Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations

European Journal of Pharmaceutical Sciences

Volumn 38, Issue 4, 2009, Pages 325-331

  Landín, Mariana ^a   Rowe, R C ^b   York, P ^b  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

^b UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

Data mining; Design of experiments; Direct compression tablets; Neurofuzzy logic; Response surface methodology

Indexed keywords

MICROCRYSTALLINE CELLULOSE; PREDNISONE;

ACCURACY; ARTICLE; ARTIFICIAL INTELLIGENCE; CONTROLLED STUDY; DRUG FORMULATION; ERROR; EXPERIMENTAL DESIGN; FUZZY SYSTEM; MULTIPLE REGRESSION; PREDICTION; PRIORITY JOURNAL; STATISTICAL ANALYSIS; TABLET COMPRESSION;

CHEMISTRY, PHARMACEUTICAL; COMPRESSIVE STRENGTH; FUZZY LOGIC; PHARMACEUTICAL PREPARATIONS; RESEARCH DESIGN; TECHNOLOGY, PHARMACEUTICAL;

EID: 70349838064     PISSN: 09280987     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejps.2009.08.004     Document Type: Article

References (24)

1. Achanta A.S., Kowalski J.G., and Rhodes C.T. Artificial neural network: implications for pharmaceutical sciences. Drug Dev. Ind. Pharm. 21 (1995) 119-155
2. Arulsudar N., Subramanian N., and Murthy R.S.R. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leprolide acetate loaded liposomes. J. Pharm. Pharmaceut. Sci. 8 (2005) 243-258
3. BMDP Statistical Software, 1983. W.J. Dixon. University of Califormia Press, Berkeley.
4. Cochram W.G., and Cox G.W. Experimental Designs. 2nd ed. (1957), J. Willey, London
5. Colbourn E., and Rowe R.C. In: Swarbrick J., and Boylan J.C. (Eds). Neural Computing and Pharmaceutical Formulation. 3rd ed. (2005), Encyclopedia of Pharmaceutical Technology
6. Colbourn E. Neural computing: enable intelligent formulations. Pharm. Technol. Suppl. (2003) 16-20
7. Culzoni M.J., and Goicoechea H.C. Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks. Anal. Bioanal. Chem. 389 (2007) 2217-2225
8. de Matas M., Shao Q., Richardson C.H., and Chrystyn H. Evaluation of in-vitro in-vivo correlations for dry powder inhaler delivery using artificial neural networks. Eur. J. Pharm. Sci. 33 (2008) 80-90
9. de Matas M., Shao Q., Silkstone V., and Chrystyn H. Evaluation of an in-vitro in-vivo correlation for nebulizer delivery using artificial neural networks. J. Pharm. Sci. 96 (2007) 3293-3303
10. Fan T., Takayama K., Hattori Y., and Maitani Y. Formulation optimization of paclitaxel carried by PEGylated emulsions based on artificial neural network. Pharm. Res. 21 (2004) 1692-1697
11. Goicoechea H.C., Collado M.S., Satuf M.L., and Olivieri A.C. Complementary use of partial least-squares and artificial neural networks for the non-linear spectrophotometric analysis of pharmaceutical samples. Anal. Bioanal. Chem. 374 (2002) 460-465
12. Hussain A.S., Yu X., and Johnson R.D. Application of neural computing in pharmaceutical product development. Pharm. Res. 8 (1991) 1248-1252
13. Ibrić S., Jovanović M., Djurić Z., Parojčić, Petrovič S.D., Solomun L., and Stupar B. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance. AAPS Pharm. Sci. Technol. 4 (2003) 1-9
14. Kavli T., and Weyer E. ASMOD (adaptive spline modelling of observation data)-some theoretical and experimental results. IEE Colloquium (Digest) 136 (1994) 1-7
15. Lewis G.A., Mathieu D., and Phan-Tan-Luu R. Pharmaceutical and Experimental Design (1999), Marcel Dekker, New York
16. Plumb A.P., Rowe R.C., York P., and Brown M. Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithm. Eur. J. Pharm. Sci. 25 (2005) 395-405
17. Shao Q., Rowe R.C., and York P. Comparison of neurofuzzy logic and neural networks in modeling experimental data of an immediate release tablet formulation. Eur. J. Pharm. Sci. 28 (2006) 394-404
18. Shao Q., Rowe R.C., and York P. Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation. Eur. J. Pharm. Sci. 31 (2007) 129-136
19. Singh B., Chakkal S.K., and Ahuja N. Formulation and optimization of controlled release mucoadhesive tablets of atenolol using response surface methodology. AAPS Pharm. Sci. Technol. 7 (2006) E1-E10 (Article 3)
20. Singh B., Dahiya M., Saharan V., and Ahuja N. Optimizing drug delivery systems using systematic "design of experiments". Part II. Retrospect and prospects. Crit. Rev. Therap. Drug Carrier Syst. 22 (2005) 215-293
21. Singh B., Kumar R., and Ahuja N. Optimizing drug delivery systems using systematic design of experiments. Part I. Fundamental aspects. Crit. Rev. Therap. Drug Carrier Syst. 22 (2004) 27-105
22. Subramanian N., Yajnik A., and Murthy R.S.S. Artificial neural network as an alternative to multiple resgression analysis in optimizing formulation parameters of cytarabine liposomes. AAPS Pharm. Sci. Technol. 5 (2004) 1-9
23. Takayama K., Fujikawa M., and Nagai T. Artificial neural network as a novel method to optimize pharmaceutical formulations. Pharm. Res. 16 (1999) 1-6
24. Wehrlé R., and Stamm A. Statistical tools for process control and quality improvement in the pharmaceutical industry. Drug Dev. Ind. Pharm. 20 (1994) 141-146