FORMULATION AND IN-VITRO EVALUATION OF EFAVIRENZ LIQUISOLID COMPACTS

Objective: The present research is aimed to enhance the dissolution rate of Efavirenz using liquisolid compact technology.Methods: About 16 different formulations were developed using factorial design with carriers (Neusilin and Fugicalin), binder (PVP K-30) and vehicle (polyethylene glycol 300) as independent variables and aerosil 200 is used as coating material. The In-vitro drug release from the LSC has used a dependent variable. The empirical method by Spireas and Bolton was applied to calculate the amounts of carrier and coating materials and obtained the improved flow characteristics and hardness by changing the proportion of carrier and coating materials.Results: A 23 factorial design is used and developed LSC using Neusilin (LSC-N1 to LSC-N8) and Fugicalin (LSC-F1 to LSC-F8). The physicochemical evaluation of all formulations exhibited well within the specification limits with respect to weight variation, hardness, friability and content uniformity. The In-vitro drug release from these LSC was evaluated in 0.1 N HCl and the optimized formulation (LSC-N8) was compared with pure drug (capsule) and physical mixture (tablet). The release studies proved that the liquisolid tablets results in higher release profile than pure drug and physical mixture due to increase in surface and wetting properties of the drug.Conclusion: LSC technique confirmed the enhanced dissolution rate of Efavirenz, which in turn helps in improving bioavailability.Â

Sequence-dependent clustering of parts and machines:a Fuzzy ART neural network approach

This study addresses the problem of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems, and for streamlining material flows in general. A pattern recognition approach based on artificial neural networks is proposed, and it is shown that the Fuzzy ART neural network can be effectively utilized for this application. First, a representation scheme for operation sequences is developed, followed by an illustrative example. A more comprehensive experimental verification, based on the mixture-model approach is then performed to evaluate its performance. The experimental factors include size of the part-machine matrix, proportion of voids, proportion of exceptional elements, and vigilance threshold. It is shown that this neural network is effective in identifying good clustering solutions, consistently and with relatively fast execution times