Table 3.
Formulation, characterization and optimization of modified release solid dosage forms are listed, with respect to inputs/outputs selected, type of the network used, and the authors(year) of performed studies.
| Formulation, characterization and optimization of modified release formulation | ||
|---|---|---|
| Inputs/outputs/aim | Network type | Authors, year |
| Design of controlled release formulations. Varying formulation variables were used as inputs and in vitro cumulative percentages of drug released were used as ANN outputs. | MLP | Chen, 1999 [21] |
| Optimization of diclofenac sodium sustained release matrix tablets. Trained model was used to predict release profiles and to optimize the formulation composition. | MLP | Zupancic Bozic, 1997 [31] |
| Design of extended release aspirin tablets. The amount of Eudragit® RS PO/Eudragit® L-polymer and compression pressure were selected as inputs, whereas in vitro dissolution profiles, release order and release constant from the Korsmayer Peppas equation were selected as output parameters. | GRNN | Ibric, 2002, 2003, 2007 [12,27,28] |
| Prediction of drug dissolution profiles. Inputs for the network training were the matrix forming agents’ ratio, the time point of the measurement of percent dissolved, and the difference between the release rate of the preceding two time points of the predicted profile. In vitro dissolution profiles were used as network outputs. | MLP | Peh, 2000 [24] |
| Investigation of controlled drug release. Drug fraction and time were used as network inputs and in vitro dissolution profiles as outputs. | MLP | Reis, 2004 [32] |
| Prediction of dissolution profiles for matrix controlled release theophylline pellet preparation. Inputs for the network training were the matrix forming agents’ ratio, and the time point of the measurement of percent dissolved; in vitro dissolution profiles were used as outputs. | EDNN | Goh, 2002 [18] |
| Modeling of diclofenac sodium release from Carbopol 71G matrix tablets. Polymer and binder content were inputs, while in vitro dissolution profiles were outputs | MLP | Ivic, 2010a [26] |
| Modeling of diclofenac sodium release from polyethylene oxide matrix tablets. Polymer weight ratio and compression force were used as inputs, whereas in vitro dissolution profiles were used as networks outputs. Dissolution profiles were treated as time series using dynamic neural networks. | MLP, GMDNN, OLDNN | Petrović, 2009 [13] |
| Drug release control and system understanding of sucrose esters matrix tablets. Networks inputs were HLB values of sucrose esters (SEs), SEs concentration, tablet volume, tablet porosity and tablet tensile strength. In vitro dissolution profiles and parameters indicative of burst release mean dissolution time and release exponent were used as outputs. | MLP | Chansanroj, 2011 [33] |
| A number of unique ANN configurations are presented, that have been evaluated for their ability to determine an IVIVC from different formulations of the same product. In vitro dissolution data were used as inputs and associated outputs were pharmacokinetic time points from nine patients enrolled in a crossover study. | MLP, GRNN, RNN | Dowell, 1999 [34] |
| Development of level A in vitro–in vivo correlation. Inputs for the network training were in vitro dissolution samples whereas in vivo dissolution profiles calculated by numerical deconvolution for each volunteer individually were used as outputs. | GRNN | Parojčić, 2007 [35] |
| Prediction of relative lung bioavailability and clinical effect of salbutamol when delivered to healthy volunteers and asthmatic patients from dry powder inhalers (DPIs). Training of the ANN network was performed using in vitro aerodynamic characteristics of the formulation and demographic data of volunteers/patients as input parameters, whilst in vivo data (urinary excretion of the drug and its metabolite) were networks outputs. | MLP | De Matas, 2008 [36] |
| Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres. Three independent variables, drug loading level, concentration of NaCl and CaCl2 in the release medium were used as the ANN inputs and the fractional release of doxorubicin at four different time points as the outputs. | MLP, HNN | Li, 2005 [37] |
| Prediction of drug release profiles in transdermal iontophoresis. Neural networks inputs were the process conditions of pH, ionic strength and current, as well as the time point. The output was the predicted permeation rate of the drug (diclofenac sodium). | RBFNN | Lim, 2003 [38] |
| Optimization of drug release from compressed multi unit particle system (MUPS) using generalized regression neural network (GRNN) | GRNN | Ivic, 2010b [39] |
Abbreviations: MLP, Multilayered Perceptron; GRNN, Generalized Regression Neural Network; RBFNN, Radial Basis Function Neural Network; EDNN, Elman Dynamic Neural Network; GMDNN, Gamma memory Dynamic Neural Network; OLDNN, One Layer Dynamic Neural Network; HNN, Hierarchical Neural Network.