Digital Twins for Continuous mRNA Production

Helgers, Heribert; Hengelbrock, Alina; Schmidt, A.; Strube, Jochen

doi:10.3390/pr9111967

Cited by 30 publications

(34 citation statements)

References 69 publications

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“…If predictive, 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]. Part of this procedure is the collection and quantitative evaluation of risk severity and risk probability, which together result in a risk rank that forms the decision-making basis for the design of multiand univariate investigations [36]. [37].…”

Section: Qbd-based Process Developmentmentioning

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: Qbd-based Process Developmentmentioning

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 steps and actions that must be taken for the transition of a steady-state mathematical model to a fully-fledged digital twin. 42,43 Figure 3. Simulation results of viable cell and product concentration in perfusion.…”

Section: Precipitationmentioning

confidence: 99%

“…Figure2. The steps and actions that must be taken for the transition of a steady-state mathematical model to a fully-fledged digital twin 42,43. …”

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confidence: 99%

Process analytical technology as key‐enabler for digital twins in continuous biomanufacturing

Schmidt

Helgers

Lohmann

et al. 2022

J of Chemical Tech & Biotech

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Over the last few years rapid progress has been made in adopting well‐known process modeling techniques from chemicals to biologics manufacturing. The main challenge has been analytical methods as engineers need quantitative data for their workflow. Industrialization 4.0, Internet of Things, artificial intelligence and machine learning activities up to big data analysis have taken their share in solving fundamental problems like component‐ or at least group‐specific evaluation of spectroscopic data. Besides, concerning inline analytics methods included in process analytical technology concepts the key technology has been the generation of decisive validated digital twins based on process models. This review aims to summarize the methodology to achieve a holistic understanding of process models, control and optimization by means of digital twins using the example of recent work published in this field. © 2021 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

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“…Given the vast applications of DT ranging from smart manufacturing to mRNA (Qi and Tao, 2018; Helgers et al, 2021), it is necessary to establish a targeted aspect of DTs. As mentioned in the introduction, this work deals explicitly with the metallic components resulting from a given set of manufacturing processes.…”

Section: Materials Within a Dtmentioning

confidence: 99%

Guidance for Materials 4.0 to interact with a digital twin

Cogswell

Paramatmuni

Scotti

et al. 2022

DCE

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The rapid development of new infrastructure programmes requires an accelerated deployment of new materials in new environments. Materials 4.0 is crucial to achieve these goals. The application of digital to the field of materials has been at the forefront of research for many years, but there does not exist a unified means to describe a framework for this area creating pockets of development. This is confounded by the broader expectations of a digital twin (DT) as the possible answer to all these problems. The issue being that there is no accepted definition of a component DT, and what information it should contain and how it can be implemented across the product lifecycle exist. Within this position paper, a clear distinction is made between the “manufacturing DT” and the “component DT”; the former being the starting boundary conditions of the latter. In order to achieve this, we also discuss the introduction of a digital thread as a key concept in passing data through manufacturing and into service. The stages of how to define a framework around the development of DTs from a materials perspective is given, which acknowledges the difference between creating new understanding within academia and the application of this knowledge on a per-component basis in industry. A number of challenges are identified to the broad application of a component DT; all lead to uncertainty in properties and locations, resolving these requires judgments to be made in the provision of safety-dependent materials property data.

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Digital Twins for Continuous mRNA Production

Cited by 30 publications

References 69 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

Process analytical technology as key‐enabler for digital twins in continuous biomanufacturing

Guidance for Materials 4.0 to interact with a digital twin

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