Development and Validation of Handheld Vibrational Spectroscopy Methods for the Quantitation of Active Ingredients in Pharmaceutical Solid Dosage Forms - 2025
Development and Validation of Handheld Vibrational Spectroscopy Methods for the Quantitation of Active Ingredients in Pharmaceutical Solid Dosage Forms
Sacre, Pierre-Yves; De Bleye, Charlotte; BECKERS, Pierreet al.
2025 • In Peru, Deborah A. (Ed.) Practical Quantitative Vibrational and Electronic Spectroscopy: A Guide for Developing, Optimizing, and Validating Procedures
Analytical Quality by Design; total analytical error; handheld Raman spectroscopy; chemometrics
Abstract :
[en] Over the last decades, vibrational spectroscopy methods such as Raman and near-infrared (NIR) spectroscopy have seen increasing adoption in the pharmaceutical industry. These techniques are valued for their non-destructive nature, rapid data acquisition, and ability to assess both physical and chemical properties of pharmaceuticals, including active ingredient levels, humidity, and polymorphism. Initially reliant on benchtop instruments for monitoring and quality control, advancements in technology have enabled the development of portable, handheld devices, facilitating a shift from the traditional “Sample to Laboratory” model to a “Laboratory to Sample” approach.
This chapter details the development and validation of a handheld Raman spectroscopy method for quantifying active ingredients in immediate-release tablets. It emphasizes the use of the Analytical Quality by Design (AQbD) framework, as defined in ICH Q14/Q2(R2) and USP <1220> guidelines, to streamline method development and adapt it to various scenarios. All important steps are exemplified and discussed to enable the user to adopt such approach and tailor it to its own application.
Research Center/Unit :
CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège
Disciplines :
Pharmacy, pharmacology & toxicology
Author, co-author :
Sacre, Pierre-Yves ; Université de Liège - ULiège > Unités de recherche interfacultaires > Centre Interdisciplinaire de Recherche sur le Médicament (CIRM)
De Bleye, Charlotte ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique ; Université de Liège - ULiège > Unités de recherche interfacultaires > Centre Interdisciplinaire de Recherche sur le Médicament (CIRM)
BECKERS, Pierre ; Centre Hospitalier Universitaire de Liège - CHU
Hubert, Philippe ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique ; Université de Liège - ULiège > Unités de recherche interfacultaires > Centre Interdisciplinaire de Recherche sur le Médicament (CIRM)
Ziemons, Eric ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique ; Université de Liège - ULiège > Unités de recherche interfacultaires > Centre Interdisciplinaire de Recherche sur le Médicament (CIRM)
Language :
English
Title :
Development and Validation of Handheld Vibrational Spectroscopy Methods for the Quantitation of Active Ingredients in Pharmaceutical Solid Dosage Forms
Publication date :
December 2025
Main work title :
Practical Quantitative Vibrational and Electronic Spectroscopy: A Guide for Developing, Optimizing, and Validating Procedures
U.S. Food and Drug Administration (2004). Guidance for industry, PAT-A framework for innovative pharmaceutical development, manufacturing and quality assurance. FDA-2003-D-0032, September.
Popping, B. and Diaz-Amigo, C. (2020). A paradigm shift: From “Sample to Laboratory” to “Laboratory to Sample”. Journal of AOAC International 104 (1): 1-6.
General Chapter (2023). <1151> pharmaceutical dosage forms. In: USP-NF. United States Pharmacopeia:Rockville, MD.
ICH Q8 (R2) (2009). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Pharmaceutical Development.
Deidda, R., Sacre, P.Y., Clavaud, M. et al. (2019). Vibrational spectroscopy in analysis of pharmaceuticals:Critical review of innovative portable and handheld NIR and Raman spectrophotometers. TrAC, Trends in Analytical Chemistry 114: 251-259.
Kimani, M.M., Lanzarotta, A., and Batson, J.S. (2021). Rapid determination of eight benzodiazepines in suspected counterfeit pharmaceuticals using surface-enhanced Raman scattering with handheld Raman spectrometers. Journal of Forensic Sciences 66 (6): 2167-2179.
Lanzarotta, A., Kimani, M.M., Thatcher, M.D. et al. (2020). Evaluation of suspected counterfeit pharmaceutical tablets declared to contain controlled substances using handheld Raman spectrometers. Journal of Forensic Sciences 65 (4): 1274-1279.
Mansouri, M.A., Sacré, P.Y., Coïc, L. et al. (2020). Quantitation of active pharmaceutical ingredient through the packaging using Raman handheld spectrophotometers: A comparison study. Talanta 207:120306.
Eliasson, C., Macleod, N.A., Jayes, L.C. et al. (2008). Non-invasive quantitative assessment of the content of pharmaceutical capsules using transmission Raman spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 47 (2): 221-229.
Kang, Y., Zhou, Y., Wu, Q. et al. (2020). Low-content quantitation in Entecavir tablets using 1064nm Raman spectroscopy. Journal of Spectroscopy 28: 1-11.
Cooper, J.B., Abdelkader, M., and Wise, K.L. (2013). Sequentially shifted excitation Raman spectroscopy:Novel algorithm and instrumentation for fluorescence-free Raman spectroscopy in spectral space. Applied Spectroscopy 67 (8): 973-984.
Conti, C., Botteon, A., Bertasa, M. et al. (2016). Portable sequentially shifted excitation Raman spectroscopy as an innovative tool for in situ chemical interrogation of painted surfaces. The Analyst 141 (15): 4599-4607.
ICH Q14 (2023). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Analytical Procedure Development.
ICH Q2 (R2) (2023). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Validation of Analytical Procedures.
General Chapter (2023). <1220> Analytical Procedure Lifecycle. In: USP-NF. United States Pharmacopeia:Rockville, MD.
Ntziouni, A., Thomson, J., Xiarchos, I. et al. (2022). Review of existing standards, guides, and practices for Raman spectroscopy. Applied Spectroscopy 76 (7): 747-772.
Barton, B., Thomson, J., Lozano Diz, E., and Portela, R. (2022). Chemometrics for Raman spectroscopy harmonization. Applied Spectroscopy 76 (9): 1021-1041.
Bertinetto, C., Engel, J., and Jansen, J. (2020 Nov). ANOVA simultaneous component analysis: A tutorial review. Analytica Chimica Acta: X 6: 100061.
PLS_Toolbox 9.2 (2023) [Internet]. Manson, WA USA 98831: Eigenvector Research, Inc.; Available from:http://www.eigenvector.com (accessed 20 January 2024).
JMP JMP Pro 17.2.0 [Internet]. Cary, NC: SAS Institute Inc.; 2023. Available from: www.jmp.com (accessed 25 Jan 2024).
Rodionova, O., Kucheryavskiy, S., and Pomerantsev, A. (2021). Efficient tools for principal component analysis of complex data-A tutorial. Chemometrics and Intelligent Laboratory Systems 213: 104304.
Rodionova, O.Y. and Pomerantsev, A.L. (2019). Detection of outliers in projection-based modeling. Analytical Chemistry 92 (3): 2656-2664.
Wold, S., Sjöström, M., and Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58 (2): 109-130.
Ali, H., Muthudoss, P., Ramalingam, M. et al. (2023). Machine learning-enabled NIR spectroscopy. Part 2: Workflow for selecting a subset of samples from publicly accessible data. AAPS PharmSciTech 24 (1):34.
Bu, D., Wan, B., and McGeorge, G. (2013). A discussion on the use of prediction uncertainty estimation of NIR data in partial least squares for quantitative pharmaceutical tablet assay methods. Chemometrics and Intelligent Laboratory Systems 120: 84-91.
Fearn, T., Riccioli, C., Garrido-Varo, A., and Guerrero-Ginel, J.E. (2009). On the geometry of SNV and MSC. Chemometrics and Intelligent Laboratory Systems 96 (1): 22-26.
Abraham, S. and Golay, M.J.E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36 (8): 1627-1639.
Wise, B.M. (2022). Evaluating models: Hating on R-squared [Internet]. Eigenvector [cited 2024 Jan 12]. Available from: https://eigenvector.com/%EF%BF%BCevaluating-models-hating-on-r-squared/ (accessed 10 Feb. 2024).
Esbensen, K.H. and Geladi, P. (2010). Principles of proper validation: Use and abuse of re-sampling for validation. Journal of Chemometrics 24 (3-4): 168-187.
Kennard, R.W. and Stone, L.A. (1969). Computer aided design of experiments. Technometrics 11 (1):137-148.
Snee, R.D. (1977). Validation of regression models: Methods and examples. Technometrics 19 (4):415-428.
Gallagher, N.B., O’Sullivan, D. Selection of representative learning and test sets using the onion method. EigenVector Research Inc [Internet]. Available from: https://eigenvector.com/wp-content/uploads/2020/01/Onion_SampleSelection.pdf (accessed 20 Feb. 2024).
Ferré, J. and Rius, F.X. (1997). Constructing D-optimal designs from a list of candidate samples. TrAC, Trends in Analytical Chemistry 16 (2): 70-73.
Galvao, R., Araujo, M., Jose, G. et al. (2005). A method for calibration and validation subset partitioning. Talanta 67 (4): 736-740.
Pomerantsev, A.L. and Rodionova OYe. (2023). Subset selection using combined analytical signal. Microchemical Journal 190: 108654.
Kucheryavskiy, S., Rodionova, O., and Pomerantsev, A. (2023). Procrustes cross-validation of multivariate regression models. Analytica Chimica Acta 1255: 341096.
General Chapter (2023). <1210> Statistical tools for procedure validation. In: USP-NF. United States Pharmacopeia: Rockville, MD.
Chong, I.G. and Jun, C.H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 78 (1-2): 103-112.
Farrés, M., Platikanov, S., Tsakovski, S., and Tauler, R. (2015). Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. Journal of Chemometrics 29 (10): 528-536.
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B. et al. (2010). Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC, Trends in Analytical Chemistry 29 (9): 1073-1081.
De Bleye, C., Chavez, P.F., Mantanus, J. et al. (2012). Critical review of near-infrared spectroscopic methods validations in pharmaceutical applications. Journal of Pharmaceutical and Biomedical Analysis 69:125-132.
Hubert, P., Nguyen-Huu, J.J., Boulanger, B. et al. (2004). Harmonization of strategies for the validation of quantitative analytical procedures. Journal of Pharmaceutical and Biomedical Analysis 36 (3): 579-586.
Hubert, P., Nguyen-Huu, J.J., Boulanger, B. et al. (2007). Harmonization of strategies for the validation of quantitative analytical procedures. Journal of Pharmaceutical and Biomedical Analysis 45 (1): 70-81.
Hubert, P., Nguyen-Huu, J.J., Boulanger, B. et al. (2007 Sep). Harmonization of strategies for the validation of quantitative analytical procedures. Journal of Pharmaceutical and Biomedical Analysis 45 (1):82-96.
Hubert, P., Nguyen-Huu, J.J., Boulanger, B. et al. (2008). Harmonization of strategies for the validation of quantitative analytical procedures: A SFSTP proposal. Journal of Pharmaceutical and Biomedical Analysis 48 (3): 760-771.
e-noval (2023). e-noval 4.1 [Internet]. Mont-Saint-Guibert: Cencora Pharmalex Available from:https://www.arlenda.com/enoval4.1/ (accessed 27 Feb 2024).
Esbensen, K.H., Geladi, P., and Larsen, A. (2014). The RPD myth…. NIR News 25 (5): 24-28.
Borman, P.J., Guiraldelli, A.M., Weitzel, J. et al. (2024). Ongoing analytical procedure performance verification using a risk-based approach to determine performance monitoring requirements. Analytical Chemistry 96 (3): 966-979.
Avohou, T.H., Sacré, P.Y., Hamla, S. et al. (2022). Optimizing the soft independent modeling of class analogy (SIMCA) using statistical prediction regions. Analytica Chimica Acta 1229 (August): 340339.
Wise, B.M. and Roginski, R.T. (2015). A calibration model maintenance roadmap. IFAC-PapersOnLine 48 (8): 260-265.
Miyano, T., Nakagawa, H., Watanabe, T. et al. (2015). Operationalizing maintenance of calibration models based on near-infrared spectroscopy by knowledge integration. Journal of Pharmaceutical Innovation 10 (4): 287-301.
Wang, H.P., Chen, P., Dai, J.W. et al. (2022). Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues. TrAC, Trends in Analytical Chemistry 153:116648.
Steinbach, D., Anderson, C.A., McGeorge, G. et al. (2017). Calibration transfer of a quantitative transmission Raman PLS model: Direct transfer vs. global modeling. Journal of Pharmaceutical Innovation 12 (4):347-356.
Brouckaert, D., Uyttersprot, J.S., Broeckx, W., and De Beer, T. (2017 Sep). Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions between two at-line instruments installed at two liquid detergent production plants. Analytica Chimica Acta 984:1-18.
Fearn, T. (2001). Standardisation and calibration transfer for near infrared instruments: A review. Journal of Near Infrared Spectroscopy 9 (4): 229-244.
Workman, J.J. (2017). A review of calibration transfer practices and instrument differences in spectroscopy. Applied Spectroscopy 72 (3): 340-365.
General Chapter (2023). <1224> transfer of analytical procedures. In: USP-NF. United States Pharmacopeia: Rockville, MD.
Dewé, W., Govaerts, B., Boulanger, B. et al. (2007). Using total error as decision criterion in analytical method transfer. Chemometrics and Intelligent Laboratory Systems 85 (2): 262-268.
Rozet, E., Dewé, W., Ziemons, E. et al. (2009). Methodologies for the transfer of analytical methods:A review. Journal of Chromatography B 877 (23): 2214-2223.