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Niezen LE, Desmet G. A new chromatographic response function with automatically adapting weight factor for automated method development. J Chromatogr A 2024; 1727:465008. [PMID: 38788402 DOI: 10.1016/j.chroma.2024.465008] [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: 03/29/2024] [Revised: 05/14/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
A critical factor for automated method development in chromatography is the maximization or minimization of an objective function describing the quality (and speed) of the separation. In chromatography, this function is commonly referred to as a chromatographic response function (CRF). Many CRFs have previously been introduced, but many have unfavourable properties such as featuring multiple optima, insufficient discriminatory power, and a too strong dependence on the weight factors needed to balance resolution and time penalty components. To overcome these problems, the present study introduces a new type of CRF wherein the relative weight of the time penalty term is a self-adaptive function of the separation quality. The ability to unambiguously identify the optimal gradient settings of this newly proposed CRF is compared to that of some of the most frequently used CRFs in a study covering 100 randomly composed in silico samples. Doing so, the new CRF is found to flawlessly lead to the correct solution (=linear gradient parameters providing the highest resolution in the shortest potential time) in 100 % of the cases, while the most frequently used literature CRFs were off-target for about 50 to 60 % of the samples, even when considering the availability of spectral peak shape data. Some slight alterations to the proposed CRF are introduced and discussed as well.
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Affiliation(s)
- Leon E Niezen
- Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussel 1050, Belgium
| | - Gert Desmet
- Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussel 1050, Belgium.
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2
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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. [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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Kensert A, Libin P, Desmet G, Cabooter D. Deep reinforcement learning for the direct optimization of gradient separations in liquid chromatography. J Chromatogr A 2024; 1720:464768. [PMID: 38442496 DOI: 10.1016/j.chroma.2024.464768] [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: 12/27/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/07/2024]
Abstract
While Reinforcement Learning (RL) has already proven successful in performing complex tasks, such as controlling large-scale epidemics, mitigating influenza and playing computer games beyond expert level, it is currently largely unexplored in the field of separation sciences. This paper therefore aims to introduce RL, specifically proximal policy optimization (PPO), in liquid chromatography, and evaluate whether it can be trained to optimize separations directly, based solely on the outcome of a single generic separation as input, and a reward signal based on the resolution between peak pairs (taking a value between [-1,1]). More specifically, PPO algorithms or agents were trained to select linear (1-segment) or multi-segment (2-, 3-, or 16-segment) gradients in 1 experiment, based on the outcome of an initial, generic linear gradient (ϕstart=0.3, ϕend=1.0, and tg=20min), to improve separations. The size of the mixtures to be separated varied between 10 and 20 components. Furthermore, two agents, selecting 16-segment gradients, were trained to perform this optimization using either 2 or 3 experiments, in sequence, to investigate whether the agents could improve separations further, based on previous outcomes. Results showed that the PPO agent can improve separations given the outcome of one generic scouting run as input, by selecting ϕ-programs tailored to the mixture under consideration. Allowing agents more freedom in selecting multi-segment gradients increased the reward from 0.891 to 0.908 on average; and allowing the agents to perform an additional experiment increased the reward from 0.908 to 0.918 on average. Finally, the agent outperformed random experiments as well as standard experiments (ϕstart=0.0, ϕend=1.0, and tg=20min) significantly; as random experiments resulted in average rewards between 0.220 and 0.283, and standard experiments resulted in average rewards of 0.840. In conclusion, while there is room for improvement, the results demonstrate the potential of RL in chromatography and present an interesting future direction for the automated optimization of separations.
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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
| | - Pieter Libin
- Vrije Universiteit Brussel, Department of Computer Science, Artificial Intelligence Laboratory, Pleinlaan 9, 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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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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Niezen LE, Bos TS, Schoenmakers PJ, Somsen GW, Pirok BWJ. Capacitively coupled contactless conductivity detection to account for system-induced gradient deformation in liquid chromatography. Anal Chim Acta 2023; 1271:341466. [PMID: 37328247 DOI: 10.1016/j.aca.2023.341466] [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: 01/11/2023] [Revised: 05/12/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled "contactless" conductivity detection (C4D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards.
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Affiliation(s)
- Leon E Niezen
- Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Tijmen S Bos
- Centre for Analytical Sciences Amsterdam (CASA), the Netherlands; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Peter J Schoenmakers
- Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Govert W Somsen
- Centre for Analytical Sciences Amsterdam (CASA), the Netherlands; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Bob W J Pirok
- Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.
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Boelrijk J, Ensing B, Forré P, Pirok BWJ. Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization. Anal Chim Acta 2023; 1242:340789. [PMID: 36657888 DOI: 10.1016/j.aca.2023.340789] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/13/2022] [Accepted: 01/02/2023] [Indexed: 01/07/2023]
Abstract
Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized.
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Affiliation(s)
- Jim Boelrijk
- AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands; AMLab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands.
| | - Bernd Ensing
- AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands; Computational Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands
| | - Patrick Forré
- AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands; AMLab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands
| | - Bob W J Pirok
- AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands; Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands.
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Performance of global retention models in the optimisation of the chromatographic separation (I): Simple multi-analyte samples. J Chromatogr A 2023; 1689:463756. [PMID: 36610184 DOI: 10.1016/j.chroma.2022.463756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/23/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022]
Abstract
Conventional retention models lead to accurate descriptions of the elution behaviour from the fitting of data for single solutes or from a set of solutes, one by one. However, the simultaneous fitting of several solutes through a regression process that separates the contributions of column and solvent from those of each solute is also possible. The result is a global retention model constituted by a set of equations with some common parameters (those associated with column and solvent), whereas others, specific to each solute, differ for each equation. This work explores the possibilities, advantages, and limitations of global models when they are applied to the optimisation of chromatographic resolution. A set constituted by 13 drugs (diuretics and β-blockers) and a training experimental design of seven multi-linear gradients are considered. Since standards for all compounds were available, the optimisation based on global models could be compared with the conventional optimisation, which is based on individual models. In their current state, global models do not predict changes in elution order, but they do allow for incorporating additional solutes (e.g., new analytes or matrix peaks) with only one new experiment. This possibility is explored by extending the model for the 13 analytes to include 26 peaks associated with a contamination in the injector. The combination of individual and global models allows an optimisation where the effects of matrix peaks on the separation of analytes can be integrated.
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Stoll DR, Pirok BW. Perspectives on the Use of Retention Modeling to Streamline 2D-LC Method Development: Current State and Future Prospects. LCGC NORTH AMERICA 2022. [DOI: 10.56530/lcgc.na.zo2782l9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The history of multidimensional liquid chromatography (MDLC) has been dominated by methods that have been developed using highly empirical, experience-driven, trial-and-error approaches. These approaches have been sufficient in progressing the field forward scientifically, primarily in academic research laboratories. However, more widespread usage of multidimensional separations will require more systematic approaches to method development that rely less on user experience and lower the barriers to development and use of methods by a wider community of users. In this mini-review, we discuss recent research aimed at developing such systematic, model-driven approaches to streamline method development and speculate about likely advances in the same direction in the near future. It seems likely that such model-driven approaches would be particularly helpful for methods developed for analyzing biopharmaceutical molecules, which tend to be very sensitive to slight changes in method conditions (for example, mobile phase composition).
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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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A chromatographic objective function to characterise chromatograms with unknown compounds or without standards available. J Chromatogr A 2015. [DOI: 10.1016/j.chroma.2015.07.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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