Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments

Abstract

Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE) (intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development burden. The iDoE can provide more process response information in less overall process time, whereas hybrid modeling serves as a commodity to describe this behavior the best way. Therefore, a combination of both approaches appears beneficial for faster design screening and is especially of interest at larger scales where the costs per experiment rise significantly. Ideally, profound process knowledge is gathered at a small scale and only complemented with few validation experiments on a larger scale, saving valuable resources. In this work, the transferability of hybrid modeling for Chinese hamster ovary cell bioprocess development along process scales was investigated. A two-dimensional DoE was fully characterized in shake flask duplicates (300 ml), containing three different levels for the cultivation temperature and the glucose concentration in the feed. Based on these data, a hybrid model was developed, and its performance was assessed by estimating the viable cell concentration and product titer in 15 L bioprocesses with the same DoE settings. To challenge the modeling approach, 15 L bioprocesses also comprised iDoE runs with intra-experimental CPP shifts, impacting specific cell rates such as growth, consumption, and formation. Subsequently, the applicability of the iDoE cultivations to estimate static cultivations was also investigated. The shaker-scale hybrid model proved suitable for application to a 15 L scale (1:50), estimating the viable cell concentration and the product titer with an NRMSE of 10.92% and 17.79%, respectively. Additionally, the iDoE hybrid model performed comparably, displaying NRMSE values of 13.75% and 21.13%. The low errors when transferring the models from shaker to reactor and between the DoE and the iDoE approach highlight the suitability of hybrid modeling for mammalian cell culture bioprocess development and the potential of iDoE to accelerate process characterization and to improve process understanding.

Antibody glycation during a Chinese hamster ovary fed‐batch process is following a constrained second order reaction

“…In this case, a chemical process in the supernatant was investigated. Biological processes could lead to different results in this constellation, but it has been shown elsewhere that transferability of iDoE driven data is also possible at various other levels using hybrid modeling strategies 24,25 . In agreement with proposed models 11,12 our developed glycation model does not take into account different glycation sites.…”

“…To achieve an understanding of glycation shown elsewhere that transferability of iDoE driven data is also possible at various other levels using hybrid modeling strategies. 24,25 In agreement with proposed models 11,12 our developed glycation model does not take into account different glycation sites. In particular, the evolved model is proposed for glycation monitoring and prediction of fermentation processes.…”

Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths

“…Thus, it is of high importance to thoroughly consider the key process variables by means of risk analysis of potential CPPs to set up the experimentsand the design space, capturing the process behavior and enabling successful process characterization. The more important factors are considered the more experimental effort will be required but the subsequent models can easier be applied during scale‐up [ 18 ] or as model predictive control applications in the future. Thereby an expected variation (e.g., pump failures) in one variable can potentially be corrected by adaptation of the other CPPs.…”

“…These rates were embedded in the subsequent mechanistic part to propagated predictions as a function of the model inputs (Equations [14][15][16][17][18][19][20].…”

“…The design space is created by selecting critical process parameters (CPP) of the bioprocess, for example, cultivation temperature, dissolved oxygen setpoint, media or feed components, and feeding rates. [13,17,18] These CPPs supposedly have a crucial impact on critical quality attributes (CQA) of the biomanufacturing process and the final product, [6] for example, viable cells, product titer, or purity. Thoughtful planning and execution of these experiments, mostly using Design of Experiments (DoE), are of high importance to create and maximize the added value obtained from modeling.…”