利用近红外化学成像技术对高孔药品中氢溴西酞普兰的可视化和定量分析。

IF 4.7 3区 医学 Q1 PHARMACOLOGY & PHARMACY
Ilari Ahola , Claus Cornett , Jukka Rantanen , Natalja Genina , José Manuel Amigo
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引用次数: 0

摘要

在早期的一项研究中,介绍了一种制造灵活和精确剂量的抗抑郁药氢溴西酞普兰(CHB)的新方法,该方法利用两种将CHB纳入剂型的方法。这种方法被称为可调模块化设计(TMD),采用冷冻干燥和喷墨打印含chb的墨水到模块上的chb预加载模块。在本研究中,利用近红外化学成像(NIR-CI)来可视化CHB在样品印刷表面的分布,并通过成像这些样品的横截面。此外,由于TMD样品将在护理点进行最佳生产,因此了解影响剂量验证快速、非破坏性定量模型开发的因素至关重要,以确保向患者提供高质量的药品。为此目的使用了最先进的推帚式NIR-CI仪器。采用行业标准偏最小二乘(PLS)和支持向量机(SVR)模型进行数据分析。此外,还探索了多变量曲线分辨率作为定性和定量建模工具。所得预测模型准确,定量SVR模型预测均方根误差低至0.21。此外,NIR-CI可以沿横截面和表面检测样品中的打印药物,从而使其成为TMD样品故障排除和质量控制的有力工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Visualization and quantification of citalopram hydrobromide within highly porous drug products by near-infrared chemical imaging

Visualization and quantification of citalopram hydrobromide within highly porous drug products by near-infrared chemical imaging
A novel method for the fabrication of flexible and precise doses of an antidepressant, citalopram hydrobromide (CHB), utilizing two approaches of incorporating CHB into the dosage form, was introduced in an earlier study. This method, called tunable modular design (TMD), employs CHB-preloaded modules done by freeze-drying together with inkjet printing of CHB-containing ink onto the modules. In this study, near-infrared chemical imaging (NIR-CI) was utilized to visualize the distribution of CHB both on the printed surface of the samples as well as through imaging the cross-section of these samples. Furthermore, as the TMD samples are to be optimally produced at the point of care, it is crucial to understand the factors affecting the development of fast, non-destructive, quantitative models for dose verification to ensure the delivery of high-quality drug products to patients. A state-of-the-art pushbroom NIR-CI instrument was utilized for this purpose. Industry-standard partial least squares (PLS) and support vector machine (SVR) models were implemented for data analysis. Furthermore, multivariate curve resolution was explored as both qualitative and quantitative modeling tools. The obtained prediction models were accurate, having the root mean square error of prediction of the quantitative SVR models as low as 0.21. Additionally, NIR-CI could detect the printed drug in the samples along the cross-section and on the surface, thus making it a potent tool for the troubleshooting and quality control of TMD samples.
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来源期刊
CiteScore
9.60
自引率
2.20%
发文量
248
审稿时长
50 days
期刊介绍: The journal publishes research articles, review articles and scientific commentaries on all aspects of the pharmaceutical sciences with emphasis on conceptual novelty and scientific quality. The Editors welcome articles in this multidisciplinary field, with a focus on topics relevant for drug discovery and development. More specifically, the Journal publishes reports on medicinal chemistry, pharmacology, drug absorption and metabolism, pharmacokinetics and pharmacodynamics, pharmaceutical and biomedical analysis, drug delivery (including gene delivery), drug targeting, pharmaceutical technology, pharmaceutical biotechnology and clinical drug evaluation. The journal will typically not give priority to manuscripts focusing primarily on organic synthesis, natural products, adaptation of analytical approaches, or discussions pertaining to drug policy making. Scientific commentaries and review articles are generally by invitation only or by consent of the Editors. Proceedings of scientific meetings may be published as special issues or supplements to the Journal.
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