR-8/SVneo cells by gelatin zymography and western blot respectively. The results that were obtained showed that exogenous IL8 stimulated HTR-8/SVneo cell migration and invasion. MMP2 and MMP9 levels were stimulated to 182% (P!0.01) and 134% (P!0.01) respectively. Integrin a 5 expression was increased to 119% (P!0.05) and integrin b 1 expression to 173% (P!0.001) of the control values. The data that were obtained show for the first time the sensitivity of the HTR-8/SVneo cells, in addition to isolated first trimester CT, to IL8. Exogenous IL8/CXCL8 increased trophoblast cell migration and invasion, which may be partly attributable to stimulation of MMP2 and MMP9 levels and an increase in integrins. HTR-8/SVneo cell viability and proliferation were also increased.

No abstract

No abstract

Drug release from hydrophilic matrix tablets can be strongly influenced by the proportion of matrix forming polymer and the dimensions and geometry of the tablets. A complete two-factor, three-level factorial design, followed by multiple regression analysis and response surface methodology, was applied to investigate the influence of polymer level and tablet size on drug release kinetics from hydrophilic matrix tablets prepared with Carbopol 971P and Carbopol 71G. Tablet diameter, radius-to-height ratio, tablet surface area, and surface-area-to-volume ratio were evaluated as independent variables in terms of their applicability to characterize tablet size and geometry. The results indicate that it may be possible to control the rate of drug release by modifying the proportion of carbomer in tablets and tablet dimensions. The practical benefit of these simulations is to optimize the geometry and dimensions of a controlled release device and reduce the number of experiments involved in the development of new controlled release dosage forms.

No abstract

KEYWORDS: artificial neural network, matrix tablets, controlled release, Eudragit L 100, aspirinThe purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tablets formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root mean square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f 1 2.465) and similarity (f 2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates th9e potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.

No abstract

No abstract