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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2014
Authors
Krajišnik, Danina
Stepanović-Petrović, Radica

Tomić, Maja

Micov, Ana

Ibrić, Svetlana

Milić, Jela
Article (Published version)

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In this study, utilization of artificial neural network (ANN) models [staticmultilayer perceptron (MLP) and generalized regression neural networks and dynamicgamma one-layer network and recurrent one-layer network] for prediction of diclofenac sodium (DS) release from drug-cationic surfactant-modified zeolites physical mixtures comprising different surfactant/drug molar ratio (0.2-2.5) was performed. The inputs for ANNs trainings were surfactant/drug molar ratios, that is, drug loadings in the drug-modified zeolite mixtures, whereas the outputs were percents of drug release in predetermined time points during drug release test (8 h). The obtained results revealed that MLP showed the highest correlation between experimental and predicted drug release. The safety of both natural and cationic surfactant-modified zeolite as a potential excipient was confirmed in an acute toxicity testing during 72 h. DS (1.5, 5, 10, mg/kg, p.o.) as well as DS-modified zeolites mixtures produced a significa...nt dose-dependent reduction of the rat paw edema induced by proinflammatory agent carrageenan. DS antiedematous effect was intensified and prolonged significantly by modified zeolite. These results could suggest the potential improvement in the treatment of inflammation by DS-modified zeolite mixtures.
Keywords:
clinoptilolite / cationic surfactant / adsorption / excipient / diclofenac sodium / neural networks / in silico modeling / dissolution / antiedematous activity / dose-responseSource:
Journal of Pharmaceutical Sciences, 2014, 103, 4, 1085-1094Publisher:
- Wiley-Blackwell, Hoboken
Projects:
DOI: 10.1002/jps.23869
ISSN: 0022-3549
PubMed: 24496922