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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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Poole CF, Atapattu SN. Determination of physicochemical properties of small molecules by reversed-phase liquid chromatography. J Chromatogr A 2020; 1626:461427. [DOI: 10.1016/j.chroma.2020.461427] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/19/2020] [Accepted: 07/22/2020] [Indexed: 02/07/2023]
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3
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The impact of lipophilicity on environmental processes, drug delivery and bioavailability of food components. Microchem J 2019. [DOI: 10.1016/j.microc.2019.01.030] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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4
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Artificial Neural Network Prediction of Retention of Amino Acids in Reversed-Phase HPLC under Application of Linear Organic Modifier Gradients and/or pH Gradients. Molecules 2019; 24:molecules24030632. [PMID: 30754702 PMCID: PMC6384946 DOI: 10.3390/molecules24030632] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 02/06/2019] [Accepted: 02/07/2019] [Indexed: 12/29/2022] Open
Abstract
A multi-layer artificial neural network (ANN) was used to model the retention behavior of 16 o-phthalaldehyde derivatives of amino acids in reversed-phase liquid chromatography under application of various gradient elution modes. The retention data, taken from literature, were collected in acetonitrile⁻water eluents under application of linear organic modifier gradients ( gradients), pH gradients, or double pH/ gradients. At first, retention data collected in gradients and pH gradients were modeled separately, while these were successively combined in one dataset and fitted simultaneously. Specific ANN-based models were generated by combining the descriptors of the gradient profiles with 16 inputs representing the amino acids and providing the retention time of these solutes as the response. Categorical "bit-string" descriptors were adopted to identify the solutes, which allowed simultaneously modeling the retention times of all 16 target amino acids. The ANN-based models tested on external gradients provided mean errors for the predicted retention times of 1.1% ( gradients), 1.4% (pH gradients), 2.5% (combined and pH gradients), and 2.5% (double pH/ gradients). The accuracy of ANN prediction was better than that previously obtained by fitting of the same data with retention models based on the solution of the fundamental equation of gradient elution.
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Taheri M, Bagheri M, Moazeni-Pourasil RS, Ghassempour A. Response surface methodology based on central composite design accompanied by multivariate curve resolution to model gradient hydrophilic interaction liquid chromatography: Prediction of separation for five major opium alkaloids. J Sep Sci 2017; 40:3602-3611. [DOI: 10.1002/jssc.201700416] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/10/2017] [Accepted: 07/09/2017] [Indexed: 11/05/2022]
Affiliation(s)
- Mohammadreza Taheri
- Medicinal Plants and Drugs Research Institute; Shahid Beheshti University; Tehran Iran
| | - Mohsen Bagheri
- Medicinal Plants and Drugs Research Institute; Shahid Beheshti University; Tehran Iran
| | | | - Alireza Ghassempour
- Medicinal Plants and Drugs Research Institute; Shahid Beheshti University; Tehran Iran
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Sykora D, Vozka J, Tesarova E. Chromatographic methods enabling the characterization of stationary phases and retention prediction in high-performance liquid chromatography and supercritical fluid chromatography. J Sep Sci 2015; 39:115-31. [DOI: 10.1002/jssc.201501023] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 10/08/2015] [Accepted: 10/08/2015] [Indexed: 11/11/2022]
Affiliation(s)
- David Sykora
- Department of Analytical Chemistry; University of Chemistry and Technology; Prague Czech Republic
| | - Jiri Vozka
- Department of Analytical Chemistry; University of Chemistry and Technology; Prague Czech Republic
- Department of Physical and Macromolecular Chemistry, Faculty of Science; Charles University in Prague; Prague Czech Republic
| | - Eva Tesarova
- Department of Physical and Macromolecular Chemistry, Faculty of Science; Charles University in Prague; Prague Czech Republic
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Wiczling P, Struck-Lewicka W, Kubik Ł, Siluk D, Markuszewski M, Kaliszan R. The simultaneous determination of hydrophobicity and dissociation constant by liquid chromatography–mass spectrometry. J Pharm Biomed Anal 2014; 94:180-7. [DOI: 10.1016/j.jpba.2014.01.038] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 01/23/2014] [Indexed: 11/16/2022]
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8
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Zisi C, Fasoula S, Pappa-Louisi A, Nikitas P. Expressions for Multilinear Combined pH/Organic Solvent Elution of Ionizable Analytes in Reversed-Phase HPLC. Anal Chem 2013; 85:9514-21. [DOI: 10.1021/ac4021243] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ch. Zisi
- Laboratory
of Physical Chemistry,
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - S. Fasoula
- Laboratory
of Physical Chemistry,
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - A. Pappa-Louisi
- Laboratory
of Physical Chemistry,
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - P. Nikitas
- Laboratory
of Physical Chemistry,
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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