Near-Infrared Hyperspectral Imaging as a Monitoring Tool for On-Demand Manufacturing of Inkjet-Printed Formulations

被引:14
|
作者
Stranzinger, Sandra [1 ]
Wolfgang, Matthias [1 ]
Klotz, Emma [2 ]
Scheibelhofer, Otto [1 ]
Ghiotti, Patrizia [3 ]
Khinast, Johannes G. [1 ,4 ]
Hsiao, Wen-Kai [1 ]
Paudel, Amrit [1 ,4 ]
机构
[1] Res Ctr Pharmaceut Engn RCPE GmbH, Inffeldgasse 13, A-8010 Graz, Austria
[2] Graz Univ Technol, Inst Med Engn, Stremayrgasse 16, A-8010 Graz, Austria
[3] UCB Pharma SA, Allee Rech 60, B-1070 Brussels, Belgium
[4] Graz Univ Technol, Inst Proc & Particle Engn, Inffeldgasse 13, A-8010 Graz, Austria
关键词
Near-infrared hyperspectral imaging (NIR-HSI); Inkjet technology; Predictive models; Process Analytical Technology (PAT); Personalized medicine; DOSAGE FORMS; QUALITY-CONTROL; DRUG-DELIVERY; SPECTROSCOPY; IDENTIFICATION; FILMS;
D O I
10.1208/s12249-021-02091-x
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
This study evaluates the potential use of near-infrared hyperspectral imaging (NIR-HSI) for quantitative determination of the drug amount in inkjet-printed dosage forms. We chose metformin hydrochloride as a model active pharmaceutical ingredient (API) and printed it onto gelatin films using a piezoelectric inkjet printing system. An industry-ready NIR-HSI sensor combined with a motorized movable linear stage was applied for spectral acquisition. Initial API-substrate screening revealed best printing results for gelatin films with TiO2 filling. For calibration of the NIR-HSI system, escalating drug doses were printed on the substrate. After spectral pre-treatments, including standard normal variate (SNV) and Savitzky-Golay filtering for noise reduction and enhancement of spectral features, principal component analysis (PCA) and partial least squares (PLS) regression were applied to create predictive models for the quantification of independent printed metformin hydrochloride samples. It could be shown that the concentration distribution maps provided by the developed HSI models were capable of clustering and predicting the drug dose in the formulations. HSI model prediction showed significant better correlation to the reference (HPLC) compared to on-board monitoring of dispensed volume of the printer. Overall, the results emphasize the capability of NIR-HSI as a fast and non-destructive method for the quantification and quality control of the deposited API in drug-printing applications.
引用
收藏
页数:10
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