Modeling and optimization of a pharmaceutical formulation system using radial basis function network

International Journal of Neural Systems

Volumn 19, Issue 2, 2009, Pages 127-136

  Anand, P ^a   Siva Prasad, B V N ^a   Venkateswarlu, Ch ^a  

^a INDIAN INSTITUTE OF CHEMICAL TECHNOLOGY   (India)

Author keywords

Multiobjective optimization; Multiple regression; Pharmaceutical formulation; Radial basis function network

Indexed keywords

MATHEMATICAL RELATION; MODELING AND OPTIMIZATION; MULTIPLE REGRESSION; MULTIPLE REGRESSIONS; NETWORK PARAMETERS; NOVEL METHODS; OPTIMAL FORMULATION; PHARMACEUTICAL FORMULATION; PROCESS VARIABLES; QUANTITATIVE MODELS; RESPONSE SURFACE METHOD; SELF-ORGANIZING LEARNING; SIMULATION RESULT;

ATTITUDE CONTROL; DRUG PRODUCTS; EDUCATION; EQUIPMENT TESTING; LEARNING ALGORITHMS; METAL ANALYSIS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

MULTIOBJECTIVE OPTIMIZATION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; DELAYED RELEASE FORMULATION; HUMAN; METHODOLOGY; NONLINEAR SYSTEM; PHARMACEUTICS; REGRESSION ANALYSIS;

CHEMISTRY, PHARMACEUTICAL; DELAYED-ACTION PREPARATIONS; HUMANS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; REGRESSION ANALYSIS; TECHNOLOGY, PHARMACEUTICAL;

EID: 67650081522     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065709001896     Document Type: Article

References (21)

1. F. P. Duta, F. P. de Franca and L. M. Almeida Lopes, Optimization of culture conditions for epoxy saccharides production in rhizobeum sp using the response surface method, Electronic Journal of Biotechnology 9(4) (2006) 391-397.
2. A. I. Khuri and M. Conlon, Simultaneous optimization of multiple responses predicted by polynomial regression functions, Technometrics 23 (1981) 363-375.
3. B. Muir, W. A. Carrick and D. B. Cooper, Application of central composite design in the optimization of thermal desorption parameters for the trace level determination of the chemical warfare agent chloropicrin, Analyst 127 (2002) 1198-1202.
4. M. Shachamn and N. Brauner, Considering experimental data in construction of optimal regression models, Chemical Engineering and Processing 38 (1999) 477-486.
5. K. Takayama and T. Nagai, Simultaneous optimization for several characteristics concerning percutaneous absorption and skin damage of ketoprofen hydrogels containing D-limonene, International Journal of Pharmaceutics 74 (1991) 115-126.
6. M. F. Anjum, I. Tasadduq and K. Al-Sultan, Response surface methodology: A neural network approach, European Journal of Operations Research 101 (1997) 65-73.
7. S. Ibric, M. Jovanovic, Z. Djuric, J. Parajecic, S. D. Petrovic, L. Solomun and B. Stupar, Artificial neural networks in the modeling and optimization of aspirin extended release tablets with eudragit L 100 as matrix substance, AAPS PharmSciTech 4(1) (2003) 1-9.
8. M. M. Leane, I. Cumming and O. I. Corrigan, The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the In Vitro dissolution of sustained release minitablets, AAPS PharmSciTech 4(2) (2003) 1-12.
9. K. K. Peh, C. P. Lim, S. S. Quek and K. H. Khoh, Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor, Pharmaceutical Research 17(11) (2000) 1384-1388.
10. K. Takayama, M. Fuziwaka and T. Nagai, Artificial neural net as a novel method to optimize pharmaceutical formulations, Pharmaceutical Research 16 (1991) 1-6.
11. H. Adeli and H. S. Park, Optimization of space structures by neural dynamics, Neural Networks 8 (1995) 769-781.
12. H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, Journal of Structural Engineering, ASCE 123 (1997) 880-888.
13. S. Chen, S. A. Billings, C. F. N. Covan and P. M. Grant, Nonlinear systems identification using radial basis functions, International Journal of Systems Science 21(12) (1990) 2513-2539.
14. M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris and D. M. Hummels, On the training of radial basis function classifiers, Neural Networks 5 (1992) 595-603.
15. S. Lee and R. M. Kil, A Gaussian potential function network with hierarchically self organizing learning, Neural Networks 4 (1991) 207-224.
16. M. R. Warnes, J. Glassey, G. A. Montague and B. Kara, Application of radial basis function and feedforward artificial neural networks to the Escheri-cia coli fermentation process, Neuro Computing 20 (1998) 67-82.
17. Ch. Venkateswarlu and K. Venkat Rao, Dynamic recurrent radial basis function network model predictive control of unstable nonlinear processes, Chemical Engineering Science 60 (2005) 6718-6732.
18. A. Karim and H. Adeli, Radial basis function neural network for work zone capacity and queue estimation, Journal of Transportation Engineering, ASCE 129 (2003) 494-503.
19. R. V. Mayorga and J. Carrera, A radial basis function network approach for the computational of inverse continuous time variant functions, International Journal of Neural Systems 17 (2007) 149-160.
20. J. Takahara, K. Takayama and T. Nagai, Multiobjec-tive simultaneous optimization technique based on an artificial neural network in sustained release formulations, Journal of Controlled Release 49 (1997) 11-20.
21. S. Akhnanazarova and V. Kafarov, Experimental optimization in Chemistry and Chemical Engineering, Mir Publishers, Moscow, Russia (1982).