Prediction uncertainty assessment of chromatography models using Bayesian inference

Briskot, Till; Stückler, Ferdinand; Wittkopp, Felix; Williams, Christopher K. I.; Yang, Jessica; Konrad, Susanne; Doninger, Katharina; Griesbach, Jan; Bennecke, Moritz; Hepbildikler, Stefan; Hubbuch, Jürgen

doi:10.1016/j.chroma.2018.11.076

Cited by 47 publications

(29 citation statements)

References 42 publications

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“…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. The large number of modelling approaches may be related to the importance of correlated molecular mechanisms at distinct length scales as well as the broad variability of biological parameters among living organisms.…”

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

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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.

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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

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“…Previous work has demonstrated the predictive power of SMA models, even when extrapolating beyond the experimental conditions applied for model calibration (Briskot et al, 2019). Despite the proven predictive power and the mechanistic nature of the SMA model, it is not clear which structural characteristics of mAbs influence adsorption model parameters.…”

Section: Introductionmentioning

confidence: 99%

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography

Saleh

Hess

Ahlers-Hesse

et al. 2021

Biotech & Bioengineering

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A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate.While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementaritydetermining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody.Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

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“…For mechanistic modeling of mAbs and other proteins in ion exchange chromatography, the SMA adsorption isotherm is frequently used in academic and industrial case studies. 16,[28][29][30][31][32][33] The SMA isotherm describes the multipoint binding of proteins to the resin under consideration of a protein's characteristic charge, the thermodynamic equilibrium of the adsorption process, and steric shielding effects. Multiple studies have demonstrated successful application of mechanistic models for the scale-up of chromatography processes.…”

Section: Introductionmentioning

confidence: 99%

“…They consist of partial differential equations, describing macroscopic transport through the column, mass transport within the stationary phase, and adsorption of protein to the resin. For mechanistic modeling of mAbs and other proteins in ion exchange chromatography, the SMA adsorption isotherm is frequently used in academic and industrial case studies 16,28‐33 . The SMA isotherm describes the multipoint binding of proteins to the resin under consideration of a protein's characteristic charge, the thermodynamic equilibrium of the adsorption process, and steric shielding effects.…”

Section: Introductionmentioning

confidence: 99%

Cross‐scale quality assessment of a mechanistic cation exchange chromatography model

Saleh

Wang

Mueller

et al. 2020

Biotechnology Progress

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Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory-to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

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Prediction uncertainty assessment of chromatography models using Bayesian inference

Cited by 47 publications

References 42 publications

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

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

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography

Cross‐scale quality assessment of a mechanistic cation exchange chromatography model

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