Using data analytics to accelerate biopharmaceutical process scale-up

Pierantonio Facco

doi:10.1016/j.bej.2020.107791

2020-12-01

PRE

Pierantonio Facco *

Simeone Zomer

Ruth C. Rowland‐Jones

Douglas Marsh

Paloma Diaz‐Fernandez

Gary Finka

Fabrizio Bezzo

Massimiliano Barolo

DOI：10.1016/j.bej.2020.107791

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摘要

En 中文

In the biopharmaceutical industry, the selection of high-quality cell lines characterized by high productivity and stability over time plays a critical role for the development of processes meeting the desired quality and manufacturability criteria. Cell line selection involves their testing through subsequent stages at different process scales to progressively recreate the conditions of an industrial-scale bioreactor, from static micro-well plates, to shake flasks to reactors of increasing volumes and complexity. In this context both scale-up and scale-down are challenging due to the variety and quantity of data to mine and the scarcity of first-principles understanding on cells culture dynamics. This paper illustrates the use of advanced data-driven modeling where patterns across multiple scales are identified and interpreted to aid the cell line selection scale-up process. Methods such as multiway principal components analysis, Procrustes analysis and joint-Y projection to latent structures are used to characterize cell lines cross correlation and their dynamic behavior over time, and to carry out performance predictions across different scales. The approach has the potential to mature into a general framework to aid scale-up for cell-line selection and so drive acceleration in the development cycle of biopharmaceuticals through an optimal use of the available data acquisition platforms.

Keyword:

scale-up

scale-down

biopharmaceutical process

data analytics

multivariate statistical techniques

machine learning