3D‐liquid chromatography as a complex mixture characterization tool for knowledge‐based downstream process development

Hanke, Alexander T.; Tsintavi, Eleni; Vazquez, Maria del Pilar Ramirez; Wielen, Luuk A.M. van der; Verhaert, Peter; Eppink, M.H.M.; Sandt, Emile J.A.X. van de; Ottens, Marcel

doi:10.1002/btpr.2320

Cited by 12 publications

(9 citation statements)

References 37 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The column dead volume is very important in miniature columns such as the RoboColumns, since the ratio of column volume (

V_{normalc normalo normall}

) to column dead volume is smaller. In previous studies, it was found to be 30 μL . The bed porosity

ɛ_{normalb}

generally lies in between 0.3 and 0.4 for packed chromatography columns.…”

Section: Methodsmentioning

confidence: 86%

“…Each fraction of interest was analyzed with isocratic experiments at different salt concentrations on RoboColumns containing the respective resin (1, 2 and 3 on POROS 50HS; 4 and 5 on Capto MMC). Fractions collected here were further analyzed with size exclusion measurements, to increase resolution and sensitivity, and UV measurements, to determine the well volume. Typical results of these experiments are shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

“…As a first separation dimension, the samples were fractionated by linear pH‐gradient chromatography on a Mono Q 4.6/100 strong anion exchange column (GE Healthcare, Sweden) or a Mono S 4.6/100 strong cation exchange column (GE Healthcare, Sweden). The prefractionation was performed as described by Hanke et al…”

Section: Methodsmentioning

confidence: 99%

“…All protein concentrations were determined in a UHPLC + (Thermo Fisher Scientific, MA, USA) system as described by Hanke et al…”

Section: Methodsmentioning

confidence: 99%

“…Previously, such approaches involving column experiments have even been applied to complex feedstocks by performing multiple fractionation steps . Subsequently, in efforts to save precious sample, Hanke et al developed a 3D liquid chromatography approach that consists of: a pH gradient prefractionation as a first dimension to reduce sample complexity; a second dimension with gradient experiments to obtain isotherm parameters on RoboColumns, which have a bed volume of only 200 μL, and a final dimension of size exclusion chromatography to increase the resolution …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Chromatographic parameter determination for complex biological feedstocks

Pirrung

Cruz

Hanke

et al. 2018

Biotechnology Progress

Self Cite

View full text Add to dashboard Cite

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018

show abstract

“…The column dead volume is very important in miniature columns such as the RoboColumns, since the ratio of column volume (

V_{normalc normalo normall}

) to column dead volume is smaller. In previous studies, it was found to be 30 μL . The bed porosity

ɛ_{normalb}

generally lies in between 0.3 and 0.4 for packed chromatography columns.…”

Section: Methodsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…All protein concentrations were determined in a UHPLC + (Thermo Fisher Scientific, MA, USA) system as described by Hanke et al…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Chromatographic parameter determination for complex biological feedstocks

Pirrung

Cruz

Hanke

et al. 2018

Biotechnology Progress

Self Cite

View full text Add to dashboard Cite

show abstract

Characterisation of the E. coli HMS174 and BLR host cell proteome to guide purification process development

Disela,

Bussy,

Geldhof

et al. 2023

Biotechnology Journal

Self Cite

View full text Add to dashboard Cite

Mass‐spectrometry‐based proteomics is increasingly employed to monitor purification processes or to detect critical host cell proteins in the final drug substance. This approach is inherently unbiased and can be used to identify individual host cell proteins without prior knowledge. In process development for the purification of new biopharmaceuticals, such as protein subunit vaccines, a broader knowledge of the host cell proteome could promote a more rational process design. Proteomics can establish qualitative and quantitative information on the complete host cell proteome before purification (i.e., protein abundances and physicochemical properties). Such information allows for a more rational design of the purification strategy and accelerates purification process development. In this study, we present an extensive proteomic characterisation of two E. coli host cell strains widely employed in academia and industry to produce therapeutic proteins, BLR and HMS174. The established database contains the observed abundance of each identified protein, information relating to their hydrophobicity, the isoelectric point, molecular weight, and toxicity. These physicochemical properties were plotted on proteome property maps to showcase the selection of suitable purification strategies. Furthermore, sequence alignment allowed integration of subunit information and occurrences of post‐translational modifications from the well‐studied E. coli K12 strain.

show abstract

A combined screening and in silico strategy for the rapid design of integrated downstream processes for process and product‐related impurity removal

Vecchiarello

Timmick

Goodwine

et al. 2019

Biotech & Bioengineering

View full text Add to dashboard Cite

Integrated designs of chromatographic processes for purification of biopharmaceuticals provides potential gains in operational efficiency and reductions of costs and material requirements. We describe a combined method using screening and in silico algorithms for ranking chromatographic steps to rapidly design orthogonally selective integrated processes for purifying protein therapeutics from both process-and product-related impurities. IFN-α2b produced in Pichia pastoris containing a significant product variant challenge was used as a case study. The product and product-related variants were screened on a set of 14 multimodal, ion exchange, and hydrophobic charge induction chromatography resins under various pH and salt linear gradient conditions. Data generated from reversed-phase chromatography of the fractions collected were used to generate a retention database for IFN-α2b and its variants. These data, in combination with a previously constructed process-related impurity database for P. pastoris, were input into an in silico process development tool that generated and ranked all possible integrated chromatographic sequences for their ability to remove both process and product-related impurities. Top-ranking outputs guided the experimental refinement of two successful three step purification processes, one comprising all bind-elute steps and the other having two bind-elute steps and a flowthrough operation. This approach suggests a new platform-like approach for rapidly designing purification processes for a range of proteins where separations of both process-and product-related impurities are needed. K E Y W O R D Shigh throughput process development, integrated downstream processing, mixed mode chromatography, process development tool, protein chromatography, straight-through processing

show abstract

3D‐liquid chromatography as a complex mixture characterization tool for knowledge‐based downstream process development

Cited by 12 publications

References 37 publications

Chromatographic parameter determination for complex biological feedstocks

Chromatographic parameter determination for complex biological feedstocks

Characterisation of the E. coli HMS174 and BLR host cell proteome to guide purification process development

A combined screening and in silico strategy for the rapid design of integrated downstream processes for process and product‐related impurity removal

Contact Info

Product

Resources

About