Quantification of drug contents in molded tablets via transmission low-frequency Raman spectroscopy

Abstract

Orally disintegrating tablets (ODTs) are useful for elderly and pediatric patients suffering from difficulty in swallowing conventional tablets. Among various ODT preparing processes, wet tableting involves a process of the low-pressure compression of wet granules followed by drying, which is promising for the industrial production of medicines. Currently, the quality of wet molded tablets is analyzed via conventional high-performance liquid chromatography-based content uniformity tests. However, owing to the low compression pressure required for the wet tableting process, integrating spectroscopic probes with the tableting process can be a potential technique for real-time quality assurance. This study explores the use of transmission low-frequency Raman spectroscopy as a nondestructive and efficient approach for quantifying active pharmaceutical ingredients (APIs) in molded tablets. Acetaminophen and D-mannitol were used as the model API and excipient, respectively. Tablets were prepared by compressing under 150 and 300 N followed by analysis using the partial least squares regression method. The distinct spectral features of acetaminophen and D-mannitol enabled accurate quantification with good linear correlations (R2 = 0.98) and low root mean square prediction errors (1.59 for 150 N; 1.22 for 300 N). The method was effective under compressed forces, highlighting it as a real-time, nondestructive tool for quality control in pharmaceutical manufacturing. This approach aligns with continuous manufacturing and quality by design.

Graphical abstract: Quantification of drug contents in molded tablets via transmission low-frequency Raman spectroscopy

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Article information

Article type
Paper
Submitted
22 Jan 2025
Accepted
02 May 2025
First published
06 May 2025

Analyst, 2025, Advance Article

Quantification of drug contents in molded tablets via transmission low-frequency Raman spectroscopy

M. Inoue, T. Akiyama and T. Fukami, Analyst, 2025, Advance Article , DOI: 10.1039/D5AN00079C

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