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Burdick, Richard K.; LeBlond, David; Pfahler, Lori B.; Quiróz, Jorge A.; Sidor, Leslie; Vukovinsky, Kimberly; Zhang, Lanju

doi:10.1007/978-3-319-50186-4_7

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…This permits modeling flexibility, where each response is not necessarily assayed across all UOs. In bioprocess applications, it is of particular importance to consider a mechanism for estimating parameter and model uncertainty in the prediction, as heightened variation is inherent to the biosystem and significantly impacts the precision of the predictions [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications

Taylor

Pretzner

Zahel³

et al. 2022

Bioengineering

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Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output–input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle.

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Section: Introductionmentioning

confidence: 99%

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications

Taylor

Pretzner

Zahel³

et al. 2022

Bioengineering

View full text Add to dashboard Cite

show abstract

“…The goal of process characterization in biopharmaceutical development is to establish scientific evidence that a process is able to consistently deliver quality products. An important part of this procedure is to determine the effect of process parameters (PP) on critical quality attributes (CQA [ 1 , 2 ]). Design of experiments (DoE) is a well-established tool to design experimental runs that yield such information and is oftentimes followed by data analysis and inference based on regression models [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Holistic Design of Experiments Using an Integrated Process Model

Oberleitner¹,

Zahel²,

Pretzner³

et al. 2022

Bioengineering

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Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality.

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Specifications

Cited by 2 publications

References 12 publications

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications

Holistic Design of Experiments Using an Integrated Process Model

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