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缬沙坦口腔膜剂的制备与表征及图像分析的辅助支持

Formulation and Characterization of Buccal Films Containing Valsartan with Additional Support from Image Analysis.

作者信息

Grilc Blaž, Felicijan Tjaša, Parfant Timeja Planinšek, Planinšek Odon

机构信息

Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, 1000, Ljubljana, Slovenia.

Department of Biopharmaceutics and Pharmacokinetics, Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, 1000, Ljubljana, Slovenia.

出版信息

AAPS PharmSciTech. 2023 Mar 3;24(3):72. doi: 10.1208/s12249-023-02537-4.

DOI:10.1208/s12249-023-02537-4
PMID:36869211
Abstract

The present study was aimed to the development and characterization of valsartan-containing buccal films with an introduction to a novel technique of image analysis. Visual inspection of the film provided a wealth of information that was difficult to quantify objectively. The obtained images of the films observed under the microscope were embedded in a convolutional neural network (CNN). The results were clustered according to their visual quality and on the basis of data distances. Image analysis proved to be a promising method to characterize buccal films appearance and their visual properties. The differential behavior of film composition was investigated using a reduced combinatorial experimental design. Formulation properties such as dissolution rate, moisture content, valsartan particle size distribution, film thickness, and drug assay were evaluated. In addition, more advanced methods such as Raman microscopy and image analysis were used to characterize the developed product in more detail. The results of dissolution tests using four different dissolution apparatuses showed a significant difference between the formulations containing the active ingredient in different polymorphic states. The dynamic contact angle of a water droplet on the surface of the films was measured, which correlated well with the dissolution times at 80% of the released drug (t80).

摘要

本研究旨在开发含缬沙坦的口腔膜并对其进行表征,同时引入一种新型图像分析技术。对膜的目视检查提供了大量难以客观量化的信息。在显微镜下观察到的膜的图像被嵌入卷积神经网络(CNN)。结果根据其视觉质量并基于数据距离进行聚类。图像分析被证明是表征口腔膜外观及其视觉特性的一种有前途的方法。使用简化组合实验设计研究了膜组合物的差异行为。评估了诸如溶解速率、水分含量、缬沙坦粒度分布、膜厚度和药物含量等制剂特性。此外,还使用了诸如拉曼显微镜和图像分析等更先进的方法来更详细地表征所开发的产品。使用四种不同溶出装置进行的溶出试验结果表明,含有不同多晶型状态活性成分的制剂之间存在显著差异。测量了水滴在膜表面的动态接触角,其与80%药物释放时的溶出时间(t80)相关性良好。

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