Application of chemometrics in the optimization of extraction conditions and prescription formulations in pharmaceutical preparations

WANG Li-li; SHI Sen-lin

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Volume 29 Issue 4

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Article Navigation > Journal of Pharmaceutical Practice and Service > 2011 > 29(4): 241-246,255

WANG Li-li, SHI Sen-lin. Application of chemometrics in the optimization of extraction conditions and prescription formulations in pharmaceutical preparations[J]. Journal of Pharmaceutical Practice and Service, 2011, 29(4): 241-246,255.

Citation:

WANG Li-li, SHI Sen-lin. Application of chemometrics in the optimization of extraction conditions and prescription formulations in pharmaceutical preparations[J]. Journal of Pharmaceutical Practice and Service, 2011, 29(4): 241-246,255.

Application of chemometrics in the optimization of extraction conditions and prescription formulations in pharmaceutical preparations

School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou 310053, China

Received Date: 2010-12-27
Rev Recd Date: 2011-03-12

Abstract

Objective To illustrate the importance of chemometrics tools (experimental design combined with modeling and optimization) in the study on pharmaceutical preparations. Methods A wide range of publications associated with extraction condition and prescription optimizations were reviewed. It was found that the combined optimization methodology mentioned above played a very important role in the study of pharmaceutical preparations. Results The methodology based on the combination of experimental design, modeling and optimization, was a very effective tool for solving the complex optimization problems in pharmaceutical studies. With the help of experimental design, all experimental points could be arranged scientifically and reasonably, and thus in turn, only a minimal of experimental points were required; with the assist of mathematical modeling, the immanence disciplines between the experimental results and its corresponding experimental factors could be abstractively expressed and interpreted via mathematical model; with the aid of optimization, the optimal results as well as its corresponding conditions could be finally predicted. Conclusion With the rapid development of pharmaceutical preparations and its integration with other cross-disciplines, there would be more and more complex nonlinear problems which could make a wider use of the proposed combined optimization methodology.
- experimental design,
- modeling,
- optimization,
- extracting condition,
- formulation

References

[1]	Montgomery DC. Design and analysis of experiments [M]. 7th edition. New York: John Wiley & Sons. 2008: 1.
[2]	William RM.Response Surface Methodology [M]. 2nd edition. London: Informa Healthcare. 2007: 858.
[3]	Stephen G. Nonlinear neural networks: Principles, mechanisms, and architectures[J]. Neural Networks, 1988, 1 (1): 17.
[4]	Vapnik VN.Statistical learning theory [M]. 1st edition. New York: John Wiley and Johns. 1998: 1.
[5]	邢文训, 谢金星. 现代优化计算方法[M]. 第2版. 北京: 清华大学出版社. 2005: 1.
[6]	Jalali-Heravi M, Parastar H, Ebrahimi-Najafabadi H. Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography-mass spectrometry analysis[J]. J Chromatogr A, 2009, 1216 (33): 6088.
[7]	Chang Y, Liu B, Shen B. Orthogonal array design for the optimization of supercritical fluid extraction of baicalin from roots of Scutellaria baicalensis Georgi[J]. J Sep Sci, 2007, 30 (10): 1568.
[8]	Fang Q, Yeung HW, Leung HW, et al. Micelle-mediated extraction and preconcentration of ginsenosides from Chinese herbal medicine[J]. J Chromatogr A, 2000, 904 (1): 47.
[9]	Cha K H, Lee HJ, Koo SY, et al. Optimization of pressurized liquid extraction of carotenoids and chlorophylls from Chlorella vulgaris[J]. J Agric Food Chem, 2010, 58 (2): 793.
[10]	杨铭, 余敏英, 史秀峰, 等. BP神经网络结合遗传算法多目标优化秦皮提取工艺的研究[J]. 中国中药杂志, 2008, 33 (22): 2622.
[11]	陈雯, 杜守颖, 陆洋. 多指标正交试验优选仙灵骨葆胶囊中丹参、补骨脂提取工艺[J]. 中国中药杂志, 2009, 34 (14): 1792.
[12]	黄春青, 林亚平, 靳凤云, 等. 均匀设计法结合药效学指标优选金铁锁提取工艺[J]. 中国中药杂志, 2008, 33 (15): 1817.
[13]	Pasqualoto K F, Teofilo R F, Guterres M, et al. A study of physicochemical and biopharmaceutical properties of amoxicillin tablets using full factorial design and PCA biplot[J]. Anal Chim Acta, 2007, 595 (1-2): 216.
[14]	Khanvilkar K H, Huang Y, Moore A D. Influence of hydroxypropyl methylcellulose mixture, apparent viscosity, and tablet hardness on drug release using a 2(3) full factorial design[J]. Drug Dev Ind Pharm, 2002, 28 (5): 601.
[15]	Weon KY, Lee KT, Seo SH. Optimization study on the formulation of roxithromycin dispersible tablet using experimental design[J]. Arch Pharm Res, 2000, 23 (5): 507.
[16]	Huang YB, Tsai YH, Yang WC, et al. Once-daily propranolol extended-release tablet dosage form: formulation design and in vitro/in vivo investigation[J]. Eur J Pharm Biopharm, 2004, 58 (3): 607.
[17]	Ibric S, Jovanovic M, Djuric Z, et al. 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]. J Control Release, 2002, 82 (2-3): 213.
[18]	Mandal U, Gowda V, Ghosh A, et al. Optimization of metformin HCl 500 mg sustained release matrix tablets using Artificial Neural Network (ANN) based on Multilayer Perceptrons (MLP) model[J]. Chem Pharm Bull (Tokyo), 2008, 56 (2): 150.
[19]	Tiwari S, Singh S, Rawat M, et al. L(9) orthogonal design assisted formulation and evaluation of chitosan-based buccoadhesive films of miconazole nitrate[J]. Curr Drug Deliv, 2009, 6 (3): 305.
[20]	Gohel MC, Parikh RK, Aghara PY, et al. Application of simplex lattice design and desirability function for the formulation development of mouth dissolving film of salbutamol sulphate[J]. Curr Drug Deliv, 2009, 6 (5): 486.
[21]	Subramanian N, Yajnik A, Murthy RS. Artificial neural network as an alternative to multiple regression analysis in optimizing formulation parameters of cytarabine liposomes[J]. AAPS PharmSciTech, 2004, 5 (1): E4.
[22]	Xiong Y, Guo D, Wang L, et al. Development of nobiliside A loaded liposomal formulation using response surface methodology[J]. Int J Pharm, 2009, 371 (1-2): 197.
[23]	Arulsudar N, Subramanian N, Muthy RS. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes[J]. J Pharm Sci, 2005, 8 (2): 243.
[24]	Varshosaz J, GhaffariS, Khoshayand MR, et al. Development and optimization of solid lipid nanoparticles of amikacin by central composite design[J]. J Liposome Res, 2010, 20 (2): 97.
[25]	Shah M, Pathak K. Development and Statistical Optimization of Solid Lipid Nanoparticles of Simvastatin by Using 2(3) Full-Factorial Design[J]. AAPS Pharm Sci Tech, 2010, 11(2): 489.
[26]	Boldhane SP, Kuchekar BS. Gastroretentive drug delivery of metformin hydrochloride: formulation and in vitro evaluation using 3(2) full factorial design[J]. Curr Drug Deliv, 2009, 6 (5): 477.
[27]	Swain K, Pattnaik S, Mallick S, et al. Influence of hydroxypropyl methylcellulose on drug release pattern of a gastroretentive floating drug delivery system using a 3(2) full factorial design [J]. Pharm Dev Technol, 2009, 14 (2): 193.

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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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Application of chemometrics in the optimization of extraction conditions and prescription formulations in pharmaceutical preparations

School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou 310053, China

Received Date: 2010-12-27

Rev Recd Date: 2011-03-12

Key words:

Abstract: Objective To illustrate the importance of chemometrics tools (experimental design combined with modeling and optimization) in the study on pharmaceutical preparations. Methods A wide range of publications associated with extraction condition and prescription optimizations were reviewed. It was found that the combined optimization methodology mentioned above played a very important role in the study of pharmaceutical preparations. Results The methodology based on the combination of experimental design, modeling and optimization, was a very effective tool for solving the complex optimization problems in pharmaceutical studies. With the help of experimental design, all experimental points could be arranged scientifically and reasonably, and thus in turn, only a minimal of experimental points were required; with the assist of mathematical modeling, the immanence disciplines between the experimental results and its corresponding experimental factors could be abstractively expressed and interpreted via mathematical model; with the aid of optimization, the optimal results as well as its corresponding conditions could be finally predicted. Conclusion With the rapid development of pharmaceutical preparations and its integration with other cross-disciplines, there would be more and more complex nonlinear problems which could make a wider use of the proposed combined optimization methodology.

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Reference (27)

[1]	Montgomery DC. Design and analysis of experiments [M]. 7th edition. New York: John Wiley & Sons. 2008: 1.
[2]	William RM.Response Surface Methodology [M]. 2nd edition. London: Informa Healthcare. 2007: 858.
[3]	Stephen G. Nonlinear neural networks: Principles, mechanisms, and architectures[J]. Neural Networks, 1988, 1 (1): 17.
[4]	Vapnik VN.Statistical learning theory [M]. 1st edition. New York: John Wiley and Johns. 1998: 1.
[5]	邢文训, 谢金星. 现代优化计算方法[M]. 第2版. 北京: 清华大学出版社. 2005: 1.
[6]	Jalali-Heravi M, Parastar H, Ebrahimi-Najafabadi H. Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography-mass spectrometry analysis[J]. J Chromatogr A, 2009, 1216 (33): 6088.
[7]	Chang Y, Liu B, Shen B. Orthogonal array design for the optimization of supercritical fluid extraction of baicalin from roots of Scutellaria baicalensis Georgi[J]. J Sep Sci, 2007, 30 (10): 1568.
[8]	Fang Q, Yeung HW, Leung HW, et al. Micelle-mediated extraction and preconcentration of ginsenosides from Chinese herbal medicine[J]. J Chromatogr A, 2000, 904 (1): 47.
[9]	Cha K H, Lee HJ, Koo SY, et al. Optimization of pressurized liquid extraction of carotenoids and chlorophylls from Chlorella vulgaris[J]. J Agric Food Chem, 2010, 58 (2): 793.
[10]	杨铭, 余敏英, 史秀峰, 等. BP神经网络结合遗传算法多目标优化秦皮提取工艺的研究[J]. 中国中药杂志, 2008, 33 (22): 2622.
[11]	陈雯, 杜守颖, 陆洋. 多指标正交试验优选仙灵骨葆胶囊中丹参、补骨脂提取工艺[J]. 中国中药杂志, 2009, 34 (14): 1792.
[12]	黄春青, 林亚平, 靳凤云, 等. 均匀设计法结合药效学指标优选金铁锁提取工艺[J]. 中国中药杂志, 2008, 33 (15): 1817.
[13]	Pasqualoto K F, Teofilo R F, Guterres M, et al. A study of physicochemical and biopharmaceutical properties of amoxicillin tablets using full factorial design and PCA biplot[J]. Anal Chim Acta, 2007, 595 (1-2): 216.
[14]	Khanvilkar K H, Huang Y, Moore A D. Influence of hydroxypropyl methylcellulose mixture, apparent viscosity, and tablet hardness on drug release using a 2(3) full factorial design[J]. Drug Dev Ind Pharm, 2002, 28 (5): 601.
[15]	Weon KY, Lee KT, Seo SH. Optimization study on the formulation of roxithromycin dispersible tablet using experimental design[J]. Arch Pharm Res, 2000, 23 (5): 507.
[16]	Huang YB, Tsai YH, Yang WC, et al. Once-daily propranolol extended-release tablet dosage form: formulation design and in vitro/in vivo investigation[J]. Eur J Pharm Biopharm, 2004, 58 (3): 607.
[17]	Ibric S, Jovanovic M, Djuric Z, et al. 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]. J Control Release, 2002, 82 (2-3): 213.
[18]	Mandal U, Gowda V, Ghosh A, et al. Optimization of metformin HCl 500 mg sustained release matrix tablets using Artificial Neural Network (ANN) based on Multilayer Perceptrons (MLP) model[J]. Chem Pharm Bull (Tokyo), 2008, 56 (2): 150.
[19]	Tiwari S, Singh S, Rawat M, et al. L(9) orthogonal design assisted formulation and evaluation of chitosan-based buccoadhesive films of miconazole nitrate[J]. Curr Drug Deliv, 2009, 6 (3): 305.
[20]	Gohel MC, Parikh RK, Aghara PY, et al. Application of simplex lattice design and desirability function for the formulation development of mouth dissolving film of salbutamol sulphate[J]. Curr Drug Deliv, 2009, 6 (5): 486.
[21]	Subramanian N, Yajnik A, Murthy RS. Artificial neural network as an alternative to multiple regression analysis in optimizing formulation parameters of cytarabine liposomes[J]. AAPS PharmSciTech, 2004, 5 (1): E4.
[22]	Xiong Y, Guo D, Wang L, et al. Development of nobiliside A loaded liposomal formulation using response surface methodology[J]. Int J Pharm, 2009, 371 (1-2): 197.
[23]	Arulsudar N, Subramanian N, Muthy RS. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes[J]. J Pharm Sci, 2005, 8 (2): 243.
[24]	Varshosaz J, GhaffariS, Khoshayand MR, et al. Development and optimization of solid lipid nanoparticles of amikacin by central composite design[J]. J Liposome Res, 2010, 20 (2): 97.
[25]	Shah M, Pathak K. Development and Statistical Optimization of Solid Lipid Nanoparticles of Simvastatin by Using 2(3) Full-Factorial Design[J]. AAPS Pharm Sci Tech, 2010, 11(2): 489.
[26]	Boldhane SP, Kuchekar BS. Gastroretentive drug delivery of metformin hydrochloride: formulation and in vitro evaluation using 3(2) full factorial design[J]. Curr Drug Deliv, 2009, 6 (5): 477.
[27]	Swain K, Pattnaik S, Mallick S, et al. Influence of hydroxypropyl methylcellulose on drug release pattern of a gastroretentive floating drug delivery system using a 3(2) full factorial design [J]. Pharm Dev Technol, 2009, 14 (2): 193.

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Application of chemometrics in the optimization of extraction conditions and prescription formulations in pharmaceutical preparations

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