Critical review of near-infrared spectroscopic methods validations in pharmaceutical applications
Laboratory of Analytical Chemistry, CIRM, University of Liège, 1 Avenue de l'Hôpital, 4000 Liège, Belgium.Journal of pharmaceutical and biomedical analysis (Impact Factor: 2.98). 03/2012; 69:125-32. DOI: 10.1016/j.jpba.2012.02.003
Based on the large number of publications reported over the past five years, near-infrared spectroscopy (NIRS) is more and more considered an attractive and promising analytical tool regarding Process Analytical Technology and Green Chemistry. From the reviewed literature, few of these publications present a thoroughly validated NIRS method even if some guidelines have been published by different groups and regulatory authorities. However, as any analytical method, the validation of NIRS method is a mandatory step at the end of the development in order to give enough guarantees that each of the future results during routine use will be close enough to the true value. Besides the introduction of PAT concepts in the revised document of the European Pharmacopoeia (2.2.40) dealing with near-infrared spectroscopy recently published in Pharmeuropa, it agrees very well with this mandatory step. Indeed, the latter suggests to use similar analytical performance characteristics than those required for any analytical procedure based on acceptance criteria consistent with the intended use of the method. In this context, this review gives a comprehensive and critical overview of the methodologies applied to assess the validity of quantitative NIRS methods used in pharmaceutical applications.
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ABSTRACT: This paper describes the development, validation and application of NIR-chemometric methods for API content and pharmaceutical characterization (disintegration time and crushing strength) of indapamide intact tablets. Development of the method for chemical characterization was performed on samples corresponding to 80, 90, 100, 110 and 120% of indapamide content and for pharmaceutical characterization on samples prepared at nine different compression forces (covering the interval 7-45kN). NIR spectra of prepared tablets were recorded in transmission mode, and partial least-squares followed by leave-one-out cross-validation were used to develop models for the prediction of the drug content and the pharmaceutical properties of tablets. All developed models were validated in terms of trueness, precision and accuracy. No statistical differences were found between results predicted by NIR-chemometric methods and the ones determined by reference methods. Therefore, the developed NIR-chemometric methods meet the requirements of a high-throughput method for the determination of drug content, pharmaceutical properties of indapamide tablets.Journal of pharmaceutical and biomedical analysis 12/2012; 84. DOI:10.1016/j.jpba.2012.12.020 · 2.98 Impact Factor
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ABSTRACT: In conformity with the 2011 Food and Drug Administration guidance for industry, Process Validation: General Principles and Practices, there is a requirement for new methodologies that support continued cleaning verification of manufacturing equipment. Near infrared chemical imaging (NIR-CI) represents an attractive alternative to the current methodologies for cleaning verification since it represents a direct, rapid and sensitive technology. The aim of this study was to test the feasibility of NIR-CI for cleaning verification. Stainless steel, glass and Perspex surfaces were soiled with selected active pharmaceutical ingredients (APIs) with the resulting hyperspectral data employed to identify and quantify the dried residues. Correlations to high-performance liquid chromatography (HPLC) swab analysis were also made. Linear models were obtained for the number of API classified pixels and applied soil concentration with R-2 values of 0.90 and 0.98 for caffeine and sulfathiazole sodium salt, respectively, on stainless steel coupons. In the case of caffeine soils on borosilicate glass and Perspex coupons, the obtained R-2 values were 0.95 and 0.96, respectively. These R-2 values suggest a linear relationship between the number of API classified pixels and applied soil concentration. The calculated detection limits for caffeine and sulfathiazole sodium salt soils on stainless steel coupons were 48 mu g and 18 mu g 50 mm(-2), respectively. For caffeine soils on borosilicate glass and Perspex coupons the calculated detection limits were 33 mu g and 27 mu g 50 mm(-2), respectively. However, visual inspection of the NIR-CI following the classification function point to limits of detection closer to 1 mu g 50mm(-2) or lower. High correlations (R-2 of 0.91 and 0.93 for caffeine and sulfathiazole sodium salt, respectively) were also obtained between NIR-CI API soil quantification and swabbing/HPLC analysis. This research points to possible future use of portable CI systems for continued cleaning verification of pharmaceutical residues.Journal of Pharmaceutical and Biomedical Analysis 01/2013; 21(3):173. DOI:10.1255/jnirs.1052 · 2.98 Impact Factor
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ABSTRACT: Near infrared spectroscopy (NIRs) in combination with chemometrical techniques partial least squares is an effective, expeditious and nondestructive technique to analyse various parameters of interest to the pharmaceutical industry. In this work, we explored the use of NIR spectroscopy for the determination of physical (compression force, tablet hardness, mean particle size and particle size distribution) and chemical parameters (content uniformity). To measure tablet content uniformity, naproxen tablets with drug content ranging from 80% to 120% were made and analyzed using high performance liquid chromatography (HPLC) and NIR spectroscopy. For models of the compression force and hardness, the tablets were made by Rotary tablet press and the compression force was varied from 5.7 to 46.0 kN. The samples of mean particle size and particle size distribution were passed through sieves of different sieve opening to obtain six size fractions. The correlation coefficient (R), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and root mean square error of cross-validation (RMSECV) have been used to assess the predictive ability and robustness of the different partial least squares (PLS) models.Analytical methods 02/2013; 5(5):1337-1345. DOI:10.1039/C2AY26260F · 1.82 Impact Factor