title: Multivariate data analysis as a PAT tool for early bioprocess development data.
abstract: Early development datasets are typically unstructured, incomplete and truncated, yet they are readily available and contain relevant process information which is not extracted using classical data analysis techniques. In this paper, we illustrate the power of multivariate data analysis (MVDA) as a Process Analytical Technology tool to analyze early development data of a PER.C6® cell cultivation process. MVDA increased our understanding of the process studied. Principal component analysis enabled a thorough exploration of the dataset, identifying causes for batch deviations and revealing sensitivity of the process to scale. These findings were previously undetected using traditional univariate analysis. The lack of structure and gaps in the early development datasets made it impossible to fit them to more advanced partial least square regression models. This paper clearly shows that MVDA should be routinely used to analyze early development data to reveal relevant information for later development and scale-up. The value of these early development runs can be greatly enhanced if the experiments are well-structured and accompanied with full process analytics. This up-front investment will result in shorter and more efficient process development paths, resulting in lower overall development costs for new biopharmaceutical products.
authors.affiliation: Crucell Holland BV, Process Development Department, Archimedesweg 4-6, 2333 CN Leiden, The Netherlands. smercier@its.jnj.com
authors.forename: Sarah M
authors.initials: SM
authors.lastname: Mercier
authors.forename: Bas
authors.initials: B
authors.lastname: Diepenbroek
authors.forename: Marcella C F
authors.initials: MC
authors.lastname: Dalm
authors.forename: Rene H
authors.initials: RH
authors.lastname: Wijffels
authors.forename: Mathieu
authors.initials: M
authors.lastname: Streefland
mesh_terms: D019149:Bioreactors; D001709:Biotechnology; D018929:Cell Culture Techniques; D002461:Cell Line, Transformed; D019295:Computational Biology; D003625:Data Collection; D016208:Databases, Factual; D006801:Humans; D016018:Least-Squares Analysis; D008954:Models, Biological; D015999:Multivariate Analysis; D025341:Principal Component Analysis
keywords: ATF; Bioprocessing; CPP; CQA; Cell cultivation; DO; DoE; Early development; FDA; Food and Drug Administration; MFCS; MVDA; Multivariate data analysis; PAT; PC; PCA; PDT; PID; PLS; Process Analytical Technology; QbD; Quality by design; alternating tangential flow; critical process attribute; critical process parameter; design of experiment; dissolved oxygen; multi-fermentation control system; multivariate data analysis; partial least square; population doubling time; principal component; principal component analysis; proportional-integral-derivative; quality by design