November 1-5, 2015
Continuous multicolumn chromatography is gaining momentum in the industry as an enabling technology to establish continuous biomanufacturing platforms for monoclonal antibodies and recombinant proteins. Some companies are exploring continuous multicolumn chromatography processes as a stand-alone unit operation to improve specific productivity and/or reduce buffer consumption, while other companies are considering an integrated continuous downstream process based on continuous multicolumn chromatography.
One of the key features of continuous multicolumn chromatography is the amount of data generated during a single manufacturing batch or campaign. Traditional chromatography processes typically yield one (or a few) elution peaks per batch. Continuous chromatography processes, on the other hand, generates 30 to 100 elution peaks per batch. In addition to this, multicolumn chromatography systems typically have four to five times more sensors and hence collect much more information on the process.
The enormous amount of information generated during a single batch and/or throughout a campaign provides an opportunity to monitor the process consistency in many ways. In this presentation, we will discuss the use of multivariate data analysis for monitoring the column performance throughout a prolonged series of BioSMB experiments with Protein A chromatography.
In the presentation we will demonstrate how multivariate data analysis allows immediate identification of deviations in the process. Malfunctioning of the equipment and/or columns will immediately show up in the principal component analysis of various sensor signals. This strategy will also allow trending of the column performance and establish a means to determine column lifetime in a continuous process. In addition to this, the principal component analysis will be correlated with critical quality attributes such as the impurity profile of the eluate.
The presentation will discuss the potential application of the outcome of multivariate data analysis to establish control strategies for multicolumn chromatography processes.
Engin Ayturk, "Leveraging large data sets in continuous chromatography applications: Monitoring critical process parameters using MVDA" in "Integrated Continuous Biomanufacturing II", Chetan Goudar, Amgen Inc. Suzanne Farid, University College London Christopher Hwang, Genzyme-Sanofi Karol Lacki, Novo Nordisk Eds, ECI Symposium Series, (2015). http://dc.engconfintl.org/biomanufact_ii/84