Open Access
CC BY 4.0 · Pharmaceutical Fronts
DOI: 10.1055/a-2665-1298
Original Article

Quantitative Analysis of Pravastatin Sodium Polymorphs: a Comparative Study of Chemometric Techniques Combined with Powder X-ray Diffraction, Mid-Infrared, and Raman Spectroscopy

Yanyan Huang#
1   National Key Laboratory of Lead Druggability Research, Shanghai Institute of Pharmaceutical Industry, China State Institute of Pharmaceutical Industry, Shanghai, People's Republic of China
,
Chang Liu#
1   National Key Laboratory of Lead Druggability Research, Shanghai Institute of Pharmaceutical Industry, China State Institute of Pharmaceutical Industry, Shanghai, People's Republic of China
,
Hongjuan Pan
1   National Key Laboratory of Lead Druggability Research, Shanghai Institute of Pharmaceutical Industry, China State Institute of Pharmaceutical Industry, Shanghai, People's Republic of China
,
Dong Wang
2   BengBu Food and Drug Inspection Center, Antibiotic Room, Anhui, People's Republic of China
,
Jingjing Wei
3   National Institutes for Food and Drug Control, Chemical Drug Inspection Institute, Beijing, People's Republic of China
,
Jialiang Zhong
1   National Key Laboratory of Lead Druggability Research, Shanghai Institute of Pharmaceutical Industry, China State Institute of Pharmaceutical Industry, Shanghai, People's Republic of China
› Institutsangaben

Funding None.


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Abstract

Pravastatin sodium (PS) is a hydrophilic statin lipid-lowering drug that reduces low-density lipoprotein levels in the blood by inhibiting the activity of 3-hydroxy-3-methylglutaryl coenzyme A reductase. It is known to exist in 17 crystalline forms, with some different crystalline forms overlapping in the powder diffraction patterns, making it difficult to control the purity of the crystalline forms. In this study, we aimed to determine the purity of PS crystals using powder X-ray diffraction (PXRD), mid-infrared (MIR) spectroscopy, and Raman techniques. The predictive ability of the partial least squares (PLS) model was constructed and assessed using SPXY, K_S, and Random methods at different partitioning ratios. PLS calibration curves were established based on the relationship between PXRD, MIR, and Raman data and the content of a solid forms of PS (PS-A) in different ranges (full and partial spectra) using different preprocessing algorithms such as multiplicative scattering correction, standard normal variable, Savitzky–Golay filtering, and derivative spectroscopy, or a combination of them. The results showed that the calibration model (y = 0.999x + 0.008 with R 2 = 0.999) established using the PXRD method was better, with a low detection limit (1.52%) and quantification limit (4.60%). In addition, by analyzing the testing results of the blind sample, it was found that the confidence intervals of the predicted values of MIR and Raman were wider, indicating a large uncertainty of their parameter estimation. Therefore, it will be better to select the calibration model established by the PXRD method to determine the purity of PS in actual production. This can provide more reliable methodological support for the quality control of pharmaceutical products.

Supporting Information

This section includes (1) preparation process of pravastatin sodium A crystal, pravastatin sodium D crystal form, and binary mixture samples; (2) sample collection and preprocessing; (3) analysis of the results of different dataset division methods and division ratio of the sample; (4) quantitative model construction and evaluation; (5) the plot of PXRD raw data, mapping of MIR raw data and Raman raw data and trend plot of PLS modeling results at different scales of KS method and SPXY method ([Supplementary Figs. S1]–[S5] [available in online version]); and (6) samples used to build and validate quantitative PXRD models, MIR models, and Raman models ([Supplementary Tables S1]–-[S3] [available in online version]).


# These authors contributed equally to this work.


Supplementary Material



Publikationsverlauf

Eingereicht: 06. März 2025

Angenommen: 24. Juli 2025

Artikel online veröffentlicht:
22. August 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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