Model-based integrated optimization and evaluation of a multi-step ion exchange chromatography

Huuk, Thiemo; Hahn, Tobias; Osberghaus, Anna; Hubbuch, Jürgen

doi:10.1016/j.seppur.2014.09.012

Cited by 58 publications

(33 citation statements)

References 28 publications

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“…Examples of such process development efforts to achieve sufficient clearance from antibody aggregates are reported in the literature; examples of ternary separations involving aggregates as well as other product‐related impurities are less frequent . Other examples of ternary mixtures comprising non‐mAb proteins, namely BSA, ovalbumin, cytochrome C, and ribonuclease A, are commonly employed to study model‐based optimization problems …”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

Fischer

Kucia-Tran

Lewis

et al. 2019

Biotechnology Progress

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In the purification of monoclonal antibodies, ion‐exchange chromatography is typically used among the polishing steps to reduce the amount of product‐related impurities such as aggregates and fragments, whilst simultaneously reducing HCP, residual Protein A and potential toxins and viruses. When the product‐related impurities are difficult to separate from the products, the optimization of these chromatographic steps can be complex and laborious. In this paper, we optimize the polishing chromatography of a monoclonal antibody from a challenging ternary feed mixture by introducing a hybrid approach of the simplex method and a form of local optimization. To maximize the productivity of this preparative bind‐and‐elute cation‐exchange chromatography, wide ranges of the three critical operational parameters—column loading, the initial salt concentration, and gradient slope—had to be considered. The hybrid optimization approach is shown to be extremely effective in dealing with this complex separation that was subject to multiple constraints based on yield, purity, and product breakthrough. Furthermore, it enabled the generation of a large knowledge space that was subsequently used to study the sensitivity of the objective function. Increased design space understanding was gained through the application of Monte Carlo simulations. Hence, this work proposes a powerful hybrid optimization method, applied to an industrially relevant process development challenge. The properties of this approach and the results and insights gained, make it perfectly suited for the rapid development of biotechnological unit operations during early‐stage bioprocess development.

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Section: Introductionmentioning

confidence: 99%

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

Fischer

Kucia-Tran

Lewis

et al. 2019

Biotechnology Progress

View full text Add to dashboard Cite

show abstract

“…Nfor et al optimized the sequence of chromatographic unit operations sequentially rather than simultaneously, which reduces the complexity of the combinatorial problem as possible unit operation sequences increase drastically with an increasing number of unit operations (Nfor et al, ). Nevertheless, sequential optimization may lead to a suboptimal overall process . Moreover, several process options were excluded which seemed to be not competitive based on prior knowledge about the process .…”

Section: Introductionmentioning

confidence: 99%

“…However, such knowledge might not always be available or be false in a specific case. Additionally, this approach has been applied to the simultaneous optimization of a short process sequence consisting of only two purification units . One of the key issues why this approach has not yet been applied to more complex cases is the computational expense to find the optimal process.…”

Section: Introductionmentioning

confidence: 99%

“…1 These approaches are either mostly computational, 2-7 mostly experimental, 8 or a true combination of computational and experimental techniques, also called the hybrid approach. 9,10 As mechanistic models (MM) built on first principles need many process parameters that usually require experimental determination, computational approaches are often based on non-MMs such as simple economic models or the approximation of chromatograms as triangles. However, these simple models do not help in gaining additional process understanding, an increasingly important concept as put forward by the Quality by Design framework.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, sequential optimization may lead to a suboptimal overall process. 9,13,14 Moreover, several process options were excluded which seemed to be not competitive based on prior knowledge about the process. 15 However, such knowledge might not always be available or be false in a specific case.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks

Pirrung

Wielen

Beckhoven

et al. 2017

Biotechnology Progress

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Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:696-707, 2017.

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High‐Throughput Process Development

Pirrung

Ottens

2017

Preparative Chromatography for Separation of Proteins

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Model-based integrated optimization and evaluation of a multi-step ion exchange chromatography

Cited by 58 publications

References 28 publications

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks

High‐Throughput Process Development

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