Artificial Neural Networks: Implications for Pharmaceutical Sciences

Achanta, A. S.; Kowalski, J.; Rhodes, C. T.

doi:10.3109/03639049509048099

Cited by 98 publications

(37 citation statements)

References 40 publications

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“…This fact is more relevant, if we compare with other related areas as pharmaceutical science of important research in the last few years (Achanta et al, 1995;Colbourn, 2003;Takayama et al, 1999;Shao et al, 2006;Landín et al, 2009), ecology (Guégan et al, 1998;Hilbert et al, 2001;Adriaenssens et al, 2004) or agriculture (Huang, 2009 and references therein). Pioneer studies in plant science deal with the use of AI technology to improve and/or optimize biotechnology processes production.…”

Section: Applications Of Neural Network To Plant Biologymentioning

confidence: 99%

Artificial Neural Networks Technology to Model and Predict Plant Biology Process

Gallego¹,

Gago²,

Landín³

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

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Section: Applications Of Neural Network To Plant Biologymentioning

confidence: 99%

Artificial Neural Networks Technology to Model and Predict Plant Biology Process

Gallego¹,

Gago²,

Landín³

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

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“…Responses were used as 4 output layers A set of outputs and causal factors was used as tutorial data (training runs F1-F10) and fed into a computer. Several training sessions were conducted with different numbers of units (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) in the second hidden (radial) layer to determine the optimal GRNN structure. Regression plots were constructed of predicted and observed responses for the 4 test formulations, and slopes and r 2 values were determined.…”

Section: Grnn Structurementioning

confidence: 99%

“…[6][7][8] ANN is a learning system based on a computational technique that can simulate the neurological processing ability of the human brain and can be applied to quantifying a nonlinear relationship between causal factors and pharmaceutical responses by means of iterative training of data obtained from a designed experiment. 9 Matrix systems appear very attractive from the economic as well as from the process development and scale-up points of view in controlled release systems. 10,11 In our earlier studies, 12,13 the influence of various formulation variables on aspirin release from Eudragit matrices was investigated; the ratio of polymer as well as the compression pressure (influencing tablet porosity) were identified as the most important factors affecting drug release from matrix tablets.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2003

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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.

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“…Certain empirical techniques have been reported to improve the convergence of ANN in the global minima. 11,18) The network is trained using different algorithms (Back propagation, Conjugate gradient descent, Quasi-Newton, Levenberg-Marquardt, Quick propagation, Delta-bar-delta etc.). [18][19][20][21] Back propagation learning algorithm is widely used in multilayer feed forward networks.…”

Section: Ann Using Multilayer Perceptrons (Mlp)mentioning

confidence: 99%

“…[8][9][10] ANN is a learning based system on a computational technique that can simulate the neurological processing ability of the human brain and can be applied to quantify a nonlinear relationship between causal factors and pharmaceutical responses by means of iterative training of data obtained from a designed experiment. 11) Metformin HCl is an orally administered biguanide, which is widely used in the management of type-2 diabetes, a common disease that combines defects of both insulin secretion and insulin action.…”

mentioning

confidence: 99%

Optimization of Metformin HCl 500 mg Sustained Release Matrix Tablets Using Artificial Neural Network (ANN) Based on Multilayer Perceptrons (MLP) Model

et al. 2008

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The aim of the present study was to apply the simultaneous optimization method incorporating Artificial Neural Network (ANN) using Multi-layer Perceptron (MLP) model to the development of a metformin HCl 500 mg sustained release matrix tablets with an optimized in vitro release profile. The amounts of HPMC K15M and PVP K30 at three levels (؊1, 0, ؉1) for each were selected as casual factors. In vitro dissolution time profiles at four different sampling times (1 h, 2 h, 4 h and 8 h) were chosen as output variables. 13 kinds of metformin matrix tablets were prepared according to a 2 3 factorial design (central composite) with five extra center points, and their dissolution tests were performed. Commercially available STATISTICA Neural Network software (Stat Soft, Inc., Tulsa, OK, U.S.A.) was used throughout the study. The training process of MLP was completed until a satisfactory value of root square mean (RSM) for the test data was obtained using feed forward back propagation method. The root mean square value for the trained network was 0.000097, which indicated that the optimal MLP model was reached. The optimal tablet formulation based on some predetermined release criteria predicted by MLP was 336 mg of HPMC K15M and 130 mg of PVP K30. Calculated difference ( f 1 2.19) and similarity ( f 2 89.79) factors indicated that there was no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network with MLP, to assist in development of sustained release dosage forms.Key words artificial neural network; multilayer perceptron; metformin HCl; sustained release; matrix tablet Chem. Pharm. Bull. 56(2) 150-155 (2008)

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Artificial Neural Networks: Implications for Pharmaceutical Sciences

Cited by 98 publications

References 40 publications

Artificial Neural Networks Technology to Model and Predict Plant Biology Process

Artificial Neural Networks Technology to Model and Predict Plant Biology Process

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

Optimization of Metformin HCl 500 mg Sustained Release Matrix Tablets Using Artificial Neural Network (ANN) Based on Multilayer Perceptrons (MLP) Model

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