Applications of Multivariate Data Analysis In Pharmaceutical Process Development: Traditional and Advanced Multivariate Methods

The application of multivariate latent variable methods (PCA, PLS, LPLS, JYPLS, OPLS ..) has dramatically increased in the pharmaceutical sector after the introduction of concepts like Process Analytical Technology and Quality by Design . Early applications of these techniques were heavily concentrated on pure Chemometrics for the development of soft sensors or monitoring. Applications have since evolved from pure Chemometrics isolated from process engineering, to an integral usage of the data from analytical instruments combined with data and models from the process engineering perspective to derive fundamental process understanding from a system. This session seeks submissions where multivariate methods where used to understand analytical information (NIR, UV, HPLC, NMR, FBRM,…etc) in combination with process engineering principles to support or derive process understanding, ultimately seeking for a valid design space. Examples of this work include (but not limited to): Reaction kinetic studies supported by spectra; drug product and process performance and using intermediate complex analytical data; and scale-up.