Fourier transform assisted deconvolution of skewed peaks in complex multi-dimensional chromatograms

Hanke, Alexander T.; Verhaert, Peter; Wielen, Luuk A.M. van der; Eppink, M.H.M.; Sandt, Emile J.A.X. van de; Ottens, Marcel

doi:10.1016/j.chroma.2015.03.017

Cited by 8 publications

(7 citation statements)

References 31 publications

(32 reference statements)

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“…While mass‐transfer limitations apply, a longer gradient is generally expected to lead to a higher resolution. A higher resolution allows the peak fitting algorithm to determine peak properties with a higher certainty . This is clearly seen for the standard errors of the calculated peak volumes, that drop from an average error of 81% for the 12 CV gradient, to 40% for the 24 CV gradient, and down to 26% for the 36 CV gradient.…”

Section: Resultsmentioning

confidence: 99%

“…Composite multidimensional chromatograms were constructed for the two‐dimensional AEX‐gCF‐SEC reference data, as well as for each HIC gradient for each fraction with a significant UV‐detectable protein content. The peaks in each complex composite chromatogram were detected, deconvoluted, and fitted with a Fourier‐transform assisted technique reported elsewhere . To increase the methods sensitivity the absorption signal at 230 nm was used instead of 280 nm.…”

Section: Methodsmentioning

confidence: 99%

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

Hanke

Tsintavi

Vazquez

et al. 2016

Biotechnology Progress

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Knowledge-based development of chromatographic separation processes requires efficient techniques to determine the physicochemical properties of the product and the impurities to be removed. These characterization techniques are usually divided into approaches that determine molecular properties, such as charge, hydrophobicity and size, or molecular interactions with auxiliary materials, commonly in the form of adsorption isotherms. In this study we demonstrate the application of a three-dimensional liquid chromatography approach to a clarified cell homogenate containing a therapeutic enzyme. Each separation dimension determines a molecular property relevant to the chromatographic behavior of each component. Matching of the peaks across the different separation dimensions and against a high-resolution reference chromatogram allows to assign the determined parameters to pseudo-components, allowing to determine the most promising technique for the removal of each impurity. More detailed process design using mechanistic models requires isotherm parameters. For this purpose, the second dimension consists of multiple linear gradient separations on columns in a high-throughput screening compatible format, that allow regression of isotherm parameters with an average standard error of 8%. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1283-1291, 2016.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Hanke

Tsintavi

Vazquez

et al. 2016

Biotechnology Progress

Self Cite

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“…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%

“…The function chosen for fitting was based on a one‐dimensional adaption of the model for multiple superimposed exponentially modified Gaussian peaks described in Ref. . Instead of minimizing the squares between the measured data point and the curve described by the peak model, the average of the model curve was calculated over each fraction interval, and the squares between this value and the measurement were minimized.…”

Section: Methodsmentioning

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 9,21 High-throughput isocratic chromatography Column Characterization. The columns used were 200 mL RoboColumns (Repligen, Germany), packed with two different resins as described in Table 1.…”

Section: Gradient Chromatofocussing Prefractionationmentioning

confidence: 99%

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Chromatographic parameter determination for complex biological feedstocks

Pirrung

Cruz

Hanke

et al. 2018

Biotechnology Progress

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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

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Translation Modification Iteration for Resolution and Quantification of Overlapping Chromatographic Peaks

2016

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Fourier transform assisted deconvolution of skewed peaks in complex multi-dimensional chromatograms

Cited by 8 publications

References 31 publications

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

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

Chromatographic parameter determination for complex biological feedstocks

Translation Modification Iteration for Resolution and Quantification of Overlapping Chromatographic Peaks

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