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.
This study had two aims. Firstly, we wanted to model the effects of the percentage of Eudragit RS PO and compression pressure as the most important process and formulation variables on the time course of drug release from extended-release matrix aspirin tablets. Secondly, we investigated the possibility of predicting drug stability and shelf-life using an artificial neural network (ANN). Ten types of matrix aspirin tablets were prepared as model formulations and were stored in stability chambers at 60 degrees C, 50 degrees C, 40 degrees C and 30 degrees C and controlled humidity. Samples were removed at predefined time points and analysed for acetylsalicylic acid (ASA) and salicylic acid (SA) content using stability-indicating HPLC. The decrease in aspirin content followed apparent zero-order kinetics. The amount of Eudragit RS PO and compression pressure were selected as causal factors. The apparent zero-order rate constants for each temperature were chosen as output variables for the ANN. A set of output parameters and causal factors were used as training data for the generalized regression neural network (GRNN). For two additional test formulations, Arrhenius plots were constructed from the experimentally observed and GRNN-predicted results. The slopes of experimentally observed and predicted Arrhenius plots were tested for significance using Student's t-test. For test formulations, the shelf life (t(95%)) was then calculated from experimentally observed values (t(95%) 82.90 weeks), as well as from GRNN-predicted values (t(95%) 81.88 weeks). These results demonstrate that GRNN networks can be used to predict ASA content and shelf life without stability testing for formulations in which the amount of polymer and tablet hardness are within the investigated range.
In this work, the behavior of the active pharmaceutical substances methylprednisolone (in a form of methylprednisolone sodium succinate) in finished pharmaceutical dosage form, i.e., freeze-dried powder for injections, was examined. The goal was to evaluate the chemical stabilities of methylprednisolone sodium succinate packaged in a dual chamber vial, as a specific container closure system. The effect of different parameters: temperature, moisture and light were monitored. The method proposed by United States Pharmacopeia was used to determine concentrations of methylprednisolone, as the sum of the concentration of methylprednisolone esters (17-hydrogen succinate and 21-hydrogen succinate) and free methylprednisolone. The HPLC method was used for stability evaluation of the active substance and determination of related substances. Four main degradation products were registered. Temperature has a major impact on the degradation process with the appearance of 3 degradation products (impurities B, C and D), while the presence of light caused an increasing content of impurity A. Identification of impurity B, C and D has been realized using mass and NMR spectroscopy. All three substances are substances related to methylprednisolone.
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