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

European Journal of Pharmaceutical Sciences

Volumn 38, Issue 2, 2009, Pages 172-180

  Petrović, Jelena ^a   Ibrić, Svetlana ^a   Betz, Gabriele ^b   Parojčić, Jelena ^a   Durić, Zorica ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

^b UNIVERSITY OF BASEL   (Switzerland)

Author keywords

Controlled release; Drug release modeling; Dynamic neural networks; Polyethylene oxides (PEOs); Time series

Indexed keywords

DICLOFENAC; MACROGOL;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PREDICTION; CONTROLLED STUDY; DRUG RELEASE; DRUG SOLUBILITY; MOLECULAR WEIGHT; MONTE CARLO METHOD; PRIORITY JOURNAL; TABLET COMPRESSION; TABLET FORMULATION; TABLET MATRIX;

ANTI-INFLAMMATORY AGENTS, NON-STEROIDAL; DICLOFENAC; NEURAL NETWORKS (COMPUTER); POLYETHYLENE GLYCOLS; TABLETS;

EID: 68849106502     PISSN: 09280987     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejps.2009.07.007     Document Type: Article

References (32)

1. Aussem A. Dynamical recurrent neural networks towards prediction and modeling of dynamical systems. Neurocomputing 28 (1999) 207-232
2. Becerikli Y., and Oysal Y. Modeling and prediction with a class of time delay dynamic neural networks. Appl. Soft Comput. 7 (2007) 1164-1169
3. Bourquin J., Schmidli H., van Hoogevest P., and Leuenberger H. Comparison of artificial neural networks (ANN) with classical modeling techniques using different experimental designs and data from a galenical study on a solid dosage form. Eur. J. Pharm. Sci. 6 (1998) 287-300
4. Bourquin J., Schmidli H., van Hoogevest P., and Leuenberger H. Advantages of Artificial Neural Networks (ANNs) as alternative modeling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form. Eur. J. Pharm. Sci. 7 (1998) 5-16
5. Bourquin J., Schmidli H., van Hoogevest P., and Leuenberger H. Pitfalls of artificial neural networks (ANN) modeling technique for data sets containing outlier measurements using a study on mixture properties of a direct compressed dosage form. Eur. J. Pharm. Sci. 7 (1998) 17-28
6. Chen Y., McCall T.W., Baichwal A.R., and Meyer M.C. The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage forms. J. Control. Release 59 (1999) 33-41
7. Chow S.C., and Ki F.Y.C. Statistical comparison between dissolution profiles of drug product. J. Biopharm. Stat. 7 (1997) 241-258
8. De Gooijer J.G., and Hyndman R.J. 25 years of time series forecasting. Int. J. Forecasting 22 (2006) 443-473
9. De Vries B., and Principe J.C. Short term memory structures for dynamic neural networks. Conference Record of The Twenty-Sixth Asilomar Conference on Signals. Syst. Comput. 2 (1992) 26-28
10. Dimitrov M., and Lambov N. Study of Verapamil hydrochloride release from compressed hydrophilic Polyox-Wsr tablets. Int. J. Pharm. 189 (1999) 105-111
11. Ghiassi M., and Nangoy S. A dynamic artificial neural network model for forecasting nonlinear processes. Comput. Ind. Eng. 57 (2009) 287-297
12. Ghiassi M., Saidane H., and Zimbra D.K. A dynamic artificial neural network model for forecasting time series events. Int. J. Forecasting 21 (2005) 341-362
13. Goh W.Y., Lim C.P., Peh K.K., and Subari K. Application of a recurrent neural network to prediction of drug dissolution profiles. Neural Comput. Appl. 10 (2002) 311-317
14. Haykin S. Neural Networks-A Comprehensive Foundation (1999), Prentice-Hall, New Jersey
15. Hussain A.S., Yu X.Q., and Johnson R.D. Application of neural computing in pharmaceutical product development. Pharm. Res. 8 (1991) 1248-1252
16. ® RS PO as matrix substance. J. Control. Release 82 (2002) 213-222
17. ® tablets. Drug Dev. Ind. Pharm. 24 (1998) 645-651
18. Murtoniemi E., Yliruusi J., Kinnunen P., Merkku P., and Leiviskä K. The advantages by the use of neural networks in modeling the fluidized bed granulation process. Int. J. Pharm. 108 (1994) 155-164
19. Panerai R.B., Chacon M., Pereira R., and Evans D.H. Neural network modeling of dynamic cerebral auto regulation: assessment and comparison with established methods. Med. Eng. Phys. 26 (2004) 43-52
20. Peh K.K., Lim C.P., Quek S.S., and Khoh K.H. Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor. Pharm. Res. 17 (2000) 1384-1388
21. Petrovic J., Jockovic J., Ibric S., Parojcic J., and Djuric Z. Mathematical modeling of diclofenac sodium's release from polyethylene oxide matrices. Proceedings of the Tenth European Symposium on Controlled Drug Delivery. J. Control. Release 132 (2008) e44-e45
22. Principe J.C., Jyh-Ming K., and Celebi S. An analysis of the gamma memory in dynamic neural networks. IEEE Trans. Neural Networks 5 (1994) 331-337
23. Reis M.A.A., Sinisterra R.D., and Belchior J.C. An alternative approach based on artificial neural networks to study controlled drug release. J. Pharm. Sci. 93 (2004) 418-430
24. Robert C.P., and Casella G. Monte Carlo Statistical Methods (2004), Springer-Verlag, New York
25. Rowe R.C., and Roberts R.J. Artificial intelligence in pharmaceutical product formulation: neural computing and emerging technologies. PSTT 1 (1998) 200-205
26. Samani S.M., Montaseri H., and Kazemi A. The effect of polymer blends on the release profiles of diclofenac sodium from matrices. Eur. J. Pharm. Biopharm. 55 (2003) 351-355
27. Samarasinghe S. Neural Networks for Applied Sciences and Engineering (2006), Auerbach Publications, New York
28. Shaw A.M., Doyle III F.J., and Schwaber J.S. A dynamic neural network approach to nonlinear process modeling. Comput. Chem. Eng. 21 (1997) 371-385
29. Sun Y., Peng Y., Chen Y., and Shukla A. Application of artificial neural networks in the design of controlled release drug delivery systems. Adv. Drug Deliv. Rev. 55 (2003) 1201-1215
30. Turkoglu M., Aydin I., Murray M., and Sakr A. Modeling of a roller-compaction process using neural networks and genetic algorithms. Eur. J. Pharm. Biopharm. 48 (1999) 239-245
31. Yang L., Venkatesh G., and Fassihi R. Characterization of compressibility and compatibility of poly(ethylene oxide) polymers for modified release application by compaction simulator. J. Pharm. Sci. 85 (1996) 1085-1090
32. Zupancic Bozic D., Vrecer F., and Kozjek F. Optimization of diclofenac sodium dissolution from sustained release formulations using an artificial neural network. Eur. J. Pharm. Sci. 5 (1997) 163-169