Abstract
The development of new biologics is becoming more challenging due to global competition and increased requirements for process understanding and assured quality in regulatory approval. As a result, there is a need for predictive, mechanistic process models. These reduce the resources and time required in process development, generating understanding, expanding the possible operating space, and providing the basis for a digital twin for automated process control. Monoclonal antibodies are an important representative of industrially produced biologics that can be used for a wide range of applications. In this work, the validation of a mechanistic process model with respect to sensitivity, as well as accuracy and precision, is presented. For the investigated process conditions, the concentration of glycine, phenylalanine, tyrosine, and glutamine have been identified as significant influencing factors for product formation via statistical evaluation. Cell growth is, under the investigated process conditions, significantly dependent on the concentration of glucose within the investigated design space. Other significant amino acids were identified. A Monte Carlo simulation was used to simulate the cultivation run with an optimized medium resulting from the sensitivity analysis. The precision of the model was shown to have a 95% confidence interval. The model shown here includes the implementation of cell death in addition to models described in the literature.
Highlights
In biopharmaceutical production, the time-to-market for new, innovative products is growing shorter and shorter [1,2]
Monoclonal antibodies are an important representative of industrially produced biologics that can be used for a wide range of applications
Mechanistic models are to be developed alongside or instead of resource-intensive experiments and subsequently used for optimization and control, the risk assessment of the model must be carried out according to the same principles as those used in an experimental process development [35]
Summary
The time-to-market for new, innovative products is growing shorter and shorter [1,2] In this context, process development in the upstream, which typically involves optimization of the medium, feeding strategy, and various process parameters such as pH, power input, etc., is very costly, as a large number of lengthy cultivation experiments, often based on statistical experimental design, have to be performed [3]. A typical example of biopharmaceuticals for the regulated market is monoclonal antibodies (mAb) These are usually produced using animal cells, mostly Chinese hamster ovary (CHO) cells [16,17]. Simple Monod models with static yield coefficients are often used [10,18] These are easy to determine from an already performed cultivation, there is no causality between the metabolism of the cell and biomass, as well as product formation. The model used in this work includes the implementation of cell death and is, to our knowledge, the first adaption to CHO-DG44 for mAb production in fed-batch cultivation
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