Drug solubility and dissolution remain a significant challenge in pharmaceutical formulations. This study aimed to formulate and evaluate repanglinide (RPG) nanosuspension-based buccal fast-dissolving films (BDFs) for dissolution enhancement. RPG nanosuspension was prepared by the antisolvent-precipitation method using multiple hydrophilic polymers, including soluplus®, polyvinyl alcohol, polyvinyl pyrrolidine, poloxamers, and hydroxyl propyl methyl cellulose. The nanosuspension was then directly loaded into BDFs using the solvent casting technique. Twelve formulas were prepared with a particle size range of 81.6-1389 nm and PDI 0.002-1 for the different polymers. Nanosuspensions prepared with soluplus showed a favored mean particle size of 82.6 ± 3.2 nm. The particles were spherical and non-aggregating, as demonstrated by SEM imaging. FTIR showed no interaction between soluplus and RPG. Faster dissolution occurred for the nanosuspension in comparison with pure RPG (complete release vs 60% within 30 min). The nanosuspension was successfully incorporated into BDFs. The optimum film formula showed 28 s disintegration time, and 97.3% RPG released within 10 min. Ex-vivo permeation profiles revealed improved RPG nanosuspension permeation with the cumulative amount of RPG permeated is103.4% ± 10.1 and a flux of 0.00275 mg/cm2/min compared to 39.3% ± 9.57 and a flux of 0.001058 mg/cm2/min for pure RPG. RPG was successfully formulated into nanosuspension that boosted drug dissolution and permeation. The selection of the ultimate NP formula was driven by optimal particle size, distribution, and drug content. Soluplus NPs were shown to be the successful formulations, which were further incorporated into a buccal film. The film was evaluated for ex-vivo permeation, confirming successful RPG formulation with improved performance compared to pure drugs.
In this research was study the effect of increasing the number of layers of the semiconductor films as PbS on the average grain sizes and illustrate the relationship between the increase in the average grain size and thickness of the membrane, and membrane was prepared using the easy and simple and does not need the complexity of which is that the chemical bath , and from an X-ray diffraction found that the material and the installation of a random cubic and when increasing the number of layers deposited note the emergence of a number of vertices of a substance and PbS at different levels but the level is more severe (200) as well as the value is calculated optical energy gap and found to be not affected by increase thickness and from th
... Show MoreIt is not new to say that recent studies have tended to look at the margins of the texts in order to extract the information provided by the book to the recipient. The margins of the texts were defined in a number of terms, including the thresholds, and the margins of the text, etc. The difference was according to the researchers who dealt with the subject in research and inquiry. Therefore, all researchers assert that the texts must provide information even if it is propaganda for the text. Hence the importance of these texts emerged as they represent a scientific information material that encapsulates the body of the text as well as being a propaganda material that inspires the reader to read it. Hence for
... Show MoreA prepared PMMA/Anthracene film of thickness 70μm was irradiated under reduced pressure ~10-3 to 60Coγ-ray dose of (0.1mrad-10krad) range. The optical properties of the irradiated films were evaluated spectrophotometrically. The absorption spectrum showed induced absorption changes in the 200-400nm range. At 359nm, where there is a decrease in radiation-induced absorption, the optical density as a function of absorbed dose is linear from 10mrad-10Krad.It can therefore, be used as radiation dosimeter for gamma ray in the range 10mrd-10krad
Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.