Model-based optimization of integrated purification sequences for biopharmaceuticals

Pirrung, Silvia M.; Berends, Carmen; Beckhoven, Ruud F. W. C. van; Eppink, M.H.M.; Ottens, Marcel

doi:10.1016/j.cesx.2019.100025

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…As sequential optimization can lead to a suboptimal process, Huuk et al (2014) simultaneously optimized a two‐step ion‐exchange chromatography process. A similar approach was applied by Pirrung et al (2019) simultaneously optimizing an integrated process of three chromatographic steps (e.g., cation exchange, hydrophobic interaction, and mixed‐mode) including buffer exchange steps if needed (e.g., ultra‐ and diafiltration).…”

Section: Introductionmentioning

confidence: 99%

“…In the early 2000s, Nagrath et al (2004) already established a hybrid model optimization framework for preparative chromatography, using ANNs for speed improvement. In the work of Pirrung et al (2017, 2019), all flowsheets of a superstructure were evaluated by a global and local optimizer; the outcomes of the global optimizer was used as starting conditions for the local optimizer. In this case, ANNs replaced the mechanistic model during global optimization; however, these ANNs were less precise and therefore unable to always find realistic results.…”

Section: Introductionmentioning

confidence: 99%

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Using artificial neural networks to accelerate flowsheet optimization for downstream process development

Keulen,

Hagen,

Geldhof

et al. 2023

Biotech & Bioengineering

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An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model‐based approaches enable to screen a broad range of the design‐space, in contrast to traditional statistical or heuristic‐based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model‐based approaches, can be speed‐limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks to accelerate flowsheet optimization for downstream process development

Keulen,

Hagen,

Geldhof

et al. 2023

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other relevant numerical challenges arise for e.g. Bayesian inference applications (Briskot et al, 2019), process synthesis of detailed flow-sheets (Pirrung et al, 2017(Pirrung et al, , 2019, simulation of 2D general rate models (Qamar et al, 2017), and for simulation of chromatography models with inhomogeneous resin beads (Gerontas et al, 2013;Püttmann et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography

Meyer

Leweke

Lieres

et al. 2020

Computers & Chemical Engineering

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Current state of implementation of in silico tools in the biopharmaceutical industry—Proceedings of the 5th modeling workshop

Wittkopp,

Welsh,

Todd

et al. 2024

Biotech & Bioengineering

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The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high‐level guidance in ICH Q8–Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for “mechanistic modeling.” Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter‐disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

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Model-based optimization of integrated purification sequences for biopharmaceuticals

Cited by 5 publications

References 30 publications

Using artificial neural networks to accelerate flowsheet optimization for downstream process development

Using artificial neural networks to accelerate flowsheet optimization for downstream process development

ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography

Current state of implementation of in silico tools in the biopharmaceutical industry—Proceedings of the 5th modeling workshop

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