Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Copenhagen, Denmark.
AAPS PharmSciTech. 2012 Sep;13(3):747-55. doi: 10.1208/s12249-012-9796-1. Epub 2012 May 15.
Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q (2) of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.
水分含量和空气动力学粒径是喷雾干燥蛋白质配方的关键质量属性。在这项研究中，应用实验设计方法生产了用于肺部给药的喷雾干燥胰岛素粉末。近红外光谱（NIR）结合预处理和偏最小二乘投影到潜在结构（PLS）的多元分析形式用于将光谱数据与通过飞行时间原理测量的水分含量和空气动力学粒径相关联。用于预测水分含量的 PLS 模型基于 NIR 光谱中水分子的化学信息。使用热重分析作为参考方法，模型产生的预测误差（RMSEP）在 0.39%至 0.48%之间。用于预测空气动力学粒径的 PLS 模型基于 NIR 光谱中的基线偏移，预测误差在 0.27 至 0.48 μm 之间。喷雾干燥颗粒的形态对模型的预测能力有重大影响。对于具有 0.22 μm 校准误差（RMSECV）的球形颗粒，可以获得良好的预测模型，而褶皱颗粒则导致模型不那么稳健，Q（2）为 0.69。基于本研究的结果，NIR 是喷雾干燥过程过程分析和控制水分含量和粒径的合适工具，特别是对于光滑和球形颗粒。
