Opportunities and challenges for model utilization in the biopharmaceutical industry: current versus future state

Babi, Deenesh Kavi; Griesbach, Jan; Hunt, S.; Insaidoo, Francis K.; Roush, David J.; Todd, R.; Staby, Arne; Welsh, John P.; Wittkopp, Felix

doi:10.1016/j.coche.2022.100813

Cited by 22 publications

(7 citation statements)

References 53 publications

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“…Since the implementation of Quality by Design, there has been increased emphasis on process understanding through model-based approaches such as mechanistic modeling [27]. Therefore, an increasing number of publications can be found where mechanistic modeling was applied to predict chromatographic scale-up experiments [2,[8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Koch

Scheps

Gunne

et al. 2023

J of Separation Science

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In process development and characterization, the scale-up of chromatographic steps is a crucial part and brings a number of challenges. Usually, scale-down models are used to represent the process step, and constant column properties are assumed. The scaling is then typically based on the concept of linear scale-up. In this work, a mechanistic model describing an anti-Langmuirian to Langmuirian elution behavior of a polypeptide, calibrated with a pre-packed 1 ml column, is applied to demonstrate the scalability to larger column volumes up to 28.2 ml. Using individual column parameters for each column size, scaling to similar eluting salt concentrations, peak heights, and shapes is experimentally demonstrated by considering the model's relationship between the normalized gradient slope and the eluting salt concentration. Further scale-up simulations show improved model predictions when radial inhomogeneities in packing quality are considered.

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Section: Introductionmentioning

confidence: 99%

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Koch

Scheps

Gunne

et al. 2023

J of Separation Science

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“…There are two main approaches to downstream process modelling, the use of statistical methods, including Design of Experiments (DoE; [ 41 ]) and first-principles mechanistic modelling (reviewed in [ 42 , 43 , 44 ]). Mechanistic models incorporate both mass transport properties within the column as well as protein-resin (adsorption/desorption/diffusion) characteristics, captured as a series of differential equations that quantitatively define time-dependent changes in solute concentration.…”

Section: Mechanistic and Statistical Process Modellingmentioning

confidence: 99%

Recent Advances and Future Directions in Downstream Processing of Therapeutic Antibodies

Matte¹

2022

IJMS

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Despite the advent of many new therapies, therapeutic monoclonal antibodies remain a prominent biologics product, with a market value of billions of dollars annually. A variety of downstream processing technological advances have led to a paradigm shift in how therapeutic antibodies are developed and manufactured. A key driver of change has been the increased adoption of single-use technologies for process development and manufacturing. An early-stage developability assessment of potential lead antibodies, using both in silico and high-throughput experimental approaches, is critical to de-risk development and identify molecules amenable to manufacturing. Both statistical and mechanistic modelling approaches are being increasingly applied to downstream process development, allowing for deeper process understanding of chromatographic unit operations. Given the greater adoption of perfusion processes for antibody production, continuous and semi-continuous downstream processes are being increasingly explored as alternatives to batch processes. As part of the Quality by Design (QbD) paradigm, ever more sophisticated process analytical technologies play a key role in understanding antibody product quality in real-time. We should expect that computational prediction and modelling approaches will continue to be advanced and exploited, given the increasing sophistication and robustness of predictive methods compared to the costs, time, and resources required for experimental studies.

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“…While this burden for digital transition and technology transfer has been observed in the past (and successfully overcome) in other industry sector (e.g., petrochemical companies, aeronautics), biopharma faces the additional challenge of its operation relying on complex biological systems that cannot be easily described with known first principles rules ( Strube et al, 2018 ). Numerous modelling studies successfully characterizing the influence of measurable process conditions on culture dynamic exist in literature ( Smiatek et al, 2020 ; Wang et al, 2020 ; Tsopanoglou and Jiménez del Val, 2021 ; Babi et al, 2022 ; Schwarz et al, 2022 ). Unfortunately, they often depend on numerous measurements not often available at manufacturing scale and/or complex modeling and optimization procedures requiring important computational expertise.…”

Section: Introductionmentioning

confidence: 99%

Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion

Richelle¹,

Corbett²,

Agarwal³

et al. 2022

Front. Bioeng. Biotechnol.

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There is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.

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Opportunities and challenges for model utilization in the biopharmaceutical industry: current versus future state

Cited by 22 publications

References 53 publications

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Recent Advances and Future Directions in Downstream Processing of Therapeutic Antibodies

Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion

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