Robust near-infrared modeling for pharmaceutical powder streams: External variable augmented iterative optimization technology (EVA-IOT).

Abstract

The adoption of pure component models, such as iterative optimization technology (IOT) algorithms, is gaining significant interest in the pharmaceutical industry, primarily because of their calibration-free/minimal calibration requirements for process analytical technology applications. The IOT methods have recently demonstrated great potential for monitoring the quality of continuous powder mixtures by near-infrared (NIR) spectroscopy. However, the dynamic conditions of continuous manufacturing processes may limit the effectiveness of such approaches. Density variations introduced to NIR spectra that are collected from dynamic powder mixtures at different process conditions is detrimental to the drug prediction accuracy and robustness of IOT methods. This work introduces a new method, called external variable augmented iterative optimization technology (EVA-IOT), which incorporates the shape of non-chemical external sources of variability into the pure component spectra matrix to improve the prediction accuracy and robustness of the base IOT algorithm. This approach derives the shape of non-chemical external variables from the latent structure of decomposition methods using NIR spectra acquired from a single mixture at known levels of the external variable. A density-augmented EVA-IOT method was developed and implemented to quantify the active pharmaceutical ingredient (API) in continuous powder mixtures flowing at varying process conditions in a simulated continuous process. The EVA-IOT method demonstrated a significantly enhanced API prediction accuracy and robustness against process variation compared to alternative IOT methods. The overall prediction performance of EVA-IOT was comparable to that of global partial least square (PLS) regression models while reducing the calibration burden up to 97%. This makes EVA-IOT a material-sparing alternative to calibration-intensive robust decomposition modeling approaches for monitoring the quality of continuous pharmaceutical powder streams.