El-Hagrasy Arwa S, Drennen James K
Process Analytical Technology Group, Pharmaceutical Development Center of Excellence, Pharmaceutical Research Institute, Bristol-Myers Squibb, New Brunswick, New Jersey, USA.
J Pharm Sci. 2006 Feb;95(2):422-34. doi: 10.1002/jps.20465.
The Process Analytical Technology (PAT) initiative, undertaken by the Food and Drug Administration (FDA), paves the way for improvement of drug manufacturing through real-time measurements that allow better process understanding. This study is the third and final Part in a series of studies that represent an integrated approach for real-time blend uniformity assessment using near-infrared (NIR) technology. In this study, the development of a quantitative NIR model for prediction of blending end point is presented. Process signature was built into NIR calibration models by using blend samples that were collected from actual blend experiments under different processing conditions. Evaluation of various calibration algorithms including principal component regression (PCR), partial least squares (PLS), and multi-term linear regression (MLR) was performed. It was found that linear regression, using a single wavelength, yielded optimum calibration and prediction results. The blending profiles predicted by the NIR quantitative model correlated well to those determined by the UV reference analytical method. Characterization of intra-shell versus inter-shell powder mixing kinetics and its implication in sensor positioning was also performed and will be discussed.
美国食品药品监督管理局(FDA)发起的过程分析技术(PAT)计划,通过实时测量为改进药品制造铺平了道路,这种实时测量有助于更好地理解生产过程。本研究是一系列研究中的第三部分,也是最后一部分,这些研究代表了一种使用近红外(NIR)技术进行实时混合均匀度评估的综合方法。在本研究中,介绍了一种用于预测混合终点的定量近红外模型的开发。通过使用在不同加工条件下从实际混合实验中收集的混合样品,将过程特征纳入近红外校准模型。对包括主成分回归(PCR)、偏最小二乘法(PLS)和多项线性回归(MLR)在内的各种校准算法进行了评估。结果发现,使用单一波长的线性回归产生了最佳的校准和预测结果。近红外定量模型预测的混合曲线与紫外参考分析方法确定的曲线相关性良好。还进行了壳内与壳间粉末混合动力学的表征及其在传感器定位中的意义,并将进行讨论。