Resolving Analytical Challenges in Pharmaceutical Process Monitoring Using Multivariate Analysis Methods: Applications in Process Understanding, Control, and Improvement

Abstract

Multivariate analysis (MVA) refers to an assortment of statistical tools developed to handle situations in which more than one variable is involved. MVA is indispensable for data interpretation and for extraction of meaningful data, especially from fast acquisition instruments and spectral imaging techniques. This article reviews trends in the application of MVA to pharmaceutical manufacturing and control. The MVA models most commonly used in drug analysis are compared. The potential of MVA to resolve analytical challenges, such as overcoming matrix effects, extracting reliable data from dynamic matrices, clustering data into meaningful groups, removing noise from analytical response, resolving spectral overlaps, and providing simultaneous analysis of multiple components, are tackled with examples. Industrial applications of MVA capabilities are described, with special emphasis on process analytical technology (PAT) and how MVA can aid in process understanding and control. A scheme for selecting an MVA model according to the available data and the required information is proposed.