Model-Based Design of Process Strategies for Cell Culture Bioprocesses: State of the Art and New Perspectives

Abstract

Production processes for biopharmaceuticals with mammalian cells have to provide a nearly optimal environment to promote cell growth and product formation. Design and operation of a bioreactor are complex tasks, not only with respect to reactor configuration and size but also with respect to the mode of operation. New concepts for the design and layout of process strategies are required to meet regulatory demands and to guarantee efficient, safe, and reproducible biopharmaceutical production. Key elements are critical process parameters (CPPs), which affect critical quality attributes (CQAs), quality by design (QbD), process analytical tools (PAT), and design of experiment (DoE). In this chapter, some fundamentals including process and control strategies as well as concepts for process development are discussed. Examples for novel model-based concepts for the design of experiments to identify suitable fed-batch-feeding strategies are shown.

CRISPR/Cas9 gene editing for the creation of an MGAT1 deficient CHO cell line to control HIV-1 vaccine glycosylation

“…CHO cells have long been the substrate of choice for the production of therapeutic glycoproteins. This is due to their ability to grow at high densities in serum-free suspension cultures, sustain high levels of protein expression over prolonged fermentation cycles, and incorporate complex glycans on exogenously expressed proteins (24–26). Typical glycoproteins contain only a few N-linked glycans, which aid in protein folding, intracellular trafficking.…”

Anin vitro- agent-based modelling approach to optimisation of culture medium for generating muscle cells

“…Use of high throughput systems can reduce the amount and cost of trials required but are hampered when critical parameters are poorly understood. Numerical models of cell and tissue behaviours[18] present fast, low-cost methods for simulating in vitro experiments but require thorough calibration against experimental results to establish confidence in predictions. Here, we apply a combined in vitro-in silico approach in which experimental data is used to calibrate and refine a numerical model which can then be used to inform future in vitro experiments such as myogenesis, NMJ formation or disease modelling in an iterative fashion.…”

“…In vitro experiments are invaluable tools for exploring cell culture environments [4] but they remain expensive, time consuming and provide sparse data points for analysis. Numerical models of cell and tissue behaviours [5] present fast, low-cost methods for simulating in vitro experiments but require thorough calibration against experimental results to establish confidence in predictions.…”

Predicting industrial‐scale cell culture seed trains–A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method

“…A mechanistic model, similar to Frahm () and Kern, Platas‐Barradas, Pörtner, and Frahm () is applied for this approach. These types of models have gained renewed attention because they can be considered as a structured representation of the available process knowledge (Glassey et al, ; Kroll, Hofer, Stelzer, & Herwig, ; Möller & Pörtner, ; Sanderson, Phillips, & Barford, ). They have been used for development of predictive control strategies, for example, to ensure high batch‐to‐batch reproducibility in animal suspension cell cultures (Aehle et al, ) and for the design of cell culture fed‐batch control (Frahm et al, ).…”