Toward in silico CMC: An industrial collaborative approach to model‐based process development

Roush, David J.; Asthagiri, D.; Babi, Deenesh Kavi; Benner, Steve M.; Bilodeau, Camille L.; Carta, Giorgio; Ernst, Philipp; Fedesco, Mark; Fitzgibbon, Sean P.; Flamm, Matthew H.; Griesbach, Jan; Großkopf, Tobias; Hansen, Ernst Broberg; Hahn, Tobias; Hunt, S.; Insaidoo, Francis K.; Lenhoff, Abraham M.; Lin, Jasper; Marke, Henrik S.; Marques, Bruno; Papadakis, Emmanouil; Schlegel, Fabrice; Staby, Arne; Stenvang, Marcel; Sun, Larry; Tessier, Peter M.; Todd, R.; Lieres, Eric von; Welsh, John P.; Willson, Richard C.; Wang, Gang; Wucherpfennig, Thomas; Zavalov, Oleksandr

doi:10.1002/bit.27520

Cited by 31 publications

(27 citation statements)

References 22 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to DoE, finite element analysis (FEA) and computational fluid dynamics (CFD) are another standard optimisation techniques used in both academia and industry, applied in fields such as aerospace, electronics and biomechanics. In fact, the FDA is actively investing in CFD for medical devices and biological fluids [132]. The wide adoption of both techniques can be attributed to the high degree of accuracy that can be achieved, which in some instances has been found to be more accurate than results obtained from experimental measurements [133,134].…”

Section: Finite Element Analysis and Computational Fluid Dynamicsmentioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

102

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Finite Element Analysis and Computational Fluid Dynamicsmentioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

102

View full text Add to dashboard Cite

show abstract

“…In the pharmaceutical industry, CFD is no longer only an innovative tool that has to prove its value but has evolved to be part of the day-to-day business. In fact, due to increasing computational power and more sophisticated simulation software, CFD is typically integrated in scale-up models, process design, optimization and the development of scale-down models [1][2][3][4]. Thereby, its applicability is shown to address issues in up-and downstream development as well as for the production of biopharmaceuticals.…”

Section: Introductionmentioning

confidence: 99%

“…Typical topics are risk mitigation during transfer, troubleshooting and reduction in lab experiments [5,6]. In line with the digitalization trend in the biopharmaceutical industry, other modeling approaches for upstream and downstream processing have also recently gained recognition [4,[7][8][9]. The benefits are reduction in costs and improved equipment and process understanding, which can ultimately lead to a faster time to market.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of Novel Lattice Boltzmann Large Eddy Simulations (LB LES) for Equipment Characterization in Biopharma

et al. 2021

Self Cite

View full text Add to dashboard Cite

Detailed process and equipment knowledge is crucial for the successful production of biopharmaceuticals. An essential part is the characterization of equipment for which Computational Fluid Dynamics (CFD) is an important tool. While the steady, Reynolds-averaged Navier–Stokes (RANS) k − ε approach has been extensively reviewed in the literature and may be used for fast equipment characterization in terms of power number determination, transient schemes have to be further investigated and validated to gain more detailed insights into flow patterns because they are the method of choice for mixing time simulations. Due to the availability of commercial solvers, such as M-Star CFD, Lattice Boltzmann simulations have recently become popular in the industry, as they are easy to set up and require relatively low computing power. However, extensive validation studies for transient Lattice Boltzmann Large Eddy Simulations (LB LES) are still missing. In this study, transient LB LES were applied to simulate a 3 L bioreactor system. The results were compared to novel 4D particle tracking (4D PTV) experiments, which resolve the motion of thousands of passive tracer particles on their journey through the bioreactor. Steady simulations for the determination of the power number followed a structured workflow, including grid studies and rotating reference frame volume studies, resulting in high prediction accuracy with less than 11% deviation, compared to experimental data. Likewise, deviations for the transient simulations were less than 10% after computational demand was reduced as a result of prior grid studies. The time averaged flow fields from LB LES were in good accordance with the novel 4D PTV data. Moreover, 4D PTV data enabled the validation of transient flow structures by analyzing Lagrangian particle trajectories. This enables a more detailed determination of mixing times and mass transfer as well as local exposure times of local velocity and shear stress peaks. For the purpose of standardization of common industry CFD models, steady RANS simulations for the 3 L vessel were included in this study as well.

show abstract

“…Over the last years, modelling and simulation has become an important field of research for biotherapeutical manufacturing and process development. Due to increasing computational power as well as the improved use of process analytical technologies, novel computational approaches for complex upstream and downstream processes are in the focus of recent interest [1] , [2] , [3] . While mechanistic kinetic-dispersive models are nowadays considered as standard methods for the study of capturing and polishing steps in downstream operations [4] , [5] , [6] , [7] , [8] , [9] , [10] , there exist a plethora of distinct models for upstream processes with certain advantages and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

Smiatek

Clemens

Montano-Herrera

et al. 2021

Biotechnology Reports

Self Cite

View full text Add to dashboard Cite

Highlights Novel machine learning method based on recurrent neural networks for the accurate prediction of upstream cultivation processes. Insights into the temporal evolution of main upstream parameters and outcomes. High predictive capability, straightforward implementation and broad transferability of the proposed approach. New insights into the influence of parameter changes for process outcomes. Introduction of new analysis methods like autocorrelation functions for the detailed study of experimental and computational data.

show abstract

Toward in silico CMC: An industrial collaborative approach to model‐based process development

Cited by 31 publications

References 22 publications

Harnessing artificial intelligence for the next generation of 3D printed medicines

Harnessing artificial intelligence for the next generation of 3D printed medicines

Validation of Novel Lattice Boltzmann Large Eddy Simulations (LB LES) for Equipment Characterization in Biopharma

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

Contact Info

Product

Resources

About