Freeze-dried medicine; Moisture content; Near-infrared spectroscopy; Suport vector regression; Global model
Abstract :
[en] Near-infrared (NIR) global quantitative models were evaluated for the moisture content (MC) determination of three different freeze-dried drug products. The quantitative models were based on 3822 spectra measured on two identical spectrometers to include variability. The MC, measured with the reference Karl Fischer (KF) method, were ranged from 0.05% to 4.96%. Linear and non-linear regression models using Partial Least Square (PLS), Decision Tree (DT), Bayesian Ridge Regression (Bayes-RR), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were created and evaluated. Among them, the SVR model was retained for a global application. The Standard Error of Calibration (SEC) and the Standard Error of Prediction (SEP) were respectively 0.12% and 0.15%. This model was then evaluated in terms of total error and risk-based assessment, linearity, and accuracy. It was observed that MC can be fastly and simultaneously determined in freeze-dried pharmaceutical products thanks to a global NIR model created with different medicines. This innovative approach allows to speed up the validation time and the in-lab release analyses.
Research center :
CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège
Food and Drug Administration (FDA), Guide to inspections of lyophilization of parenterals, 2014 (accessed 01.06.2016). < http://www.fda.gov/ICECI/Inspections/InspectionGuides/ucm074909.htm>.
Kauppinen, A., Toiviainen, M., Lehtonen, M., Järvinen, K., Paaso, J., Juuti, M., Ketolainen, J., Validation of a multipoint near-infrared spectroscopy method for in-line moisture content analysis during freeze-drying. J. Pharm. Biomed. Anal. 95 (2014), 229–237.
K.P. Malik, C. Duru, M. Ahmed, P. Matejtschuk, Analytical Options for the Measurement of Residual Moisture Content in Lyophilized Biological Materials, American Pharmaceutical review, 2010.
Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A., Jent, N., A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal. 44 (2007), 683–700.
Lin, T.P., Hsu, C., Determination of residual moisture in lyophilized protein using a rapid and non invasive method: near infrared spectroscopy. PDA J. Pharm. Sci. Technol. 56 (2002), 196–205.
Jerome, J., Workman, J.R., Interpretive spectroscopy for near infrared applied spectroscopy reviews. Appl. Spectrosc. Rev. 31 (1996), 251–320.
De Bleye, C., Chavez, P.-F., Mantanus, J., Marini, R., Hubert, P., Rozet, E., Ziemons, E., Critical review of near-infrared spectroscopy methods validations in pharmaceutical applications. J. Pharm. Biomed. Anal. 69 (2012), 125–132.
Jiang, Y., Yang, M., Zhang, X.-B., Yin, L.-H., Research on quantitative models of water content for Chinese herbal pieces by NIR Chinese. J. Pharm. Anal. 33 (2013), 1572–1577.
Xb, Z., Yc, F., Cq, H., Feasibility and extension of universal quantitative models for moisture content determination in beta-lactam powder injections by near-infrared spectroscopy. Anal. Chim. Acta 630 (2008), 131–140.
X. Yan Mei, S. Xiang Zhong, C. Chang Zhou, L. Hong Ping, T. Guo, H. Yue, D. Jia, M. Shun Geng, The establishment and evaluation of near infrared universal model to determinate the effective ingredient content in pesticide rapidly, Chin. Chem. Lett, 23 (2012).
Anastasia, M., Vladislav, G., Elena, S., Andrey, B., Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range. Food Chem. 203 (2016), 190–198.
Liu, X.-P., Feng, Y.-C., Hu, C.-Q., Ding, L., Construction of universal quantitative models for determination of cefradine capsules. Chin. J. Pharm. Anal., 28, 2008.
H.h. Pang, Y.C. Feng, C.Q. Hu, X.B. Ren, Construction of universal quantitative models for determination of cefoperazone sodium for injection from different manufacturers using near infrared reflectrance spectroscopy, spectroscopy and spectral analysis, 26 (2006) 2214–2218.
Xiaoliang, W., Qiang, F., Jinfang, S., Xin, Y., Jianzhong, J., Wei, D., Construction of a universal quantitative model for ibuprofen sustained-release capsules from different manufacturers using near-infrared diffuse reflection spectroscopy. Vib. Spectroscopy 53 (2010), 214–217.
Lei, Y., Luo, Z.-Y., Hu, C.Q., Rapidly screening counterfeit drugs using near infrared spectroscopy: combining qualitative analysis with quantitative analysis to increase effectiveness. J. Near Infrared Spectroscopy 16 (2008), 349–355.
Yan-Chun, F., Chang-Qin, H., Construction of universal quantitative models for determination of roxithromycin and erythromycin ethylsuccinate in tablets from different manufacturers using near infrared reflectance spectroscopy. J. Pharm. Biomed. Anal. 41 (2006), 373–384.
WenBo, Z., YanChun, F., JiXiong, D., DanQing, S., ChangQin, H., A new strategy to iteratively update scalable universal quantitative models for the testing of azithromycin by near infrared spectroscopy. Sci. China Chem. 56 (2013), 533–540.
Yh, J., Xp, L., Yc, F., Cq, H., A training set selection strategy for a universal near-infrared quantitative model. AAPS PharmSciTech. 12 (2011), 738–745.
Osborne, B.G., Fearn, T., Collaborative evaluation of universal calibrations for the measurement of protein and moisture in flour by near infrared reflectance. J. Fd Technol. 18 (1983), 453–460.
Carl Emil, E., Per Waaben, H., Thomas, S., Federico, M., Lars, N., Evaluation of multivariate calibration models transferred between spectroscopic instruments: Applied to near infrared measurements of flour samples. J. Near Infrared Spectroscopy, 24, 2016.
Clavaud, M., Roggo, Y., Dégardin, K., Sacré, P.-Y., Hubert, P., Ziemons, E., Moisture content determination in an antibody-drug conjugate freeze-dried medicine by near-infrared spectroscopy: a case study for release testing. J. Pharm. Biomed. Anal. 131 (2016), 380–390.
Barnes, R.J., Dhanoa, M.S., Lister, S.J., Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectroscopy 43 (1989), 772–777.
Blanco, M., Coello, J., Iturriaga, H., Maspoch, S., De la Pezuela, C., Effect of data preprocessing methods in near-infrared diffuse reflectance spectroscopy for the determination of the active compound in a pharmaceutical preparation. Appl. Spectroscopy 51 (1997), 240–246.
Wold, S., Sjöström, M., Eriksson, L., PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. 58 (2001), 109–130.
Springer, Pattern recognition and machine learning, in: M. Jordan, J. Kleinberg, B. Schölkopf (Eds.), Singapore, 2006.
Chen, T., Martin, E., Bayesian linear regression and variable selection for spectroscopic calibration. Anal. Chim. Acta 631 (2009), 13–21.
Czajkowski, M., Kretowski, M., The role of decision tree representation in regression problems – an evolutionary perspective. Appl. Soft Comput. 48 (2016), 458–475.
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning - Data Mining, Inference, and Prediction - Second Edition, in: S.S.i. Statistics (Ed.), 2008.
Christianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines. 2000, Cambridge University Press, New York.
Hubert, P., Nguyen-Huu, J.-J., Boulanger, B., Chapuzet, E., Chiap, P., Cohen, N., Compagnon, P.-A., Dewe, W., Feinberg, M., Lallier, M., Laurentie, M., Mercier, N., Muzard, G., Nivet, C., Valat, L., Harmonization of strategies for the validation of quantitative analytical procedures. A SFSTP proposal—part I. J. Pharm. Biomed. Anal. 36 (2004), 579–586.
D. Ascher, P.F. Dubois, K. Hinsen, J. Hugunin, T. Oliphant, Numerical Python, 1999 (accessed 24.01.2017) < http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.2204&rep=rep1&type=pdf>.
Van der Walt, S., Colbert, S.C., Varoquaux, G., The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 1521–9615 (2011), 22–30.
Batten, G.D., Plant analysis using near infrared reflectance spectroscopy: the potential and the limitations. Aust. J. Exp. Agr. 38 (1998), 697–706.
P. Williams, K. Norris (Eds), Variables affecting near-infrared reflectance spectroscopic analysis, in: P.C. Williams (Ed.), Am. Cereal Assoc. Cereal Chem., St. Paul, 1987, pp. 143–167.
