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Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees

Само за регистроване кориснике
2012
Аутори
Petrović, Jelena
Ibrić, Svetlana
Betz, Gabriele
Đurić, Zorica
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документу
Апстракт
The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo... simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.

Кључне речи:
Matrix tablets / Neural networks / Decision trees / Drug release modeling / Controlled release
Извор:
International Journal of Pharmaceutics, 2012, 428, 1-2, 57-67
Издавач:
  • Elsevier Science BV, Amsterdam
Пројекти:
  • Развој производа и технологија које обезбеђују жељено ослобађање лековитих супстанци из чврстих фармацеутских облика (RS-34007)

DOI: 10.1016/j.ijpharm.2012.02.031

ISSN: 0378-5173

PubMed: 22402474

WoS: 000302364600008

Scopus: 2-s2.0-84859428478
[ Google Scholar ]
16
12
URI
http://farfar.pharmacy.bg.ac.rs/handle/123456789/1705
Колекције
  • Radovi istraživača / Researchers’ publications
Институција
Pharmacy

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