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

Authorized Users Only
2014
Authors
Krajišnik, Danina
Stepanović-Petrović, Radica
Tomić, Maja
Micov, Ana
Ibrić, Svetlana
Milić, Jela
Article (Published version)
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Abstract
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-response
Source:
Journal of Pharmaceutical Sciences, 2014, 103, 4, 1085-1094
Publisher:
  • Wiley-Blackwell, Hoboken
Projects:
  • Development of micro- and nanosystems as carriers for drugs with anti-inflammatory effect and methods for their characterization (RS-34031)

DOI: 10.1002/jps.23869

ISSN: 0022-3549

PubMed: 24496922

WoS: 000332778100007

Scopus: 2-s2.0-84896393153
[ Google Scholar ]
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URI
http://farfar.pharmacy.bg.ac.rs/handle/123456789/2149
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  • Radovi istraživača / Researchers’ publications
Institution
Pharmacy

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