Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures.

Autor: Rish AJ; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA., Henson SR; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA., Velez-Silva NL; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA., Nahid Hasan M; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA., Drennen JK; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, USA., Anderson CA; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, USA. Electronic address: andersonca@duq.edu.
Jazyk: angličtina
Zdroj: International journal of pharmaceutics [Int J Pharm] 2023 Aug 25; Vol. 643, pp. 123261. Date of Electronic Publication: 2023 Jul 20.
DOI: 10.1016/j.ijpharm.2023.123261
Abstrakt: Process analytical technology (PAT) is an essential tool within pharmaceutical manufacturing to ensure consistent quality and maintain process control. Near-infrared (NIR) spectroscopy is one of the most popular PAT techniques, particularly for monitoring active pharmaceutical ingredient (API) concentrations. To interpret the spectral outputs of NIR spectroscopy, advanced multivariate models are required. Calibration-free models such as iterative optimization technology (IOT) algorithms are increasingly of interest, due primarily to their reduced material and time burdens. Variable/wavelength selection is a common method to improve prediction performance and robustness for IOT by focusing on spectral regions with the most relevant information. However, currently proposed wavelength selection approaches rely on training sets for optimization, therefore reducing or removing the advantages of IOT over empirical calibration-dependent models. In this work, a true calibration-free wavelength selection method is proposed based on measuring the difference between individual wavelengths of a mixture spectra and the net analyte signals via a wavelength angle mapper (WAM). An extension of the WAM utilizing a spectral window of wavelength instead of individual wavelengths, called SWAM, was also developed. However, the SWAM method does require a small training set to optimize wavelength selection parameters. The WAM and SWAM methods showed similar prediction performance for API in pharmaceutical powder blends when compared against other calibration-dependent models and the base IOT algorithm.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)
Databáze: MEDLINE
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