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一种用于连续直接压缩制造过程中实时在线过程控制的混合近红外软传感器方法。

A hybrid NIR-soft sensor method for real time in-process control during continuous direct compression manufacturing operations.

机构信息

Worldwide Research and Development, Pfizer Inc., Groton, CT 06340, USA.

Worldwide Research and Development, Pfizer Inc., Groton, CT 06340, USA.

出版信息

Int J Pharm. 2021 Jun 1;602:120620. doi: 10.1016/j.ijpharm.2021.120620. Epub 2021 Apr 20.

Abstract

Near Infrared (NIR) spectroscopy is commonly utilized for continuous manufacturing as Process Analytical Technology (PAT) tool. This paper focus on a continuous direct compression manufacturing process, in which an NIR PAT probe is integrated into the tablet press feed frame and into the tablet diversion control system to ensure continuous monitoring of the potency and homogeneity of the blend within the process line. The quantification of NIR spectra is achieved through Partial Least-Squares (PLS) modeling, calibrated with offline analyzed tablet cores at different potency levels. Because the NIR measurements are often sensitive to sample physical properties caused by raw materials or process conditions, etc., adopting a data-driven approach will require a large amount of representative data throughout the method lifecycle. During the early stages of process development, whenever new uncaptured source of variability in the model space are encountered, the chemometric predictions can deviate from the offline reference, requiring frequent model updates. These deviations can be reduced by integrating process and physico-chemical knowledge in the on-line potency estimation. This paper presents a novel hybrid method combining the online NIR PLS and a potency soft sensor estimation, enabling a robust potency prediction whilst minimizing maintenance downtimes and facilitating cross-site method transfer.

摘要

近红外(NIR)光谱分析通常被用作连续制造的过程分析技术(PAT)工具。本文主要介绍一种连续直接压缩制造工艺,其中将 NIR PAT 探头集成到压片机进料框架和片剂分流控制系统中,以确保在整个工艺线上连续监测混合物的效力和均一性。通过偏最小二乘法(PLS)建模对 NIR 光谱进行定量,该模型使用不同效力水平的离线分析片剂芯进行校准。由于 NIR 测量通常对原材料或工艺条件等引起的样品物理性质敏感,因此采用数据驱动方法将需要在整个方法生命周期中获得大量具有代表性的数据。在工艺开发的早期阶段,每当在模型空间中遇到新的不可捕获的变化源时,化学计量预测就会偏离离线参考值,需要频繁更新模型。通过将过程和物理化学知识集成到在线效力估计中,可以减少这些偏差。本文提出了一种新颖的混合方法,将在线 NIR PLS 和效力软传感器估计相结合,在最小化维护停机时间和促进跨站点方法转移的同时,实现了强大的效力预测。

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