Artificial neural networks in the modeling and optimization of aspirin extended release tablets with eudragit L 100 as matrix substance

Abstract

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.

Comparison of novel granulated pellet-containing tablets and traditional pellet-containing tablets by artificial neural networks

“…Due to the excellent elongation of Eudragit FS 30D, it is commonly used to modify the mechanical properties of Eudragit L 30D-55 films 21 . Meanwhile, the addition of Eudragit FS 30D would not change the enteric property of the film, but only adjust the release rate of drug for its insolubility at pH 5.5.…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…For example, the reliability of ANNs in optimizing controlled release capsules and ketoprofen hydrogel ointment has been demonstrated by Hussain et al [54]. A trained ANN model has been successfully employed to predict release profile and optimize formulation of various drug formulations such as aspirin extended release tablets [55,56], diclofenac sodium sustained release matrix tablets [57], salbutamol sulfate osmotic pump tablets [58], transdermal ketoprofen hydrogel [59], and nimodipine floating tablet formulation [60].…”

Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms

“…Moreover surrogate models built without derivatives are superior to local surrogate models built with derivatives for some surprisingly simple and smooth frameworks [34,6]. Some experiments with very different forms of meta-models can be found in [18,28,37], and many applications can be found in [35,32,22,23,27,20,29,24]. Theorem 5.2 can also be applied to optimization with surrogate models.…”