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Molenaar SRA, Bos TS, Boelrijk J, Dahlseid TA, Stoll DR, Pirok BWJ. Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography. J Chromatogr A 2023; 1707:464306. [PMID: 37639847 DOI: 10.1016/j.chroma.2023.464306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023]
Abstract
Method development in comprehensive two-dimensional liquid chromatography (LC × LC) is a complicated endeavor. The dependency between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, renders method development by "trial-and-error" time-consuming and highly dependent on user experience. In this work, an open-source algorithm for the automated and interpretive method development of complex gradients in LC × LC-mass spectrometry (MS) was developed. A workflow was designed to operate within a closed-loop that allowed direct interaction between the LC × LC-MS system and a data-processing computer which ran in an unsupervised and automated fashion. Obtaining accurate retention models in LC × LC is difficult due to the challenges associated with the exact determination of retention times, curve fitting because of the use of gradient elution, and gradient deformation. Thus, retention models were compared in terms of repeatability of determination. Additionally, the design of shifting gradients in the second dimension and the prediction of peak widths were investigated. The algorithm was tested on separations of a tryptic digest of a monoclonal antibody using an objective function that included the sum of resolutions and analysis time as quality descriptors. The algorithm was able to improve the separation relative to a generic starting method using these complex gradient profiles after only four method-development iterations (i.e., sets of chromatographic conditions). Further iterations improved retention time and peak width predictions and thus the accuracy in the separations predicted by the algorithm.
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Affiliation(s)
- Stef R A Molenaar
- van 't Hoff Institute for Molecular Sciences, Analytical Chemistry Group, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, The Netherlands
| | - Tijmen S Bos
- Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, The Netherlands; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
| | - Jim Boelrijk
- Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, The Netherlands; AMLab, Informatics Institute, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; AI4Science Lab, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Tina A Dahlseid
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States
| | - Dwight R Stoll
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States
| | - Bob W J Pirok
- van 't Hoff Institute for Molecular Sciences, Analytical Chemistry Group, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, The Netherlands.
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Fast and Versatile Chromatography Process Design and Operation Optimization with the Aid of Artificial Intelligence. Processes (Basel) 2021. [DOI: 10.3390/pr9122121] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced.
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Bos TS, Knol WC, Molenaar SR, Niezen LE, Schoenmakers PJ, Somsen GW, Pirok BW. Recent applications of chemometrics in one- and two-dimensional chromatography. J Sep Sci 2020; 43:1678-1727. [PMID: 32096604 PMCID: PMC7317490 DOI: 10.1002/jssc.202000011] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/20/2020] [Accepted: 02/21/2020] [Indexed: 12/28/2022]
Abstract
The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.
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Affiliation(s)
- Tijmen S. Bos
- Division of Bioanalytical ChemistryAmsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Wouter C. Knol
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Stef R.A. Molenaar
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Leon E. Niezen
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Peter J. Schoenmakers
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Govert W. Somsen
- Division of Bioanalytical ChemistryAmsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Bob W.J. Pirok
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
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Park H, Lee JM, Kim JY, Hong J, Oh HB. Prediction of liquid chromatography retention times of erectile dysfunction drugs and analogues using chemometric approaches. J LIQ CHROMATOGR R T 2017. [DOI: 10.1080/10826076.2017.1364264] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hyekyung Park
- Department of Chemistry, Sogang University, Seoul, Korea
| | - Jung-Min Lee
- Department of Chemistry, Sogang University, Seoul, Korea
| | - Jin Young Kim
- Biomedical Omics Group, Korea Basic Science Institute, Ochang, Korea
| | - Jongki Hong
- College of Pharmacy, Kyung Hee University, Seoul, Korea
| | - Han Bin Oh
- Department of Chemistry, Sogang University, Seoul, Korea
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Wang G, Briskot T, Hahn T, Baumann P, Hubbuch J. Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. J Chromatogr A 2017; 1515:146-153. [DOI: 10.1016/j.chroma.2017.07.089] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 07/07/2017] [Accepted: 07/28/2017] [Indexed: 11/24/2022]
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Wang G, Briskot T, Hahn T, Baumann P, Hubbuch J. Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks. J Chromatogr A 2017; 1487:211-217. [DOI: 10.1016/j.chroma.2017.01.068] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 01/23/2017] [Accepted: 01/25/2017] [Indexed: 11/26/2022]
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Hadjmohammadi MR, Mousavi Kiasari Z, Nazari SSSJ. Separation of some phenolic acids in micellar liquid chromatography using design of experiment-response surface methodology. JOURNAL OF ANALYTICAL CHEMISTRY 2016. [DOI: 10.1134/s106193481606006x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Barron LP, McEneff GL. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods. Talanta 2016; 147:261-70. [DOI: 10.1016/j.talanta.2015.09.065] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 09/22/2015] [Accepted: 09/27/2015] [Indexed: 12/01/2022]
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Munro K, Miller TH, Martins CP, Edge AM, Cowan DA, Barron LP. Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data. J Chromatogr A 2015; 1396:34-44. [DOI: 10.1016/j.chroma.2015.03.063] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 02/27/2015] [Accepted: 03/23/2015] [Indexed: 02/07/2023]
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Miller TH, Musenga A, Cowan DA, Barron LP. Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks. Anal Chem 2013; 85:10330-7. [DOI: 10.1021/ac4024878] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Thomas H. Miller
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Alessandro Musenga
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - David A. Cowan
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Leon P. Barron
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
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Cela R, Ordoñez E, Quintana J, Rodil R. Chemometric-assisted method development in reversed-phase liquid chromatography. J Chromatogr A 2013; 1287:2-22. [DOI: 10.1016/j.chroma.2012.07.081] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2012] [Revised: 07/25/2012] [Accepted: 07/26/2012] [Indexed: 11/16/2022]
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Hadjmohammadi MR, Nazari SSSJ. OPTIMIZATION OF SEPARATION OF FLAVONOIDS IN MICELLAR LIQUID CHROMATOGRAPHY USING EXPERIMENTAL DESIGN AND DERRINGER'S DESIRABILITY FUNCTION. J LIQ CHROMATOGR R T 2013. [DOI: 10.1080/10826076.2012.678458] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Vemic A, Malenovic A, Rakic T, Kostic N, Jancic Stojanovic B. Chemometrical Tools in the Study of the Retention Behavior of Azole Antifungals. J Chromatogr Sci 2013; 52:95-102. [DOI: 10.1093/chromsci/bms211] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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