Modeling Suspension Cultures of Microbial and Mammalian Cells with an Adaptable Six-Compartment Model

About Model Validation in Bioprocessing

In bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guidelines on how models for QbD are validated are still missing. In this review we provide a comprehensive overview of the validation methods, mathematical approaches, and metrics currently applied in bioprocess modeling. The methods cover analytics for data used for modeling, model training and selection, measures for predictiveness, and model uncertainties. We point out the general issues in model validation and calibration for different types of models and put this into the context of existing health authority recommendations. This review provides a starting point for developing a guide for model validation approaches. There is no one-fits-all approach, but this review should help to identify the best fitting validation method, or combination of methods, for the specific task and the type of bioprocess model that is being developed.

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. Modeling of such processes is an important task since these systems are usually nonlinear and complex. In this chapter, a framework for the estimation of process model parameters and its implementation is shown. It is aimed to support the parameter estimation task, which increases the potential of implementation and improvement of mathematical process models into the novel and existing bioprocesses. Apart from the parameter estimation, evaluation of the estimated parameters plays an essential role in order to verify these parameters and subsequently the selected model. The workflow is outlined and shown specifically on the basis of a mathematical process model describing a mammalian cell culture batch process.

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.org/10.1007/s00449-019-02089-7 , 2019) was extended and implemented into a software ("mDoE-toolbox") to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.

Digital Twins for Bioprocess Control Strategy Development and Realisation

New innovative Digital Twins can represent complex bioprocesses, including the biological, physico-chemical, and chemical reaction kinetics, as well as the mechanical and physical characteristics of the reactors and the involved peripherals. Digital Twins are an ideal tool for the rapid and cost-effective development, realisation and optimisation of control and automation strategies. They may be utilised for the development and implementation of conventional controllers (e.g. temperature, dissolved oxygen, etc.), as well as for advanced control strategies (e.g. control of substrate or metabolite concentrations, multivariable controls), and the development of complete bioprocess control. This chapter describes the requirements Digital Twins must fulfil to be used for bioprocess control strategy development, and implementation and gives an overview of research projects where Digital Twins or "early-stage" Digital Twins were used in this context. Furthermore, applications of Digital Twins for the academic education of future control and bioprocess engineers as well as for the training of future bioreactor operators will be described. Finally, a case study is presented, in which an "early-stage" Digital Twin was applied for the development of control strategies of the fed-batch cultivation of Saccharomyces cerevisiae. Development, realisation and optimisation of control strategies utilising Digital Twins.

Digital Twins and Their Role in Model-Assisted Design of Experiments

Rising demands for biopharmaceuticals and the need to reduce manufacturing costs increase the pressure to develop productive and efficient bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive expert knowledge about the investigated factors and their boundary values and often leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in model-assisted DoE (mDoE) can be used as an alternative to decrease the number of experiments significantly. mDoE enables a knowledge-driven bioprocess development including the definition of a mathematical process model in the early development stages. In this chapter, digital twins and their role in mDoE are discussed. First, statistical DoE methods are introduced as the basis of mDoE. Second, the combination of a mathematical process model and DoE into mDoE is examined. This includes mathematical model structures and a selection scheme for the choice of DoE designs. Finally, the application of mDoE is discussed in a case study for the medium optimization in an antibody-producing Chinese hamster ovary cell culture process.