Digital Twins in Biomanufacturing

Zobel-Roos, Steffen; Schmidt, A.; Uhlenbrock, Lukas; Ditz, Reinhard; Köster, Dirk; Strube, Jochen

doi:10.1007/10_2020_146

Cited by 34 publications

(41 citation statements)

References 115 publications

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“…Compared with the experimental approach, this offers the advantage that many parameter combinations can be screened within a short time [12,13]. For later automation, the process model also serves as the basis of the digital twin [14]. In the course of validating the process model, mechanistic understanding of the process can also be generated, which reduces hurdles in approval and expands the possible operating range [15].…”

Section: Introductionmentioning

confidence: 99%

Towards Autonomous Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing Using a Dynamic Metabolic Model

2022

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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.

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Section: Introductionmentioning

confidence: 99%

Towards Autonomous Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing Using a Dynamic Metabolic Model

2022

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“…Also, it overlooks inter-dependencies among system variables, which impedes linking experimental results with process designs controlling multiple variables at a time. This can result in sub-optimal processes [26].…”

Section: Introductionmentioning

confidence: 99%

“…Automation [10] and digitalization [9, 26] make trial-and-error approaches more efficient, reducing operator-dependency and human errors, thus supporting process tracking and control. This dramatically increases the yield of in vitro experimental campaigns, allowing a more significant number of experiments, thus a broader exploration of the design space.…”

Section: Introductionmentioning

confidence: 99%

A methodology for co-simulation-based optimization of biofabrication protocols

Giannantoni

Bardini

Carlo

2022

Preprint

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Biofabrication processes are complex and often unsatisfactory. Trial-and-error methods are costly and yield only incremental innovation, starting from sub-optimal and poorly represented existing processes. Although computational techniques might support efficient process design to find optimal process configurations, intelligent computational approaches must comprise biological complexity to provide meaningful insights. This paper proposes a novel co-simulation-based optimization methodology for the systematic design of protocols for cell culture and biofabrication. The proposed strategy integrates evolutionary computation and simulation for efficient design space exploration and assessment of candidate protocols. A generic library supports the modular and flexible composition of multiscale and multidomain co-simulation scenarios. The feasibility of the presented approach was demonstrated in the automatic generation of protocols for the biofabrication of an epithelial cell monolayer. The results are twofold. First, the prototype co-simulation library helps build flexible, loosely coupled simulation scenarios. Second, the in-silico experimentation on the use case shows that the proposed approach is a viable first step towards standard and automated design in biofabrication.

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“…The next research steps will focus on further experimental validation of the models at the lab scale to test the robustness of the models when applied as digital twins in combination with existing PAT strategies [16,48,49,[76][77][78], for advanced process control towards autonomous operation manufacturing concepts.…”

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confidence: 99%

Digital Twins for Continuous mRNA Production

et al. 2021

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The global coronavirus pandemic continues to restrict public life worldwide. An effective means of limiting the pandemic is vaccination. Messenger ribonucleic acid (mRNA) vaccines currently available on the market have proven to be a well-tolerated and effective class of vaccine against coronavirus type 2 (CoV2). Accordingly, demand is presently outstripping mRNA vaccine production. One way to increase productivity is to switch from the currently performed batch to continuous in vitro transcription, which has proven to be a crucial material-consuming step. In this article, a physico-chemical model of in vitro mRNA transcription in a tubular reactor is presented and compared to classical batch and continuous in vitro transcription in a stirred tank. The three models are validated based on a distinct and quantitative validation workflow. Statistically significant parameters are identified as part of the parameter determination concept. Monte Carlo simulations showed that the model is precise, with a deviation of less than 1%. The advantages of continuous production are pointed out compared to batchwise in vitro transcription by optimization of the space–time yield. Improvements of a factor of 56 (0.011 µM/min) in the case of the continuously stirred tank reactor (CSTR) and 68 (0.013 µM/min) in the case of the plug flow reactor (PFR) were found.

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Digital Twins in Biomanufacturing

Cited by 34 publications

References 115 publications

Towards Autonomous Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing Using a Dynamic Metabolic Model

Towards Autonomous Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing Using a Dynamic Metabolic Model

A methodology for co-simulation-based optimization of biofabrication protocols

Digital Twins for Continuous mRNA Production

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