Film-forming polymer-granulated excipients as the matrix materials for controlled release dosage forms

Tsai, Tsung‐Han; San, Yu; Ho, Hsiu O.; Wu, Jen Sen; Sheu, Ming Thau

doi:10.1016/s0168-3659(97)00183-1

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…Similar findings were reported in an experimental study by Fu et al (28). Different results have been published about the relationship between hardness and the polymer amount used for granulation; e.g., no effect on the tensile strength of the tablets was observed when different amounts of Eudragit ® RL 30 D and RS 30 D (2.5%-10.0%) were used for the granulation of lactose and dicalcium phosphate mixture (27). However, the increase of hardness, described above, could be due to the increasing tablet mass, which could also contribute to the improvement of the mechanical properties of tablets (29).…”

Section: Discussionsupporting

confidence: 86%

The Development of Eudragit® NM-Based Controlled-Release Matrix Tablets

et al. 2012

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Eudragit ® NM was investigated as a matrix former in combination with microcrystalline cellulose as an insoluble filler for preparing controlled-release tablets containing model drugs with different solubility.Material and Methods. Three sets of matrix tablets differing in the drug-to-filler ratio (1:1, 2:1, and 4:1) and polymer amount with diltiazem hydrochloride (freely soluble) or caffeine (sparingly soluble) were prepared. Samples were evaluated by the dissolution test at pH 6.8 corresponding to the upper part of the small intestine; the selected samples were tested at a changing pH level to better simulate in vivo conditions.Results. The prepared matrix tablets fulfilled all the requirements of the European Pharmacopoeia. Tablets with Eudragit ® NM showed excellent mechanical characteristics. In vitro studies showed that the set 1:1 was the most suitable for the sustained release of a freely soluble drug concerning the burst effect and the total drug amount released within 12 hours. The significant effect of the drug-to-filler ratio and polymer amount on the dissolution profile was confirmed by similarity factor analysis. A faster drug release was observed during the dissolution test within changing pH levels because of the pH-dependent solubility of diltiazem. A prolonged release of the sparingly soluble drug was not achieved, and a trend for fast disintegration was observed.Conclusions. The combination of Eudragit ® NM with microcrystalline cellulose as an insoluble filler seems to be suitable only for freely soluble drugs, when the amount of the drug and the filler is similar.

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Section: Discussionsupporting

confidence: 86%

The Development of Eudragit® NM-Based Controlled-Release Matrix Tablets

et al. 2012

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“…4 shows that on increasing Eudragit® RS form 1% to 10% a decrease in drug release was observed at Y 1 , Y 2 , and Y 3 . Such finding was as expected and is in agreement with the findings of many previous reports (41,50,51). These results stem from the fact that Eudragit® RS is insoluble in aqueous media and acts as a shield preventing the penetration of the dissolution medium into the tablets and mesalazine from dissolution (52).…”

Section: Determination Of the Regression Model And Statistical Evaluasupporting

confidence: 93%

Once Daily, High-Dose Mesalazine Controlled-Release Tablet for Colonic Delivery: Optimization of Formulation Variables Using Box–Behnken Design

2011

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Abstract. The aim of this work was to statistically optimize a novel high-dose, mesalazine colonic delivery matrix system, potentially suitable for once daily administration, using simple wet granulation method. A hydrophobic-hydrophilic polymeric blend was used to manipulate drug release. A three-factor, three-level Box-Behnken design was used to construct polynomial models correlating the dependent and independent variables. Independent formulation variables were the percentages of the hydrophilic polymer Carbopol® 940, hydrophobic polymer Eudragit® RS, and the superdisintegrant croscarmellose sodium. The cumulative percentages of drug released at 6, 10, and 14 h were selected as dependent variables and restricted to 7.5-22.5% (Y 1 ), 42.5-57.5 % (Y 2 ), and 72.5-87.5% (Y 3 ), respectively. A second-order polynomial equation fitted to the data was used to optimize the independent formulation variables. Based on Box-Behnken experimental design, different mesalazine release profiles were obtained. The optimized formulation containing 5.72% Carbopol®, 9.77% Eudragit® RS, and 1.45% croscarmellose sodium was prepared according to the software determined levels. It provided a release profile which was very close to the targeted release profile, where the calculated values of f 1 and f 2 were 8.47 and 67.70, respectively, and followed zero-order release kinetics.

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“…The use of Surelease ® , an aqueous dispersion of ethylcellulose as a granulating fluid, has also been reported, where it has been shown to retard drug release from monolithic matrix formulations [35,36]. These materials were investigated as potential excipients, in combination to control the rate of salbutamol sulfate release from hydrophilic matrix tablets.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Salbutamol Sulfate Dissolution from Sustained Release Matrix Formulations Using an Artificial Neural Network

2010

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An artificial neural network was used to optimize the release of salbutamol sulfate from hydrophilic matrix formulations. Model formulations to be used for training, testing and validating the neural network were manufactured with the aid of a central composite design with varying the levels of Methocel® K100M, xanthan gum, Carbopol® 974P and Surelease® as the input factors. In vitro dissolution time profiles at six different sampling times were used as target data in training the neural network for formulation optimization. A multi layer perceptron with one hidden layer was constructed using Matlab®, and the number of nodes in the hidden layer was optimized by trial and error to develop a model with the best predictive ability. The results revealed that a neural network with nine nodes was optimal for developing and optimizing formulations. Simulations undertaken with the training data revealed that the constructed model was useable. The optimized neural network was used for optimization of formulation with desirable release characteristics and the results indicated that there was agreement between the predicted formulation and the manufactured formulation. This work illustrates the possible utility of artificial neural networks for the optimization of pharmaceutical formulations with desirable performance characteristics.

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Film-forming polymer-granulated excipients as the matrix materials for controlled release dosage forms

Cited by 16 publications

References 9 publications

The Development of Eudragit® NM-Based Controlled-Release Matrix Tablets

The Development of Eudragit® NM-Based Controlled-Release Matrix Tablets

Once Daily, High-Dose Mesalazine Controlled-Release Tablet for Colonic Delivery: Optimization of Formulation Variables Using Box–Behnken Design

Optimization of Salbutamol Sulfate Dissolution from Sustained Release Matrix Formulations Using an Artificial Neural Network

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