Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data

Altern, Scott Howard; Welsh, John P.; Lyall, Jessica Y.; Kocot, Andrew J.; Burgess, Sean; Kumar, Vijesh; Williams, Chris; Lenhoff, Abraham M.; Koley, Sushmita

doi:10.1016/j.chroma.2023.463878

Cited by 11 publications

(2 citation statements)

References 77 publications

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“…This is explained by the assumption shown in Equation (1) that protein binding to the functional groups for IEC and HIC occurs simultaneously, which is legitimate given the spatially close position on the ligand. However, due to the individual saturation capacities in the pure IEC and HIC modes, different relative ligand occupancies arise in the transition area between IEC and HIC, which contradicts the fundamental assumption of simultaneous binding and may be the reason for the difficulties observed in Reference 20 to describe the linear and non‐linear isotherm equally well.…”

Section: Introductionmentioning

confidence: 94%

“…Salt‐dependent pore diffusion was employed for high‐salt binding 17 and changes in water structure 18 have been taken into account, similar to the usage in pure HIC models 19 . A recent study performing model discrimination 20 has shown that a high level of model complexity is not necessary in every case and that, for example, the water structure change can be neglected. However, the same study also showed discrepancies of the model by Nfor et al in accurately describing the linear and non‐linear regions of adsorption isotherms simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Hahn¹,

Geng²,

Petrushevska-Seebach

et al. 2022

Biotechnology Progress

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Mixed‐mode chromatography combines features of ion‐exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow‐through operations on traditional unmixed resins or as a pH‐controlled bind‐and‐elute step, the use of both interaction modes promises a better removal of product‐specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer‐optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion‐exchange chromatography. In a case study, a model for a pH‐controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed‐mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Hahn¹,

Geng²,

Petrushevska-Seebach

et al. 2022

Biotechnology Progress

View full text Add to dashboard Cite

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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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High‐throughput in silico workflow for optimization and characterization of multimodal chromatographic processes

Altern,

Lyall,

Welsh

et al. 2024

Biotechnology Progress

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While high‐throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high‐throughput in silico (HT‐IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High‐throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab‐scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT‐IS workflow for rapid process development and characterization.

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Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data

Cited by 11 publications

References 77 publications

Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

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

High‐throughput in silico workflow for optimization and characterization of multimodal chromatographic processes

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