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Kensert A, Desmet G, Cabooter D. A perspective on the use of deep deterministic policy gradient reinforcement learning for retention time modeling in reversed-phase liquid chromatography. J Chromatogr A 2024; 1713:464570. [PMID: 38101304 DOI: 10.1016/j.chroma.2023.464570] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
Artificial intelligence and machine learning techniques are increasingly used for different tasks related to method development in liquid chromatography. In this study, the possibilities of a reinforcement learning algorithm, more specifically a deep deterministic policy gradient algorithm, are evaluated for the selection of scouting runs for retention time modeling. As a theoretical exercise, it is investigated whether such an algorithm can be trained to select scouting runs for any compound of interest allowing to retrieve its correct retention parameters for the three-parameter Neue-Kuss retention model. It is observed that three scouting runs are generally sufficient to retrieve the retention parameters with an accuracy (mean relative percentage error MRPE) of 1 % or less. When given the opportunity to select additional scouting runs, this does not lead to a significantly improved accuracy. It is also observed that the agent tends to give preference to isocratic scouting runs for retention time modeling, and is only motivated towards selecting gradient scouting runs when penalized (strongly) for large analysis/gradient times. This seems to reinforce the general power and usefulness of isocratic scouting runs for retention time modeling. Finally, the best results (lowest MRPE) are obtained when the agent manages to retrieve retention time data for % ACN at elution of the compound under consideration that spread the entire relevant range of ACN (5 % ACN to 95 % ACN) as well as possible, i.e., resulting in retention data at a low, intermediate and high % ACN. Based on the obtained results, we believe reinforcement learning holds great potential to automate and rationalize method development in liquid chromatography in the future.
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Affiliation(s)
- Alexander Kensert
- University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium; Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium
| | - Gert Desmet
- Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium
| | - Deirdre Cabooter
- University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium.
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Rutan SC, Cash K, Stoll DR. Experimental design and re-parameterization of the Neue-Kuss model for accurate and precise prediction of isocratic retention factors from gradient measurements in reversed phase liquid chromatography. J Chromatogr A 2023; 1711:464443. [PMID: 37890376 DOI: 10.1016/j.chroma.2023.464443] [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: 08/11/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
The present work describes a re-parameterization of the Neue Kuss (NK) model for describing retention in liquid chromatography, and this re-parameterized model is used to fit a large set of isocratic retention measurements with improved convergence properties relative to the original parameterization of the model. Next, an experimental design for retention measurements using mobile phase gradient elution conditions is proposed for the purpose of obtaining accurate and precise NK parameters. Simulated retention data for mobile phase gradient elution conditions with two different levels of noise, as well as an essentially zero noise level were fit with the re-parameterized model. The results showed that the re-parameterized fits yielded average (absolute value) prediction errors for the parameters at the highest noise level of 7.2 % for S1,ref, 18 % for S2,ref and 6.2 % for kref (the re-parameterized NK model parameters). These errors were significantly smaller than those for the original parameterization of the NK model, where the errors were 23 % for S1, 25 % for S2 and 160 % for kw (the original NK model parameters). Furthermore, isocratic retention factors predicted using these model parameters were found to have an average magnitude of error of 0.51 % for the re-parameterized model, as opposed to 6800 % for the model with the original parameterization. A further test of this approach was carried out for independent experimental measurements for five solutes on a C18 column. The average magnitude of error of the isocratic retention factors predicted from parameters obtained from fits of gradient data was 1.6 %, provided that the range of organic solvent compositions that the solute sampled in the mobile phase gradient experiments was consistent with the isocratic experiments. These results indicate that the re-parameterization of the NK model allows for significant improvements in the fitting process, and that the proposed experimental design allows for NK parameters to be extracted from mobile phase gradient experiments, with prediction accuracies of isocratic retention factors on the order of 1-2 %.
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Affiliation(s)
- Sarah C Rutan
- Department of Chemistry, Box 842006, Virginia Commonwealth University, Richmond, VA 23284-2006, USA.
| | - Kathryn Cash
- Department of Chemistry, Gustavus Adolphus College, 800 West College Avenue, Saint Peter, MN 56082, USA
| | - Dwight R Stoll
- Department of Chemistry, Gustavus Adolphus College, 800 West College Avenue, Saint Peter, MN 56082, USA
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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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den Uijl MJ, Roeland T, Bos TS, Schoenmakers PJ, van Bommel MR, Pirok BW. Assessing the feasibility of stationary-phase-assisted modulation for two-dimensional liquid-chromatography separations. J Chromatogr A 2022; 1679:463388. [DOI: 10.1016/j.chroma.2022.463388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/18/2022] [Accepted: 07/21/2022] [Indexed: 02/06/2023]
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Stoll DR, Kainz G, Dahlseid TA, Kempen TJ, Brau T, Pirok BWJ. An approach to high throughput measurement of accurate retention data in liquid chromatography. J Chromatogr A 2022; 1678:463350. [PMID: 35896047 DOI: 10.1016/j.chroma.2022.463350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 11/30/2022]
Abstract
Efforts to model and simulate various aspects of liquid chromatography (LC) separations (e.g., retention, selectivity, peak capacity, injection breakthrough) depend on experimental retention measurements to use as the basis for the models and simulations. Often these modeling and simulation efforts are limited by datasets that are too small because of the cost (time and money) associated with making the measurements. Other groups have demonstrated improvements in throughput of LC separations by focusing on "overhead" associated with the instrument itself - for example, between-analysis software processing time, and autosampler motions. In this paper we explore the possibility of using columns with small volumes (i.e., 5 mm x 2.1 mm i.d.) compared to conventional columns (e.g., 100 mm x 2.1 mm i.d.) that are typically used for retention measurements. We find that isocratic retention factors calculated for columns with these dimensions are different by about 20%; we attribute this difference - which we interpret as an error in measurements based on data from the 5 mm column - to extra-column volume associated with inlet and outlet frits. Since retention factor is a thermodynamic property of the mobile/stationary phase system under study, it should be independent of the dimensions of the column that is used for the measurement. We propose using ratios of retention factors (i.e., selectivities) to translate retention measurements between columns of different dimensions, so that measurements made using small columns can be used to make predictions for separations that involve conventional columns. We find that this approach reduces the difference in retention factors (5 mm compared to 100 mm columns) from an average of 18% to an average absolute difference of 1.7% (all errors less than 8%). This approach will significantly increase the rate at which high quality retention data can be collected to thousands of measurements per instrument per day, which in turn will likely have a profound impact on the quality of models and simulations that can be developed for many aspects of LC separations.
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Affiliation(s)
- Dwight R Stoll
- Gustavus Adolphus College, 800W College Ave, St. Peter, MN 56082, USA.
| | - Gudrun Kainz
- Gustavus Adolphus College, 800W College Ave, St. Peter, MN 56082, USA
| | - Tina A Dahlseid
- Gustavus Adolphus College, 800W College Ave, St. Peter, MN 56082, USA
| | - Trevor J Kempen
- Gustavus Adolphus College, 800W College Ave, St. Peter, MN 56082, USA
| | - Tyler Brau
- Gustavus Adolphus College, 800W College Ave, St. Peter, MN 56082, USA
| | - Bob W J Pirok
- Gustavus Adolphus College, 800W College Ave, St. Peter, MN 56082, USA; 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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