Modeling of bioprocess pre-stages for optimization of perfusion profiles and increased process understanding.

Improving bioprocess efficiency is important to reduce the current costs of biologics on the market, bring them faster to the market, and to improve the environmental footprint. The process intensification efforts were historically focused on the main stage, while intensification of pre-stages has started to gain attention only in the past decade. Performing bioprocess pre-stages in the perfusion mode is one of the most efficient options to achieve higher viable cell densities over traditional batch methods.

View on a mechanistic model of Chlorella vulgaris in incubated shake flasks.

Kinetic growth models are a useful tool for a better understanding of microalgal cultivation and for optimizing cultivation conditions. The evaluation of such models requires experimental data that is laborious to generate in bioreactor settings. The experimental shake flask setting used in this study allows to run 12 experiments at the same time, with 6 individual light intensities and light durations.

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses.

Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train.

Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds).

Digital Seed Train Twins and Statistical Methods.

Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions.

Design, Optimization, and Adaptive Control of Cell Culture Seed Trains.

For the production of biopharmaceuticals, a procedure called seed train or inoculum train is required to generate an adequate number of cells for the inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for seed train mapping, directed modeling, and simulation as well as its optimization regarding selected optimization criteria such as optimal points in time for cell passaging.

Estimation of Process Model Parameters.

Cell culture technology has become a substantial domain of modern biotechnology, particularly in the pharmaceutical market. Today, products manufactured from cells itself dominate the biopharmaceutical industry. In addition, a limited number of products made of in vitro cultivated cells for regenerative medicine were launched to the market.

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.

For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model-assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high-quality data is available, and description of inference uncertainty, providing, apart from a "best fit"-prediction, information about the probable deviation in form of a prediction interval.