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

Abstract

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. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch‐to‐batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method.

Quantification of the dynamics of population heterogeneities in CHO cultures with stably integrated fluorescent markers

“…The identified population changes can affect process variability with different productivities in large scale, e.g., if cells are pooled from different bioreactors for inoculation [6,22]. Moreover, this study demonstrates the strong impact of preculture treatment on the bulk process performance and the need for an efficient seed train design [54] and scale-up strategy [55]. Regarding the application of the cell labeling concept, the changes in the bulk composition behavior would be not observable if the different populations were not individually marked.…”

Model uncertainty-based evaluation of process strategies during scale-up of biopharmaceutical processes

“…8 G) increased constantly up to 367 mg l −1 and is comparable to the formerly performed processes in smaller scales and other studies with the same cell line ( Möller et al, 2019a( Möller et al, , 2019b. The main advantage of predicting the 10% and 90% quantiles of the pilot scale based on the previously determined parameter distributions is that the experimental variability is incorporated, even if the process knowledge was gained at smaller scales ( Hernández Rodríguez et al, 2019;Xing et al, 2010 ). Furthermore, the a priori simulation of the scaled up process and its comparison with newly available data at the respective scale can be used to prove the current process understanding.…”

“…experimental variations) on model outcomes ( Anane et al, 2019;Liu and Gunawan, 2017;Sin et al, 2009 ). Uncertainty-based modeling techniques have been widely used in chemical systems or systems biology , but not often in bioprocess simulation studies ( Hernández Rodríguez et al, 2019 ).…”

Making sense of parameter estimation and model simulation in bioprocesses

“…In the biopharmaceuticals field, the application of statistical inferences has been reinforced by the U.S. Food and Drug Administration and the European Medicine agencies, with the main goal of improving the understanding of the processes and the robustness of product quality (Narayanan, Sokolov, Butté, & Morbidelli, 2019; Hernández Rodríguez et al, 2019). Partially motivated by this regulation, in recent years, some articles have been published in the field of bioprocess to address this issue.…”

“…Anane et al (2019) proposed a methodology, based on frequentist statistic, to detect nonidentifiable parameters and significantly reduce the output uncertainty of a mechanistic growth model. On the other hand, Hernández Rodríguez et al (2019) addressed the uncertainty on parameters and model outputs applying a Bayesian approach. Although both frameworks have the advantage of dealing together with measurement and model uncertainties, they require a deeper knowledge in statistics and coding for their implementation.…”