Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks

European Journal of Pharmaceutical Sciences

Volumn 44, Issue 3, 2011, Pages 321-331

  Chansanroj, Krisanin ^a   Petrović, Jelena ^b   Ibrić, Svetlana ^b   Betz, Gabriele ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

^b UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

Controlled release; Hydrophilic lipophilic property; Matrix tablet; Neural network; Sucrose esters; Swelling

Indexed keywords

METOPROLOL TARTRATE; SUCROSE ESTER DERIVATIVE;

ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED DRUG RELEASE; HYDROPHILIC LIPOPHILIC BALANCE; HYDROPHILICITY; LEARNING ALGORITHM; LIPOPHILICITY; PRIORITY JOURNAL; TABLET; TABLET POROSITY; TABLET PROPERTY; TABLET SWELLING; TENSILE STRENGTH;

DELAYED-ACTION PREPARATIONS; ESTERS; EXCIPIENTS; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; METOPROLOL; MODELS, CHEMICAL; NEURAL NETWORKS (COMPUTER); POROSITY; SOLUBILITY; SUCROSE; TABLETS; TENSILE STRENGTH;

EID: 80053909625     PISSN: 09280987     EISSN: 18790720     Source Type: Journal    
DOI: 10.1016/j.ejps.2011.08.012     Document Type: Article

References (42)

1. S. Agatonovic-Kustrin, and R. Beresford Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research J. Pharmaceut. Biomed. 22 2000 717 727 (Pubitemid 30193303)
2. P. Barmpalexis, F.I. Kanaze, K. Kachrimanis, and E. Georgarakis Artificial neural networks in the optimization of a nimodipine controlled release tablet formulation Eur. J. Pharm. Biopharm. 74 2010 316 323
3. J. Bourquin, H. Schmidli, P. van Hoogevest, and H. Leuenberger Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development Pharm. Dev. Technol. 2 1997 95 109 (Pubitemid 28252526)
4. J. Bourquin, H. Schmidli, P. van Hoogevest, and H. Leuenberger Application of artificial neural networks (ANN) in the development of solid dosage forms Pharm. Dev. Technol. 2 1997 111 121 (Pubitemid 28252527)
5. J. Bourquin, H. Schmidli, P. van Hoogevest, and H. Leuenberger Advantages of artificial neural networks (ANNs) as alternative modelling 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 (Pubitemid 29056222)
6. G.E.P. Box, and N.R. Draper Empirical Model-building and Response Surfaces 1987 John Wiley and Sons New York
7. W.C. Carpenter, and M.E. Hoffman Selecting the architecture of a class of back-propagation neural networks used as approximators AI EDAM 11 1997 33 44 (Pubitemid 127588584)
8. E. Castellanos Gil, A. Iraizoz Colarte, B. Bataille, F. Brouillet, and I. Caraballo Estimation of the percolation thresholds in ternary lobenzarit disodium-dextran-HPMC hydrophilic matrices tablets: effects of initial porosity Eur. J. Pharm. Sci. 38 2009 312 319
9. K. Chansanroj, and G. Betz Sucrose esters with various hydrophilic-lipophilic properties: novel controlled release agents for oral drug delivery matrix tablets prepared by direct compaction Acta Biomater. 6 2010 3101 3109
10. P. Colombo, R. Bettini, P. Santi, and N.A. Peppas Swellable matrices for controlled drug delivery: gel-layer behaviour, mechanisms and optimal performance Pharm. Sci. Technol. Today 3 2000 198 204 (Pubitemid 30364933)
11. P. Costa, and J.M.S. Lobo Modeling and comparison of dissolution profiles Eur. J. Pharm. Sci. 13 2001 123 133 (Pubitemid 32288781)
12. J.T. Fell, and J.M. Newton Determination of tablet strength by diametral compression test J. Pharm. Sci. 59 1970 688 691
13. J.L. Ford, M.H. Rubinstein, and J.E. Hogan Formulation of sustained release promethazine hydrochloride tablets using hydroxypropylmethylcellulose matrices Int. J. Pharm. 24 1985 327 338 (Pubitemid 15062540)
14. J.L. Ford, M.H. Rubinstein, and J.E. Hogan Propranolol hydrochloride and aminophylline release from matrix tablets containing hydroxypropylmethylcellulose Int. J. Pharm. 24 1985 339 350 (Pubitemid 15062541)
15. J.L. Ford, M.H. Rubinstein, F. McCaul, J.E. Hogan, and P.J. Edgar Importance of drug type, tablet shape and added diluents on drug release kinetics from hydroxypropylmethylcellulose matrix tablets Int. J. Pharm. 40 1987 223 234 (Pubitemid 18025827)
16. A.S. Hussain, X.Q. Yu, and R.D. Johnson Application of neural computing in pharmaceutical product development Pharm. Res. 8 1991 1248 1252
17. S. Ibric, M. Jovanovic, Z. Djuric, J. Parojcic, and L. Solomun The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit® RS PO as matrix substance J. Control. Release 82 2002 213 222 (Pubitemid 34880179)
18. K. Kachrimanis, V. Karamyan, and S. Malamataris Artificial neural networks (ANNs) and modeling of powder flow Int. J. Pharm. 250 2003 13 23 (Pubitemid 35447933)
19. N. Laitinen, J. Rantanen, S. Laine, O. Antikainen, E. Räsänen, S. Airaksinen, and J. Yliruusi Visualization of particle size and shape distributions using self-organizing maps Chemometr. Intell. Lab. 62 2002 47 60 (Pubitemid 34234818)
20. W.D. Lindner, and B.C. Lippold Drug release from hydrocolloid embedding with high or low susceptibility to hydrodynamic stress Pharm. Res. 12 1995 1781 1785 (Pubitemid 26004702)
21. T. Martinentz, and K. Schulten A "neural-gas" network learns topologies T. Kohonen, K. Mäkisara, O. Simula, J. Kangas, Artificial Neural Networks 1991 Elsevier Science Publishers North Holland 397 402
22. R. Mäki, E. Suihko, O. Korhonen, H. Pitkänen, R. Niemi, M. Lehtonen, and J. Ketolainen Controlled release of saccharides from matrix tablets Eur. J. Pharm. Biopharm. 62 2006 163 170 (Pubitemid 43143268)
23. Mitsubishi-Kagaku Foods Corporation Ryoto® Sugar Ester Technical Information 2003 Mitsubishi Kagaku Foods Corporation Tokyo
24. J.D. Ntawukulilyayo, C. Demuynck, and J.P. Remon Microcrystalline cellulose-sucrose esters as tablet matrix forming agents Int. J. Pharm. 121 1995 205 210
25. N.A. Peppas Analysis of fickian and non-fickian release from polymers Pharm. Acta Helv. 60 1985 110 111
26. C. Petrone, G. Hall, M. Langman, and M.J. Filiaggi Compaction strategies for modifying the drug delivery capabilities of gelled calcium polyphosphate matrices Acta Biomater. 4 2008 403 413 (Pubitemid 351218097)
27. J. Petrovic, S. Ibric, G. Betz, J. Parojcic, and Z. uric Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets Eur. J. Pharm. Sci. 38 2009 172 180
28. P.L. Ritger, and N.A. Peppas A simple equation for description of solute release I. Fickian and non-Fickian release from non-swellable devices in the form of slabs, spheres, cylinders or discs J. Control. Release 5 1987 23 36 (Pubitemid 17106643)
29. P.L. Ritger, and N.A. Peppas A simple equation for description of solute release II. Fickian and anomalous release from swellable devices J. Control. Release 5 1987 37 42 (Pubitemid 17106644)
30. F. Siepmann, K. Eckart, A. Maschke, K. Kolter, and J. Siepmann Modeling drug release from PVAc/PVP matrix tablets J. Control. Release 141 2010 216 222
31. Y. Sun, Y. Peng, Y. Chen, and A.J. Shukla Application of artificial neural networks in the design of controlled release drug delivery systems Adv. Drug Deliver. Rev. 55 2003 1201 1215 (Pubitemid 37087924)
32. F. Tajarobi, S. Abrahmsén-Alami, A.S. Carlsson, and A. Larsson Simultaneous probing of swelling, erosion and dissolution by NMR-microimaging - effect of solubility of additives on HPMC matrix tablets Eur. J. Pharm. Sci. 37 2009 89 97
33. J. Takahara, K. Takayama, and T. Nagai Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations J. Control. Release 49 1997 11 20 (Pubitemid 27417896)
34. K. Takayama, M. Fujikawa, Y. Obata, and M. Morishita Neural network based optimization of drug formulations Adv. Drug Deliver. Rev. 55 2003 1217 1231 (Pubitemid 37087925)
35. K. Takayama, A. Morva, M. Fujikawa, Y. Hattori, Y. Obata, and T. Nagai Formula optimization of theophylline controlled-release tablet based on artificial neural networks J. Control. Release 68 2000 175 186 (Pubitemid 30488895)
36. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, 1997. Guidance for industry: Dissolution testing of immediate release solid oral dosage forms, Available from: < http://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ucm070237.pdf > (accessed 14.12.10).
37. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research, 2009. Guidance for industry: Q8 (R2) Pharmaceutical development, Available from: < http://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ucm073507.pdf > (accessed 14.12.10).
38. B. Van Veen, J. Pajander, K. Zuurman, R. Lappalainen, A. Poso, H.W. Frijlink, and J. Ketolainen The effect of powder blend and tablet structure on drug release mechanisms of hydrophobic starch acetate matrix tablets Eur. J. Pharm. Biopharm. 61 2005 149 157 (Pubitemid 41427623)
39. W.I. Wilson, Y. Peng, and L.L. Augsburger Comparison of statistical analysis and Bayesian networks in the evaluation of dissolution performance of BCS Class II model drugs J. Pharm. Sci. 94 2005 2764 2776 (Pubitemid 43265398)
40. J. Zupan, and J. Gasteiger Neural Networks in Chemistry and Drug Design 1999 Willey-VCH Weinheim
41. D. Zupani Bozi, F. Vreer, and F. Kozjek Optimization of diclofenac sodium dissolution from sustained release formulations using an artificial neural network Eur. J. Pharm. Sci. 5 1997 163 169 (Pubitemid 27223336)
42. J.M. Zurada Introduction to Artificial Neural Systems 1992 West Publishing Company MN