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API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis

The process analytical technology (PAT) initiative proposed by the US Food and Drug Administration (FDA) suggests innovative methods to better understand pharmaceutical processes. The development of analytical methods that quantify active pharmaceutical ingredients (APIs) in powders and tablets is f...

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Autores principales: Villanueva-López, Vladimir, Pacheco-Londoño, Leonardo C., Villarreal-González, Reynaldo, Castro-Suarez, John R., Román-Ospino, Andrés, Ortiz-Rivera, William, Galán-Freyle, Nataly J., Hernandez-Rivera, Samuel P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309115/
https://www.ncbi.nlm.nih.gov/pubmed/34209940
http://dx.doi.org/10.3390/pharmaceutics13070985
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author Villanueva-López, Vladimir
Pacheco-Londoño, Leonardo C.
Villarreal-González, Reynaldo
Castro-Suarez, John R.
Román-Ospino, Andrés
Ortiz-Rivera, William
Galán-Freyle, Nataly J.
Hernandez-Rivera, Samuel P.
author_facet Villanueva-López, Vladimir
Pacheco-Londoño, Leonardo C.
Villarreal-González, Reynaldo
Castro-Suarez, John R.
Román-Ospino, Andrés
Ortiz-Rivera, William
Galán-Freyle, Nataly J.
Hernandez-Rivera, Samuel P.
author_sort Villanueva-López, Vladimir
collection PubMed
description The process analytical technology (PAT) initiative proposed by the US Food and Drug Administration (FDA) suggests innovative methods to better understand pharmaceutical processes. The development of analytical methods that quantify active pharmaceutical ingredients (APIs) in powders and tablets is fundamental to monitoring and controlling a drug product’s quality. Analytical methods based on vibrational spectroscopy do not require sample preparation and can be implemented during in-line manufacturing to maintain quality at each stage of operations. In this study, a mid-infrared (MIR) quantum cascade laser (QCL) spectroscopy-based protocol was performed to quantify ibuprofen in formulations of powder blends and tablets. Fourteen blends were prepared with varying concentrations from 0.0% to 21.0% (w/w) API. MIR laser spectra were collected in the spectral range of 990 to 1600 cm(−1). Partial least squares (PLS) models were developed to correlate the intensities of vibrational signals with API concentrations in powder blends and tablets. PLS models were evaluated based on the following figures of merit: correlation coefficient (R(2)), root mean square error of calibration, root mean square error of prediction, root mean square error of cross-validation, and relative standard error of prediction. QCL assisted by multivariate analysis was demonstrated to be accurate and robust for analysis of the content and blend uniformity of pharmaceutical compounds.
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spelling pubmed-83091152021-07-25 API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis Villanueva-López, Vladimir Pacheco-Londoño, Leonardo C. Villarreal-González, Reynaldo Castro-Suarez, John R. Román-Ospino, Andrés Ortiz-Rivera, William Galán-Freyle, Nataly J. Hernandez-Rivera, Samuel P. Pharmaceutics Article The process analytical technology (PAT) initiative proposed by the US Food and Drug Administration (FDA) suggests innovative methods to better understand pharmaceutical processes. The development of analytical methods that quantify active pharmaceutical ingredients (APIs) in powders and tablets is fundamental to monitoring and controlling a drug product’s quality. Analytical methods based on vibrational spectroscopy do not require sample preparation and can be implemented during in-line manufacturing to maintain quality at each stage of operations. In this study, a mid-infrared (MIR) quantum cascade laser (QCL) spectroscopy-based protocol was performed to quantify ibuprofen in formulations of powder blends and tablets. Fourteen blends were prepared with varying concentrations from 0.0% to 21.0% (w/w) API. MIR laser spectra were collected in the spectral range of 990 to 1600 cm(−1). Partial least squares (PLS) models were developed to correlate the intensities of vibrational signals with API concentrations in powder blends and tablets. PLS models were evaluated based on the following figures of merit: correlation coefficient (R(2)), root mean square error of calibration, root mean square error of prediction, root mean square error of cross-validation, and relative standard error of prediction. QCL assisted by multivariate analysis was demonstrated to be accurate and robust for analysis of the content and blend uniformity of pharmaceutical compounds. MDPI 2021-06-29 /pmc/articles/PMC8309115/ /pubmed/34209940 http://dx.doi.org/10.3390/pharmaceutics13070985 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Villanueva-López, Vladimir
Pacheco-Londoño, Leonardo C.
Villarreal-González, Reynaldo
Castro-Suarez, John R.
Román-Ospino, Andrés
Ortiz-Rivera, William
Galán-Freyle, Nataly J.
Hernandez-Rivera, Samuel P.
API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis
title API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis
title_full API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis
title_fullStr API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis
title_full_unstemmed API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis
title_short API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis
title_sort api content and blend uniformity using quantum cascade laser spectroscopy coupled with multivariate analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309115/
https://www.ncbi.nlm.nih.gov/pubmed/34209940
http://dx.doi.org/10.3390/pharmaceutics13070985
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