Influence of Hydroxypropyl Methylcellulose Molecular Weight Grade on Water Uptake, Erosion and Drug Release Properties of Diclofenac Sodium Matrix Tablets

Purpose: To comparatively evaluate the effect of two hydroxylpropyl methylcellulose (HPMC) molecular weight grades (K4M and K15M) on drug release from diclofenac sodium matrix tablets.Methods: Tablets containing diclofenac sodium were prepared by direct compression method at various drug/HPMC ratios and evaluated in vitro for their water uptake, erosion and dissolution characteristics over a period of 8 h. Their release data were analyzed according to various release kinetic models.Results: The release rate of diclofenac decreased with increase in polymer content and was dependent on the HPMC type used, with the lower release rate observed in formulations containing the higher molecular weight grade HPMC K15M. Formulations containing the higher molecular weight HPMC (F4, F5 and F6) showed higher water uptake than those containing the lower molecular weight polymer (F1, F2 and F3) (p < 0. 001). The formulations incorporating the lower molecular weight HPMC K4M (F1, F2 and F3) showed higher erosion than those that contained HPMC K15M (F4, F5 and F6) (p < 0.001). Kinetic data based on the release exponent, n, in Peppas model, showed that n values were between 0.14 and 0.55, indicating that drug release from HPMC matrices was predominantly by diffusion.Conclusion: This study demonstrates that the molecular weight (MW) of HPMC does affect the water uptake and erosion as well as the rate of drug release from of HPMC matrices.Keywords: Matrix, Diclofenac sodium, HPMC, Erosion, Water uptake

Development and Evaluation of Mucoadhesive Chlorhexidine Tablet Formulations

Purpose: To formulate mucoadhesive chlorhexidine tablets and evaluate their drug release characteristics and mechanism.Methods: Chlorhexidine buccal adhesive tablets were prepared by direct compression using a blend of hydroxypropyl methylcellulose (HPMC) and chitosan as the bioadhesive polymers. Their dissolutionproperties were assessed according to USP 24 (paddle method). In order to determine the mode of drug release from the tablets, the release data were subjected to various release kinetic models. Thebioadhesive strength of the tablets was also evaluated. Results: The results showed that as the proportion of HPMC in the blend increased, drug release rate decreased, with the lowest release rate observed when HPMC alone was used as the bioadhesive polymer (p < 0.001). Both the type and ratio of the polymers used influenced release kinetics. Also,bioadhesion force increased with increasing proportion of HPMC in the tablets, with the highest adhesion force shown when HPMC was the only polymer used, and lowest when chitosan was used alone (p < 0.01). Conclusion: The chlorhexidine formulations developed showed promise as a bioadhesive delivery system for the drug.Keywords:  Mucoadhesive tablets; Chlorhexidine; HPMC; Chitosan; Release properties; Bioadhesive strength

A Robot Web for Distributed Many-Device Localisation

We show that a distributed network of robots or other devices which make measurements of each other can collaborate to globally localise via efficient ad-hoc peer to peer communication. Our Robot Web solution is based on Gaussian Belief Propagation on the fundamental non-linear factor graph describing the probabilistic structure of all of the observations robots make internally or of each other, and is flexible for any type of robot, motion or sensor. We define a simple and efficient communication protocol which can be implemented by the publishing and reading of web pages or other asynchronous communication technologies. We show in simulations with up to 1000 robots interacting in arbitrary patterns that our solution convergently achieves global accuracy as accurate as a centralised non-linear factor graph solver while operating with high distributed efficiency of computation and communication. Via the use of robust factors in GBP, our method is tolerant to a high percentage of faults in sensor measurements or dropped communication packets.Comment: Published in IEEE Transactions on Robotics (TRO) 202

Evolving text classification rules with genetic programming

We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications