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首页> 外文期刊>Journal of Pharmaceutical and Biomedical Analysis: An International Journal on All Drug-Related Topics in Pharmaceutical, Biomedical and Clinical Analysis >Method transfer of a near-infrared spectroscopic method for blend uniformity in a poorly flowing and hygroscopic blend
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Method transfer of a near-infrared spectroscopic method for blend uniformity in a poorly flowing and hygroscopic blend

机译:近红外光谱法在流动性和吸湿性混合中混合均匀性的方法转移

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摘要

The challenges in transferring and executing a near-infrared (NIR) spectroscopic method for croscarmellose (disintegrant) in binary blends for a continuous manufacturing (CM) process are presented. This work demonstrates the development of a NIR calibration model and its use to determine the blending parameters needed for binary blends at a development plant and later used to predict CM process blends. The calibration models were developed with laboratory scale powder blends ranging from 4.32%-64.77 (%w/w) of croscarmellose and evaluated using independent test blends. The selected model was then transferred to the continuous manufacturing development site to determine the croscarmellose concentration for spectra collected in real-time. A total of 18 development plant runs were monitored using an in -line NIR spectrometer, however, these spectra showed high baseline variations. The baseline variations were caused by the poor flow of the material within the system. An inconsistent bias which varied from 2.51 to 14.95 (%w/w) was observed in the predictions of croscarmellose. High baseline spectra were eliminated and the bias was significantly reduced by 42-51%. Experiments at lower flow rate speeds did not show significant changes in baseline and bias values showed more consistency. The calibration model was then transferred to two NIR spectrometers installed at-line at the commercial site, where powder samples were collected at the beginning middle and end of each CM plant run. The NIR calibration model predicted disintegrant concentration from the powder samples. Results showed the bias values for the NIR (1) varied from 0.74 to 2.21 (%w/w) and NIR (2) from 0.28 to 3.39 (%w/w). Average concentration values for both equipments were very close to the reference concentration values of 43.18 and 50.98 (%w/w). The study showed the model was able to identify flow issues, identified as baseline shifts, that could be used to alert to problems in the powder bed that may warrant diversion from a production line. These powder flow problems such as air gaps and inconsistent powder bed height affected the NIR spectra collected at the development plant and provided results with high bias. A lower bias was obtained in samples collected at line after blending. (C) 2019 Elsevier B.V. All rights reserved.
机译:介绍了在二元共混物中转移和执行近红外(NIR)光谱法的转移和执行近红外(NIR)光谱法,用于连续制造(CM)工艺的二元共混物。这项工作展示了NIR校准模型的开发及其用于确定开发工厂中二元混合物所需的混合参数,后来用于预测CM工艺混合物。校准模型采用实验室规模粉末共混物,范围为4.32%-64.77(%w / w)的Croscarmellose,并使用独立的测试混合物评估。然后将所选模型转移到连续制造开发部位,以确定实时收集的光谱的Croscarmellose浓度。使用在线NIR光谱仪中共监测总共18个开发植物运行,然而,这些光谱显示出高基线变化。基线变化是由系统内材料的不良流动引起的。在Croscarmellose的预测中观察到从2.51至14.95(%w / w)不同的不一致偏差。消除了高基线光谱,偏差明显减少42-51%。较低流速速度的实验没有显示基线的显着变化,并且偏差值显示出更多的一致性。然后将校准模型转移到在商业部位上安装的两个NIR光谱仪,其中在每个CM植物的开始中间和末端收集粉末样品。 NIR校准模型从粉末样品预测崩解剂浓度。结果显示NIR(1)的偏差值从0.74-2.21(%w / w),nir(2)为0.28至3.39(%w / w)。两种设备的平均浓度值非常接近43.18和50.98(%w / w)的参考浓度值。该研究表明,该模型能够识别被确定为基线偏移的流动问题,可用于警惕可能在生产线路转移的粉末床中的问题。这些粉末流动问题如空气间隙和粉末床高度影响了在开发厂收集的NIR光谱,并提供了高偏差的结果。在混合后在线收集的样品中获得较低的偏压。 (c)2019 Elsevier B.v.保留所有权利。

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