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用于构建阿莫西林和克拉维酸钾口服剂型定量模型的近红外光谱库的编制

Compilation of a Near-Infrared Library for Construction of Quantitative Models of Oral Dosage Forms for Amoxicillin and Potassium Clavulanate.

作者信息

Zou Wen-Bo, Chong Xiao-Meng, Wang Yan, Hu Chang-Qin

机构信息

Antibiotic Division, National Institutes for Food and Drug Control, Beijing China.

出版信息

Front Chem. 2018 May 24;6:184. doi: 10.3389/fchem.2018.00184. eCollection 2018.

Abstract

The accuracy of quantitative models for near-infrared (NIR) spectroscopy is dependent upon calibration samples with concentration variations. Conventional sample-collection methods have shortcomings (especially time-consumption), which creates a "bottleneck" in the application of NIR models for Process Analytical Technology (PAT) control. We undertook a study to solve the problem of sample collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms (ODFs) were used as examples. The aim of this study was to find an approach to construct NIR quantitative models rapidly using a NIR spectral library based on the idea of a universal model. The NIR spectral library of amoxicillin and potassium clavulanate ODFs was defined and comprised the spectra of 377 batches of samples produced by 26 domestic pharmaceutical companies, including tablets, dispersible tablets, chewable tablets, oral suspensions, and granules. The correlation coefficient (r) was used to indicate the similarities of the spectra. The calibration sets of samples were selected from a spectral library according to the median r of the samples to be analyzed. The r of the samples selected was close to the median r. The difference in r of these samples was 1.0-1.5%. We concluded that sample selection was not a problem when constructing NIR quantitative models using a spectral library compared with conventional methods of determining universal models. Sample spectra with a suitable concentration range in NIR models were collected rapidly. In addition, the models constructed through this method were targeted readily.

摘要

近红外(NIR)光谱定量模型的准确性取决于具有浓度变化的校准样品。传统的样品采集方法存在缺点(尤其是耗时),这在将NIR模型应用于过程分析技术(PAT)控制方面形成了一个“瓶颈”。我们开展了一项研究以解决用于构建NIR定量模型的样品采集问题。以阿莫西林和克拉维酸钾口服剂型(ODF)为例。本研究的目的是基于通用模型的理念,找到一种利用NIR光谱库快速构建NIR定量模型的方法。定义了阿莫西林和克拉维酸钾ODF的NIR光谱库,其包含了26家国内制药公司生产的377批次样品的光谱,这些样品包括片剂、分散片、咀嚼片、口服混悬液和颗粒剂。相关系数(r)用于表示光谱的相似性。根据待分析样品的r中位数从光谱库中选择样品校准集。所选样品的r接近r中位数。这些样品的r差异为1.0 - 1.5%。我们得出结论,与确定通用模型的传统方法相比,使用光谱库构建NIR定量模型时样品选择不是问题。在NIR模型中快速收集了具有合适浓度范围的样品光谱。此外,通过这种方法构建的模型易于靶向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93fd/5992406/c741b38c4c39/fchem-06-00184-g0001.jpg

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