全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Pharmaceutical Applications of Chemometric Techniques

DOI: 10.1155/2013/795178

Full-Text   Cite this paper   Add to My Lib

Abstract:

Chemometrics involves application of various statistical methods for drawing vital information from various manufacturing-related processes. Multiway chemometric models like parallel factor analysis (PARAFAC), Tucker-3, N-partial least square (N-PLS), and bilinear models like principle component regression (PCR) and partial least squares (PLS) have been discussed in the paper. Chemometric approaches can be used to analyze the data obtained from various instruments including near infrared (NIR), attenuated total reflectance Fourier transform infrared (ATR-FTIR), high-performance liquid chromatography (HPLC), and terahertz pulse spectroscopy. The technique has been used in the quality assurance and quality control of pharmaceutical solid dosage forms. Moreover, application of chemometric methods in the evaluation of properties of pharmaceutical powders and tablet parametric tests has also been discussed in the review. It has been suggested as a useful method for the real-time in-process testing and is a valuable process analytical tool. 1. Introduction Chemometrics is a branch of science that derives data by the application of mathematical and statistical methods, for the extraction of useful information from physical and chemical phenomena involved in a manufacturing process. Chemometrics is used for multivariate data collection and analysis protocols, calibration, process modelling, pattern recognition and classification, signal correction and compression, and statistical process control. Both predictive and descriptive issues of life sciences could be solved by chemometrics. The predictive issues include numerous system properties that are utilized in an elaborated model with the intent of predicting the target properties, desired features, or behaviour of interest. The descriptive issues include properties of the investigated systems that are modelled in order to learn the underlying relationships and the system structure, which leads to the model identification, composition, and understanding. There is a vast volume of measurement data generated by the latest automated laboratory instruments in biological/medical research which are difficult to absorb and interpret. The use of chemometrics helps to perform such a challenging task of consuming the data and reveal the useful information. Some applications of chemometrics in pharmacy and medical sciences are depicted in Figure 1. Figure 1: Applications of chemometrics in pharmacy and medical sciences. Chemometrics and its methods are versatile and there is a high level of abstraction as it characterises

References

[1]  S. Matero, Chemometrics Methods in Pharmaceutical Tablet Development and Manufacturing Unit Operations, Publications of the University of Eastern Finland Dissertations in Health Sciences, 2010.
[2]  J. Mocak, “Chemometrics in medicine and pharmacy,” Nova Biotechnologica et Chimica, vol. 11, pp. 11–25, 2012.
[3]  I. T. Jolliffe, Principal Component Analysis, Springer, New York, NY, USA, 2nd edition, 2002.
[4]  P. D. Wentzell, D. T. Andrews, D. C. Hamilton, K. Faber, and B. R. Kowalski, “Maximum likelihood principal component analysis,” Journal of Chemometrics, vol. 11, no. 4, pp. 339–366, 1997.
[5]  H. Wold, Soft Modeling: The Basic Design and Some Extensions, North Holland Press, Amsterdam, The Netherlands, 1982.
[6]  H. Wold, Encyclopedia of the Statistical Sciences, John Wiley and Sons, New York, NY, USA, 1985.
[7]  S. Wold, H. Ruhe, H. Wold, and W. J. Dunn, “The co linearity problem in linear regression: the partial least squares (PLS) approach to generalized inverse,” SIAM Journal on Scientific Computing, vol. 5, pp. 735–743, 1984.
[8]  A. Hoskuldsson, “PLS regression methods,” Journal of Chemometrics, vol. 2, pp. 211–228, 1988.
[9]  M. Barker and W. Rayens, “Partial least squares for discrimination,” Journal of Chemometrics, vol. 17, no. 3, pp. 166–173, 2003.
[10]  S. D. Jong, B. M. Wise, and N. L. Ricker, “Canonical partial least squares and continuum power regression,” Journal of Chemometrics, vol. 15, pp. 85–100, 2001.
[11]  A. E. Hoerl and R. W. Kennard, “Ridge regression: biased estimation for nonorthogonal problems,” Technometrics, vol. 12, pp. 55–67, 1970.
[12]  W. F. Massy, “Principal components regression in exploratory statistical research,” Journal of the American Statistical Association, vol. 60, pp. 234–256, 1965.
[13]  M. Stone and R. J. Brooks, “Continuum regression: cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principal components regression,” Journal of the Royal Statistical Society, vol. 52, pp. 237–269, 1990.
[14]  P. H. Garthwaite, “An interpretation of partial least squares,” Journal of the American Statistical Association, vol. 89, pp. 122–127, 1994.
[15]  K. J. Worsley, “An overview and some new developments in the statistical analysis of PET and fMRI data,” Human Brain Mapping, vol. 5, pp. 254–258, 1997.
[16]  S. Wold, “Personal memories of the early PLS development,” Chemometrics and Intelligent Laboratory Systems, vol. 58, pp. 83–84, 2001.
[17]  I. E. Frank and J. H. Friedman, “A statistical view of some chemometrics regression tools,” Technometrics, vol. 35, pp. 109–147, 1993.
[18]  H. Martens, “Reliable and relevant modelling of real world data: a personal account of the development of PLS Regression,” Chemometrics and Intelligent Laboratory Systems, vol. 58, no. 2, pp. 85–95, 2001.
[19]  J. Hulland, “Use of partial least squares (PLS) in strategic management research: a review of four recent studies,” Strategic Management Journal, vol. 20, no. 2, pp. 195–204, 1999.
[20]  N. J. Lobaugh, R. West, and A. R. McIntosh, “Spatiotemporal analysis of experimental differences in event-related potential data with partial least squares,” Psychophysiology, vol. 38, no. 3, pp. 517–530, 2001.
[21]  R. Manne, “Analysis of two partial-least-squares algorithms for multivariate calibration,” Chemometrics and Intelligent Laboratory Systems, vol. 2, no. 1–3, pp. 187–197, 1987.
[22]  J. Nilsson, S. De Jong, and A. K. Smilde, “Multiway calibration in 3D QSAR,” Journal of Chemometrics, vol. 11, no. 6, pp. 511–524, 1997.
[23]  D. V. Nguyen and D. M. Rocke, “Tumor classification by partial least squares using microarray gene expression data,” Bioinformatics, vol. 18, no. 1, pp. 39–50, 2002.
[24]  R. Bro, “Multiway calibration: multilinear PLS,” Journal of Chemometrics, vol. 10, no. 1, pp. 47–61, 1996.
[25]  R. Bro and H. A. L. Kiers, “A new efficient method for determining the number of components in PARAFAC models,” Journal of Chemometrics, vol. 17, no. 5, pp. 274–286, 2003.
[26]  A. Smilde and R. Bro, Multi-Way Analysis with Applications in the Chemical Sciences, John Wiley and Sons, New York, NY, USA, 2005.
[27]  P. Geladi and J. Forsstr?m, “Monitoring of a batch organic synthesis by near-infrared spectroscopy: modeling and interpretation of three-way data,” Journal of Chemometrics, vol. 16, no. 7, pp. 329–338, 2002.
[28]  C. M. Andersen and R. Bro, “Practical aspects of PARAFAC modeling of fluorescence excitation-emission data,” Journal of Chemometrics, vol. 17, no. 4, pp. 200–215, 2003.
[29]  R. Bro, “PARAFAC: tutorial and applications,” Chemometrics and Intelligent Laboratory Systems, vol. 38, no. 2, pp. 149–171, 1997.
[30]  ?. Rinnan, J. Riu, and R. Bro, “Multi-way prediction in the presence of uncalibrated interferents,” Journal of Chemometrics, vol. 21, no. 1-2, pp. 76–86, 2007.
[31]  R. Bro, C. A. Andersson, and H. A. L. Kiers, “PARAFAC2. Part II: modeling chromatographic data with retention time shifts,” Journal of Chemometrics, vol. 13, no. 3-4, pp. 295–309, 1999.
[32]  H. A. L. Kiers, J. M. F. Ten Berge, and R. Bro, “PARAFAC2. Part I: a direct fitting algorithm for the PARAFAC2 model,” Journal of Chemometrics, vol. 13, no. 3-4, pp. 275–294, 1999.
[33]  A. Smilde, “Comments on three-way analyses used for batch process data,” Journal of Chemometrics, vol. 15, pp. 19–27, 2001.
[34]  A. C. Olivieri, “Analytical advantages of multivariate data processing: one, two, three, infinity?” Analytical Chemistry, vol. 80, no. 15, pp. 5713–5720, 2008.
[35]  Y. Roggo, P. Chalus, L. Maurer, C. Lema-Martinez, A. Edmond, and N. Jent, “A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies,” Journal of Pharmaceutical and Biomedical Analysis, vol. 44, no. 3, pp. 683–700, 2007.
[36]  M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, and C. de la Pezuela, “Near-infrared spectroscopy in the pharmaceutical industry,” Analyst, vol. 123, no. 8, pp. 135R–150R, 1998.
[37]  M. Blanco and I. Villarroya, “NIR spectroscopy: a rapid-response analytical tool,” Trends in Analytical Chemistry, vol. 21, no. 4, pp. 240–250, 2002.
[38]  I. Tomuta, R. Iovanov, A. L. Vonica, and S. E. Leucuta, “High-Throughput NIR-Chemometric method for Meloxicam assay from powder blends for tableting,” Scientia Pharmaceutica, vol. 79, no. 4, pp. 885–898, 2011.
[39]  K. Awa, T. Okumura, H. Shinzawa, M. Otsuka, and Y. Ozaki, “Self-modeling curve resolution (SMCR) analysis of near-infrared (NIR) imaging data of pharmaceutical tablets,” Analytica Chimica Acta, vol. 619, no. 1, pp. 81–86, 2008.
[40]  H. Cen, Y. He, and M. Huang, “Measurement of soluble solids contents and pH in orange juice using chemometrics and vis-NIRS,” Journal of Agricultural and Food Chemistry, vol. 54, no. 20, pp. 7437–7443, 2006.
[41]  J. M. Amigo and C. Ravn, “Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging,” European Journal of Pharmaceutical Sciences, vol. 37, no. 2, pp. 76–82, 2009.
[42]  T. Furukawa, H. Sato, H. Shinzawa, I. Noda, and S. Ochiai, “Evaluation of homogeneity of binary blends of poly(3-hydroxybutyrate) and poly(L-lactic acid) studied by near infrared chemical imaging (NIRCI),” Analytical Sciences, vol. 23, no. 7, pp. 871–876, 2007.
[43]  Z. Rahman, A. S. Zidan, and M. A. Khan, “Formulation and evaluation of a protein-loaded solid dispersions by non-destructive methods,” AAPS PharmSciTech, vol. 12, no. 2, pp. 158–170, 2010.
[44]  A. Kassis, V. M. Bhawtankar, and J. R. Sowa, “Attenuated total reflection infrared spectroscopy (ATR-IR) as an in situ technique for dissolution studies,” Journal of Pharmaceutical and Biomedical Analysis, vol. 53, no. 3, pp. 269–273, 2010.
[45]  O. Planin?ek, D. Planin?ek, A. Zega, M. Breznik, and S. Sr?i?, “Surface analysis of powder binary mixtures with ATR FTIR spectroscopy,” International Journal of Pharmaceutics, vol. 319, no. 1-2, pp. 13–19, 2006.
[46]  E. Din?, A. ?zdemir, H. Aksoy, ?. üstünda?, and D. Baleanud, “Chemometric determination of naproxen sodium and pseudoephedrine hydrochloride in tablets by HPLC,” Chemical and Pharmaceutical Bulletin, vol. 54, no. 4, pp. 415–421, 2006.
[47]  M. Abdelkawy, F. Metwaly, N. E. Raghy, M. Hegazy, and N. Fayek, “Simultaneous determination of Ambroxol Hydrochloride and Guaifenesin by HPLC, TLC-Spectrodensitometric and multivariate calibration methods in pure form and in Cough Cold Formulations,” Journal of Chromatography, vol. 2, p. 112, 2011.
[48]  I. Tomu?ǎ, R. Iovanov, E. Bodoki, and S. E. Leucu?a, “Quantification of meloxicam and excipients on intact tablets by near infrared spectrometry and chemometry,” Farmacia, vol. 58, no. 5, pp. 559–571, 2010.
[49]  G. M. Day, J. A. Zeitler, W. Jones, T. Rades, and P. F. Taday, “Understanding the influence of polymorphism on phonon spectra: lattice dynamics calculations and terahertz spectroscopy of carbamazepine,” Journal of Physical Chemistry B, vol. 110, no. 1, pp. 447–456, 2006.
[50]  C. J. Strachan, T. Rades, D. A. Newnham, K. C. Gordon, M. Pepper, and P. F. Taday, “Using terahertz pulsed spectroscopy to study crystallinity of pharmaceutical materials,” Chemical Physics Letters, vol. 390, no. 1–3, pp. 20–24, 2004.
[51]  P. F. Taday, I. V. Bradley, D. D. Arnone, and M. Pepper, “Using Terahertz pulse spectroscopy to study the crystalline structure of a drug: a case study of the polymorphs of ranitidine hydrochloride,” Journal of Pharmaceutical Sciences, vol. 92, no. 4, pp. 831–838, 2003.
[52]  G. R. Neil, G. L. Carr, J. F. Gubeli III et al., “Production of high power femtosecond terahertz radiation,” Nuclear Instruments and Methods in Physics Research A, vol. 507, no. 1-2, pp. 537–540, 2003.
[53]  M. C. Sarragu?a, A. V. Cruz, S. O. Soares, H. R. Amaral, P. C. Costa, and J. A. Lopes, “Determination of flow properties of pharmaceutical powders by near infrared spectroscopy,” Journal of Pharmaceutical and Biomedical Analysis, vol. 52, no. 4, pp. 484–492, 2010.
[54]  M. Kim, H. Chung, and Y. M. Jung, “Accurate determination of polyethylene pellet density using transmission Raman spectroscopy,” Journal of Raman Spectroscopy, vol. 42, no. 11, pp. 1967–1976, 2011.
[55]  M. Otsuka, Y. Mouri, and Y. Matsuda, “Chemometric evaluation of pharmaceutical properties of antipyrine granules by near-infrared spectroscopy,” AAPS PharmSciTech, vol. 4, no. 3, p. E47, 2003.
[56]  G. Szakonyi and R. Zelkó, “Water content determination of superdisintegrants by means of ATR-FTIR spectroscopy,” Journal of Pharmaceutical and Biomedical Analysis, vol. 63, pp. 106–111, 2012.
[57]  R. Ambrus, P. Kocbek, J. Kristl, R. ?ibanc, R. Rajkó, and P. Szabó-Révész, “Investigation of preparation parameters to improve the dissolution of poorly water-soluble meloxicam,” International Journal of Pharmaceutics, vol. 381, no. 2, pp. 153–159, 2009.
[58]  H. Tanabe, K. Otsuka, and M. Otsuka, “Theoretical analysis of tablet hardness prediction using chemoinformetric near-infrared spectroscopy,” Analytical Sciences, vol. 23, no. 7, pp. 857–862, 2007.
[59]  M. Otsuka and I. Yamane, “Prediction of tablet properties based on near infrared spectra of raw mixed powders by chemometrics: scale-up factor of blending and tableting processes,” Journal of Pharmaceutical Sciences, vol. 98, no. 11, pp. 4296–4305, 2009.
[60]  M. Sanni, P. Jari, M. S. Anne et al., “Predicting the drug concentration in starch acetate matrix tablets from ATR-FTIR spectra using multi-way methods,” Analytica Chimica Acta, vol. 595, no. 1-2, pp. 190–197, 2007.
[61]  R. P. Cogdill, S. M. Short, R. Forcht et al., “An efficient method-development strategy for quantitative chemical imaging using terahertz pulse spectroscopy,” Journal of Pharmaceutical Innovation, vol. 1, no. 1, pp. 63–75, 2006.
[62]  X. Kong, W. Zhu, Z. Zhao et al., “Fluorescence spectroscopic determination of triglyceride in human serum with window genetic algorithm partial least squares,” Journal of Chemometrics, vol. 26, no. 1, pp. 25–33, 2012.
[63]  M. P. Freitas, A. Sabadin, L. M. Silva et al., “Prediction of drug dissolution profiles from tablets using NIR diffuse reflectance spectroscopy: a rapid and nondestructive method,” Journal of Pharmaceutical and Biomedical Analysis, vol. 39, no. 1-2, pp. 17–21, 2005.
[64]  M. Donoso and E. S. Ghaly, “Prediction of drug dissolution from tablets using near-infrared diffuse reflectance spectroscopy as a nondestructive method,” Pharmaceutical Development and Technology, vol. 9, no. 3, pp. 247–263, 2004.
[65]  M. Donoso and E. S. Ghaly, “Prediction of tablets disintegration times using near-infrared diffuse reflectance spectroscopy as a nondestructive method,” Pharmaceutical Development and Technology, vol. 10, no. 2, pp. 211–217, 2005.
[66]  K. M. Morisseau and C. T. Rhodes, “Near-infrared spectroscopy as a nondestructive alternative to conventional tablet hardness testing,” Pharmaceutical Research, vol. 14, no. 1, pp. 108–111, 1997.
[67]  M. Donoso, D. O. Kildsig, and E. S. Ghaly, “Prediction of tablet hardness and porosity using near-infrared diffuse reflectance spectroscopy as a nondestructive method,” Pharmaceutical Development and Technology, vol. 8, no. 4, pp. 357–366, 2003.
[68]  J. D. Kirsch and J. K. Drennen, “Nondestructive tablet hardness testing by near-infrared spectroscopy: a new and robust spectral best-fit algorithm,” Journal of Pharmaceutical and Biomedical Analysis, vol. 19, no. 3-4, pp. 351–362, 1999.
[69]  M. Otsuka and I. Yamane, “Prediction of tablet hardness based on near infrared spectra of raw mixed powders by chemometrics,” Journal of Pharmaceutical Sciences, vol. 95, no. 7, pp. 1425–1433, 2006.
[70]  N. K. Ebube, S. S. Thosar, R. A. Roberts et al., “Application of near-infrared spectroscopy for nondestructive analysis of Avicel? powders and tablets,” Pharmaceutical Development and Technology, vol. 4, no. 1, pp. 19–26, 1999.
[71]  Y. Chen, S. S. Thosar, R. A. Forbess, M. S. Kemper, R. L. Rubinovitz, and A. J. Shukla, “Prediction of drug content and hardness of intact tablets using artificial neural network and near-infrared spectroscopy,” Drug Development and Industrial Pharmacy, vol. 27, no. 7, pp. 623–631, 2001.
[72]  S. Luciana, I. F. Elizabeth, S. M. Sobral, and T. S. Marcus, “Chemometric studies on natural products as potential inhibitors of the nadh oxidase from trypanosoma cruzi using the volsurf approach,” Molecules, vol. 15, no. 10, pp. 7363–7377, 2010.
[73]  G. A. Cordeiro, N. Nagata, I. Messerschmidt, P. Peralta-Zamora, and L. N. C. Rodrigues, “Multivariate spectroscopic determination of the lamivudine-zidovudine association,” Journal of the Brazilian Chemical Society, vol. 22, no. 2, pp. 337–343, 2011.
[74]  M. S. Kemper, E. J. Magnuson, S. R. Lowry et al., “Use of FT-NIR transmission spectroscopy for the quantitative analysis of an active ingredient in a translucent pharmaceutical topical gel formulation,” AAPS PharmSciTech, vol. 3, no. 3, article E23, 2001.
[75]  C. P. Meza, M. A. Santos, and R. J. Roma?ach, “Quantitation of drug content in a low dosage formulation by transmission near infrared spectroscopy,” AAPS PharmSciTech, vol. 7, no. 1, article 29, 2006.
[76]  M. Otsuka and H. Kinoshita, “Quantitative determination of hydrate content of theophylline powder by chemometric X-ray powder diffraction analysis,” AAPS PharmSciTech, vol. 11, no. 1, pp. 204–211, 2010.
[77]  M. R. Maggio, M. A. Rivero, and S. T. Kaufman, “Simultaneous acquisition of the dissolution curves of two active ingredients in a binary pharmaceutical association, employing an on-line circulation system and chemometrics-assistance,” Journal of Pharmaceutical and Biomedical Analysis, vol. 72, pp. 51–58, 2013.
[78]  C. K. Markopoulou, E. T. Malliou, and J. E. Koundourellis, “Application of two chemometric methods for the determination of imipramine, amitriptyline and perphenazine in content uniformity and drug dissolution studies,” Journal of Pharmaceutical and Biomedical Analysis, vol. 37, no. 2, pp. 249–258, 2005.
[79]  M. Otsuka, F. Kato, and Y. Matsuda, “Determination of indomethacin polymorphic contents by chemometric near-infrared spectroscopy and conventional powder X-ray diffractometry,” Analyst, vol. 126, no. 9, pp. 1578–1582, 2001.
[80]  A. S. Tatavarti, R. Fahmy, H. Wu et al., “Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms,” AAPS PharmSciTech, vol. 6, no. 1, article 15, pp. E91–E99, 2005.
[81]  F. R. Doucet, P. J. Faustino, M. Sabsabi, and R. C. Lyon, “Quantitative molecular analysis with molecular bands emission using laser-induced breakdown spectroscopy and chemometrics,” Journal of Analytical Atomic Spectrometry, vol. 23, no. 5, pp. 694–701, 2008.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133