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评估传输拉曼光谱和近红外高光谱成像在固体口服剂型含量均匀度评估中的应用。

Evaluation of Transmission Raman spectroscopy and NIR Hyperspectral Imaging for the assessment of content uniformity in solid oral dosage forms.

机构信息

Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany.

Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany.

出版信息

Eur J Pharm Sci. 2021 Nov 1;166:105963. doi: 10.1016/j.ejps.2021.105963. Epub 2021 Aug 3.

Abstract

PURPOSE

The objective of the present study was to explore and compare fast and non-destructive Transmission Raman Spectroscopy (TRS) and Near Infrared Hyperspectral imaging (NIR HSI) for the development of predictive quantitative methods to determine content uniformity (CU) of tablets.

METHODS

A set of single Active Pharmaceutical Ingredients (API) tablets with nine concentration levels of caffeine ranging from 12.75%w/w to 17.75%w/w and another set of double API tablets with five concentration levels of model API A* (5.25%w/w - 9.25%w/w) and caffeine (7%w/w - 13%w/w) were prepared. Chemometric prediction models were developed using partial least square (PLS 1) and later tested using a test set for both single and double API tablets.

RESULTS

Calibration PLS1 models were developed for both single and double APIs using a combination of S-G 1st derivative and SNV data pre-processing steps that offer an optimal model performance with the lowest cross-validation error and bias. The root mean square error of prediction (RMSEP) for the PLS1 model for single API caffeine tablets using TRS and NIR HSI was 0.27% and 0.36% respectively. The RMSEP for the PLS1 models built using TRS for the double API tablets was 0.29% for API A and 0.34% for caffeine. Similarly, for the NIR HIS prediction models the RMSEP was 0.43% for API A and 0.56% for caffeine.

CONCLUSION

Overall TRS presented a 25-30% more accurate prediction capability compared to NIR HSI in this specific sample sets. Nevertheless, both TRS ad NIR HSI possess the potential to be employed as rapid, nondestructive techniques to replace classical wet- chemistry methods for at- or off-line determination of tablet CU.

摘要

目的

本研究的目的是探索和比较快速无损透射拉曼光谱(TRS)和近红外高光谱成像(NIR HSI),以开发用于确定片剂含量均匀度(CU)的预测定量方法。

方法

制备了一组含有 9 种浓度水平咖啡因(从 12.75%w/w 到 17.75%w/w)的单活性药物成分(API)片剂和一组含有 5 种浓度水平模型 API A*(5.25%w/w - 9.25%w/w)和咖啡因(7%w/w - 13%w/w)的双 API 片剂。使用偏最小二乘法(PLS1)建立化学计量预测模型,然后使用单和双 API 片剂的测试集进行测试。

结果

使用 S-G 一阶导数和 SNV 数据预处理步骤的组合,为单和双 API 开发了 PLS1 校准模型,该组合提供了具有最低交叉验证误差和偏差的最佳模型性能。使用 TRS 和 NIR HSI 对单 API 咖啡因片剂的 PLS1 模型进行预测的均方根误差(RMSEP)分别为 0.27%和 0.36%。使用 TRS 为双 API 片剂建立的 PLS1 模型的 RMSEP 分别为 API A 的 0.29%和咖啡因的 0.34%。同样,对于 NIR HIS 预测模型,RMSEP 分别为 API A 的 0.43%和咖啡因的 0.56%。

结论

总体而言,与 NIR HSI 相比,TRS 在该特定样本集中具有 25-30%更高的预测能力。尽管如此,TRS 和 NIR HSI 都有可能被用作快速、无损的技术,以替代经典的湿法化学方法,用于在或离线测定片剂的 CU。

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