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Bosten E, Pardon M, Chen K, Koppen V, Van Herck G, Hellings M, Cabooter D. Assisted Active Learning for Model-Based Method Development in Liquid Chromatography. Anal Chem 2024; 96:13699-13709. [PMID: 38979746 DOI: 10.1021/acs.analchem.4c02700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In recent decades, there has been a growing interest in fully automated methods for tackling complex optimization problems across various fields. Active learning (AL) and its variant, assisted active learning (AAL), incorporating guidance or assistance from external sources into the learning process, play key roles in this automation by enabling the autonomous selection of optimal experimental conditions to efficiently explore the problem space. These approaches are particularly valuable in situations wherein experimentation is costly or time-consuming. This study explores the application of AAL in model-based method development (MD) for liquid chromatography (LC) by using Bayesian statistics to incorporate historical data and analyte information for the generation of initial retention models. The process involves updating the model parameters based on new experiments, coupled with an active data selection method to choose the most informative experiment to run in a subsequent step. This iterative process balances model exploitation and experimental exploration until a satisfactory separation is achieved. The effectiveness of this approach is demonstrated via two practical examples, resulting in optimized separations in a limited number of experiments by optimizing the gradient slope. It is shown that the ability of AAL to leverage past knowledge and compound information to improve accuracy and reduce experimental runs offers a flexible alternative approach to fixed design methods.
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Affiliation(s)
- Emery Bosten
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Marie Pardon
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium
| | - Kai Chen
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Valerie Koppen
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gerd Van Herck
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Mario Hellings
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Deirdre Cabooter
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium
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2
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Bosten E, Kensert A, Desmet G, Cabooter D. Automated method development in high-pressure liquid chromatography. J Chromatogr A 2024; 1714:464577. [PMID: 38104507 DOI: 10.1016/j.chroma.2023.464577] [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: 11/24/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Method development in liquid chromatography is a crucial step in the optimization of analytical separations for various applications. However, it is often a challenging endeavour due to its time-consuming, resource intensive and costly nature, which is further hampered by its complexity requiring highly skilled and experienced scientists. This review presents an examination of the methods that are required for a completely automated method development procedure in liquid chromatography, aimed at taking the human out of the decision loop. Some of the presented approaches have recently witnessed an important increase in interest as they offer the promise to facilitate, streamline and speed up the method development process. The review first discusses the mathematical description of the separation problem by means of multi-criteria optimization functions. Two different strategies to resolve this optimization are then presented; an experimental and a model-based approach. Additionally, methods for automated peak detection and peak tracking are reviewed, which, upon integration in an instrument, allow for a completely closed-loop method development process. For each of these approaches, various currently applied methods are presented, recent trends and approaches discussed, short-comings pointed out, and future prospects highlighted.
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Affiliation(s)
- Emery Bosten
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium; Department of Pharmaceutical Development and Manufacturing Sciences, Janssen Pharmaceutica, Turnhoutseweg 30, Beerse, Belgium
| | - Alexander Kensert
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium
| | - Gert Desmet
- Department of Chemical Engineering, Free University of Brussels (VUB), Pleinlaan 2, Brussels 1050, Belgium
| | - Deirdre Cabooter
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium.
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3
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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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4
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Molenaar SRA, Mommers JHM, Stoll DR, Ngxangxa S, de Villiers AJ, Schoenmakers PJ, Pirok BWJ. Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation. J Chromatogr A 2023; 1705:464223. [PMID: 37487299 DOI: 10.1016/j.chroma.2023.464223] [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: 03/14/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 07/26/2023]
Abstract
Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.
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Affiliation(s)
- Stef R A Molenaar
- Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands.
| | | | - Dwight R Stoll
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States
| | - Sithandile Ngxangxa
- Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
| | - André J de Villiers
- Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
| | - Peter J Schoenmakers
- Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands
| | - Bob W J Pirok
- Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands
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5
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Bos TS, Boelrijk J, Molenaar SRA, van ’t Veer B, Niezen LE, van Herwerden D, Samanipour S, Stoll DR, Forré P, Ensing B, Somsen GW, Pirok BWJ. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography. Anal Chem 2022; 94:16060-16068. [DOI: 10.1021/acs.analchem.2c03160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Tijmen S. Bos
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081HVAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Jim Boelrijk
- AMLab, Informatics Institute, University of Amsterdam, Science Park 904, 1098 XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
- AI4Science Lab, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Stef R. A. Molenaar
- Analytical Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Brian van ’t Veer
- Analytical Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Leon E. Niezen
- Analytical Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Denice van Herwerden
- Analytical Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Saer Samanipour
- Analytical Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Dwight R. Stoll
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, 56082Minnesota, United States
| | - Patrick Forré
- AMLab, Informatics Institute, University of Amsterdam, Science Park 904, 1098 XHAmsterdam, The Netherlands
- AI4Science Lab, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Bernd Ensing
- AI4Science Lab, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Computational Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Govert W. Somsen
- Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081HVAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
| | - Bob W. J. Pirok
- Analytical Chemistry Group, Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Centre for Analytical Sciences Amsterdam (CASA), Science Park 904, 1098XHAmsterdam, The Netherlands
- AI4Science Lab, University of Amsterdam, Science Park 904, 1098XHAmsterdam, The Netherlands
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, 56082Minnesota, United States
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6
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Ahmad IAH, Losacco GL, Shchurik V, Wang X, Cohen RD, Herron AN, Aiken S, Fiorito D, Wang H, Reibarkh M, Nowak T, Makarov AA, Stoll DR, Guillarme D, Mangion I, Aggarwal VK, Yu JQ, Regalado EL. Trapping-Enrichment Multi-dimensional Liquid Chromatography with On-Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale. Angew Chem Int Ed Engl 2022; 61:e202117655. [PMID: 35139257 DOI: 10.1002/anie.202117655] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Indexed: 11/10/2022]
Abstract
At the forefront of chemistry and biology research, development timelines are fast-paced and large quantities of pure targets are rarely available. Herein, we introduce a new framework, which is built upon an automated, online trapping-enrichment multi-dimensional liquid chromatography platform (TE-Dt-mDLC) that enables: 1) highly efficient separation of complex mixtures in a first dimension (1 D-UV); 2) automated peak trapping-enrichment and buffer removal achieved through a sequence of H2 O and D2 O washes using an independent pump setup; and 3) a second dimension separation (2 D-UV-MS) with fully deuterated mobile phases and fraction collection to minimize protic residues for immediate NMR analysis while bypassing tedious drying processes and minimizing analyte degradation. Diverse examples of target isolation and characterization from organic synthesis and natural product chemistry laboratories are illustrated, demonstrating recoveries above 90 % using as little as a few micrograms of material.
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Affiliation(s)
- Imad A Haidar Ahmad
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | | | - Vladimir Shchurik
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Xiao Wang
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Ryan D Cohen
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alastair N Herron
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Sheenagh Aiken
- School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
| | - Daniele Fiorito
- School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
| | - Heather Wang
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Mikhail Reibarkh
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Timothy Nowak
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alexey A Makarov
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Dwight R Stoll
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, USA
| | - Davy Guillarme
- School of Pharmaceutical Sciences, University of Geneva, CMU, Rue Michel-Servet 1, 1211, Geneva 4, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU, Rue Michel-Servet 1, 1211, Geneva 4, Switzerland
| | - Ian Mangion
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | | | - Jin-Quan Yu
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Erik L Regalado
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
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7
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Ahmad IAH, Losacco GL, Shchurik V, Wang X, Cohen RD, Herron AN, Aiken S, Fiorito D, Wang H, Reibarkh M, Nowak T, Makarov AA, Stoll DR, Guillarme D, Mangion I, Aggarwal VK, Yu J, Regalado EL. Trapping‐Enrichment Multi‐dimensional Liquid Chromatography with On‐Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202117655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
| | | | - Vladimir Shchurik
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Xiao Wang
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Ryan D. Cohen
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Alastair N. Herron
- Department of Chemistry The Scripps Research Institute La Jolla CA 92037 USA
| | - Sheenagh Aiken
- School of Chemistry University of Bristol Bristol BS8 1TS UK
| | - Daniele Fiorito
- School of Chemistry University of Bristol Bristol BS8 1TS UK
| | - Heather Wang
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Mikhail Reibarkh
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Timothy Nowak
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Alexey A. Makarov
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | - Dwight R. Stoll
- Department of Chemistry Gustavus Adolphus College Saint Peter MN 56082 USA
| | - Davy Guillarme
- School of Pharmaceutical Sciences University of Geneva, CMU Rue Michel-Servet 1 1211 Geneva 4 Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva, CMU Rue Michel-Servet 1 1211 Geneva 4 Switzerland
| | - Ian Mangion
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
| | | | - Jin‐Quan Yu
- Department of Chemistry The Scripps Research Institute La Jolla CA 92037 USA
| | - Erik L. Regalado
- Analytical Research & Development, MRL, Merck & Co., Inc. Rahway NJ 07065 USA
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8
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Bayesian optimization of comprehensive two-dimensional liquid chromatography separations. J Chromatogr A 2021; 1659:462628. [PMID: 34731752 DOI: 10.1016/j.chroma.2021.462628] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 11/20/2022]
Abstract
Comprehensive two-dimensional liquid chromatography (LC×LC), is a powerful, emerging separation technique in analytical chemistry. However, as many instrumental parameters need to be tuned, the technique is troubled by lengthy method development. To speed up this process, we applied a Bayesian optimization algorithm. The algorithm can optimize LC×LC method parameters by maximizing a novel chromatographic response function based on the concept of connected components of a graph. The algorithm was benchmarked against a grid search (11,664 experiments) and a random search algorithm on the optimization of eight gradient parameters for four different samples of 50 compounds. The worst-case performance of the algorithm was investigated by repeating the optimization loop for 100 experiments with random starting experiments and seeds. Given an optimization budget of 100 experiments, the Bayesian optimization algorithm generally outperformed the random search and often improved upon the grid search. Moreover, the Bayesian optimization algorithm offered a considerably more sample-efficient alternative to grid searches, as it found similar optima to the grid search in far fewer experiments (a factor of 16-100 times less). This could likely be further improved by a more informed choice of the initialization experiments, which could be provided by the analyst's experience or smarter selection procedures. The algorithm allows for expansion to other method parameters (e.g., temperature, flow rate, etc.) and unlocks closed-loop automated method development.
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Besenhard MO, Tsatse A, Mazzei L, Sorensen E. Recent advances in modelling and control of liquid chromatography. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100685] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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10
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Zhu Y, Lesch A, Li X, Lin TE, Gasilova N, Jović M, Pick HM, Ho PC, Girault HH. Rapid Noninvasive Skin Monitoring by Surface Mass Recording and Data Learning. JACS AU 2021; 1:598-611. [PMID: 34056635 PMCID: PMC8154208 DOI: 10.1021/jacsau.0c00074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Indexed: 05/08/2023]
Abstract
Skin problems are often overlooked due to a lack of robust and patient-friendly monitoring tools. Herein, we report a rapid, noninvasive, and high-throughput analytical chemical methodology, aiming at real-time monitoring of skin conditions and early detection of skin disorders. Within this methodology, adhesive sampling and laser desorption ionization mass spectrometry are coordinated to record skin surface molecular mass in minutes. Automated result interpretation is achieved by data learning, using similarity scoring and machine learning algorithms. Feasibility of the methodology has been demonstrated after testing a total of 117 healthy, benign-disordered, or malignant-disordered skins. Remarkably, skin malignancy, using melanoma as a proof of concept, was detected with 100% accuracy already at early stages when the lesions were submillimeter-sized, far beyond the detection limit of most existing noninvasive diagnosis tools. Moreover, the malignancy development over time has also been monitored successfully, showing the potential to predict skin disorder progression. Capable of detecting skin alterations at the molecular level in a nonsurgical and time-saving manner, this analytical chemistry platform is promising to build personalized skin care.
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Affiliation(s)
- Yingdi Zhu
- Institute of Chemical Sciences and Engineering, School of Basic Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andreas Lesch
- Department of Industrial Chemistry "Toso Montanari", Universita degli Studi di Bologna, 40136 Bologna, Italy
| | - Xiaoyun Li
- Department of Fundamental Oncology, Université de Lausanne, 1066 Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Université de Lausanne, 1066 Epalinges, Switzerland
| | - Tzu-En Lin
- Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, 30010 Hsinchu, Taiwan
| | - Natalia Gasilova
- Institute of Chemical Sciences and Engineering, School of Basic Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Milica Jović
- Institute of Chemical Sciences and Engineering, School of Basic Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Horst Matthias Pick
- Environmental Engineering Institute, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Ping-Chih Ho
- Department of Fundamental Oncology, Université de Lausanne, 1066 Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Université de Lausanne, 1066 Epalinges, Switzerland
| | - Hubert H Girault
- Institute of Chemical Sciences and Engineering, School of Basic Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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11
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Molenaar SRA, Dahlseid TA, Leme GM, Stoll DR, Schoenmakers PJ, Pirok BWJ. Peak-tracking algorithm for use in comprehensive two-dimensional liquid chromatography - Application to monoclonal-antibody peptides. J Chromatogr A 2021; 1639:461922. [PMID: 33540183 DOI: 10.1016/j.chroma.2021.461922] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/14/2021] [Accepted: 01/16/2021] [Indexed: 10/22/2022]
Abstract
A peak-tracking algorithm was developed for use in comprehensive two-dimensional liquid chromatography coupled to mass spectrometry. Chromatographic peaks were tracked across two different chromatograms, utilizing the available spectral information, the statistical moments of the peaks and the relative retention times in both dimensions. The algorithm consists of three branches. In the pre-processing branch, system peaks are removed based on mass spectra compared to low intensity regions and search windows are applied, relative to the retention times in each dimension, to reduce the required computational power by elimination unlikely pairs. In the comparison branch, similarity between the spectral information and statistical moments of peaks within the search windows is calculated. Lastly, in the evaluation branch extracted-ion-current chromatograms are utilized to assess the validity of the pairing results. The algorithm was applied to peptide retention data recorded under varying chromatographic conditions for use in retention modelling as part of method optimization tools. Moreover, the algorithm was applied to complex peptide mixtures obtained from enzymatic digestion of monoclonal antibodies. The algorithm yielded no false positives. However, due to limitations in the peak-detection algorithm, cross-pairing within the same peaks occurred and six trace compounds remained falsely unpaired.
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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), the Netherlands.
| | - Tina A Dahlseid
- Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States
| | - Gabriel M Leme
- 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
| | - Peter J Schoenmakers
- 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), the Netherlands
| | - 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), the Netherlands
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12
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Stilo F, Bicchi C, Jimenez-Carvelo AM, Cuadros-Rodriguez L, Reichenbach SE, Cordero C. Chromatographic fingerprinting by comprehensive two-dimensional chromatography: Fundamentals and tools. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116133] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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13
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Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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14
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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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15
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Groeneveld G, Pirok BWJ, Schoenmakers PJ. Perspectives on the future of multi-dimensional platforms. Faraday Discuss 2020; 218:72-100. [PMID: 31140485 DOI: 10.1039/c8fd00233a] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Two-dimensional liquid chromatography (2D-LC) formats have emerged to help address separation problems that are too complex for conventional one-dimensional LC. There are a number of obstacles to the proliferation of 2D-LC that are gradually being removed. Reliable commercial instrumentation has become available and data analysis software is being improved. Detector-sensitivity and phase-system compatibility issues can largely be solved by using active-modulation strategies. The remaining challenge, developing good and fast 2D-LC methods within a reasonable time, may be solved with smart algorithms. The technology platform that has been developed for 2D-LC also creates a number of other possibilities. Between the two separation stages, all kinds of physical (e.g. dissolution) or chemical (e.g. enzymatic or light-induced degradation) processes can be made to take place, allowing a wide variety of experiments to be performed within a single, efficient and automated analysis. All these developments are discussed in this paper and a number of critical issues are identified. A practical example, the characterization of polysorbates by high-resolution comprehensive two-dimensional liquid chromatography in combination with high-resolution mass spectrometry, is described as a culmination of recent developments in 2D-LC and as an illustration of the current state of the art.
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Affiliation(s)
- Gino Groeneveld
- University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands.
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16
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Pirok BWJ, den Uijl MJ, Moro G, Berbers SVJ, Croes CJM, van Bommel MR, Schoenmakers PJ. Characterization of Dye Extracts from Historical Cultural-Heritage Objects Using State-of-the-Art Comprehensive Two-Dimensional Liquid Chromatography and Mass Spectrometry with Active Modulation and Optimized Shifting Gradients. Anal Chem 2019; 91:3062-3069. [PMID: 30650969 PMCID: PMC6383186 DOI: 10.1021/acs.analchem.8b05469] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
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Unbiased characterization
of dyes and their degradation products
in cultural-heritage objects requires an analytical method which provides
universal separation power regardless of dye classes. Dyes are small
molecules that vary widely in chemical structure and properties, which
renders their characterization by a single method challenging. We
have developed a comprehensive two-dimensional liquid chromatography
method hyphenated with mass spectrometry and UV–vis detection.
We use stationary-phase-assisted modulation to enhance the method
in terms of detection limits and solvent compatibility and to reduce
the analysis time. The PIOTR program was used to optimize an assembly
of shifting second-dimension gradients, which resulted in a high degree
of orthogonality (80% in terms of the asterisk concept). The resulting
method is universally applicable to all classes of dyes extracted
from cultural-heritage objects. Thanks to the high peak capacity and
orthogonality, dye components can be separated from chemically similar
impurities and degradation products, providing a detailed fingerprint
of the dyes mixture in a specific sample. The method was applied to
a number of challenging dye extracts from 17th- and 19th-century cultural-heritage
objects.
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Affiliation(s)
- 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.,TI-COAST , Science Park 904 , 1098 XH , Amsterdam , The Netherlands
| | - Mimi J den Uijl
- van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands
| | - Giacomo Moro
- van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands
| | - Sanne V J Berbers
- van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands
| | - Charlotte J M Croes
- van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands
| | - Maarten R van Bommel
- van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands.,Faculty of Humanities, Conservation, and Restoration of Cultural Heritage , University of Amsterdam , Johannes Vermeerplein 1 , 1071 DV , Amsterdam , The Netherlands
| | - Peter J Schoenmakers
- van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group , University of Amsterdam , Science Park 904 , 1098 XH , Amsterdam , The Netherlands
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17
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Pirok BWJ, Stoll DR, Schoenmakers PJ. Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications. Anal Chem 2019; 91:240-263. [PMID: 30380827 PMCID: PMC6322149 DOI: 10.1021/acs.analchem.8b04841] [Citation(s) in RCA: 202] [Impact Index Per Article: 40.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Bob W. J. Pirok
- University
of Amsterdam, van ’t Hoff
Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands
- TI-COAST, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Dwight R. Stoll
- Department
of Chemistry, Gustavus Adolphus College, Saint Peter, Minnesota 56082, United States
| | - Peter J. Schoenmakers
- University
of Amsterdam, van ’t Hoff
Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands
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