Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Parojčić, Jelena; Đurić, Zorica

doi:10.1016/j.ejps.2009.07.007

Cited by 41 publications

(14 citation statements)

References 27 publications

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“…Every input is analyzed as a function of the previous input. The network remembers past inputs; therefore, the current output is integration of past inputs and current response of the system [ 13 ].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Correction of weights in the dynamic neural network is somewhat more complicated, in comparison to static neural networks. It is possible to use the technique called backpropagation through time where the backpropagated signal is buffered and reversed which enables getting forward and backpropagated signals synchronized in time [ 13 ].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

et al. 2012

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Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

et al. 2012

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“…Water-soluble drugs are mainly released by diffusion of dissolved drug molecules across the gel layer, while poorly water-soluble drugs are more likely to be released through erosion of the gel [14]. The controlling mechanism of drug release from PEO tablets is dependent upon the drug solubility, drug loading, the addition of water-soluble excipients and the molecular weight of the PEOs [15]. …”

Section: Introductionmentioning

confidence: 99%

Evaluation of the drug solubility and rush ageing on drug release performance of various model drugs from the modified release polyethylene oxide matrix tablets

Shojaee

Nokhodchi

Maniruzzaman³

2016

Drug Deliv. and Transl. Res.

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Hydrophilic matrix systems are currently some of the most widely used drug delivery systems for controlled-release oral dosage forms. Amongst a variety of polymers, polyethylene oxide (PEO) is considered an important material used in pharmaceutical formulations. As PEO is sensitive to thermal oxidation, it is susceptible to free radical oxidative attack. The aim of this study was to investigate the stability of PEO based formulations containing different model drugs with different water solubility, namely propranolol HCl, theophylline and zonisamide. Both polyox matrices 750 and 303 grade were used as model carriers for the manufacture of tablets stored at 40 °C. The results of the present study suggest that the drug release from the matrix was affected by the length of storage conditions, solubility of drugs and the molecular weight of the polymers. Generally, increased drug release rates were prevalent in soluble drug formulations (propranolol) when stored at the elevated temperature (40 °C). In contrast, it was not observed with semi soluble (theophylline) and poorly soluble (zonisamide) drugs especially when formulated with PEO 303 polymer. This indicates that the main parameters controlling the drug release from fresh polyox matrices are the solubility of the drug in the dissolution medium and the molecular weight of the polymer. DSC traces indicated that that there was a big difference in the enthalpy and melting points of fresh and aged PEO samples containing propranolol, whereas the melting point of the aged polyox samples containing theophylline and zonisamide was unaffected. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (doi:10.1007/s13346-016-0344-5) contains supplementary material, which is available to authorized users.

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“…Obtained diagrams showed that formulations with SDMR from 0.4 to 0.62 and DS loading from 97 to 150 mg/g exhibited prolonged drug release during 8 h. So far, DS release from various matrix tablets was optimized using ANNs models. [34][35][36][37] Additionally, the results reported in this paper showed that MLP network can be successfully used for prediction of prolonged DS release based on knowledge of SDMR in the drug/modified zeolite physical mixtures, which is relevant for suitable DS release during 8 h.…”

Section: Application Of Ann Analysis Training and Testingmentioning

confidence: 91%

Application of Artificial Neural Networks in Prediction of Diclofenac Sodium Release From Drug-Modified Zeolites Physical Mixtures and Antiedematous Activity Assessment

Krajišnik

Stepanović-Petrović

Tomić

et al. 2014

Journal of Pharmaceutical Sciences

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Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets

Cited by 41 publications

References 27 publications

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

Evaluation of the drug solubility and rush ageing on drug release performance of various model drugs from the modified release polyethylene oxide matrix tablets

Application of Artificial Neural Networks in Prediction of Diclofenac Sodium Release From Drug-Modified Zeolites Physical Mixtures and Antiedematous Activity Assessment

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