Dixon, T.M., Williams, J. orcid.org/0000-0003-2954-8339, Besenhard, M.O. et al. (6 more authors) (2024) Operator-free HPLC automated method development guided by Bayesian optimization. Digital Discovery, 3 (8). pp. 1591-1601. ISSN 2635-098X
Abstract
The need to efficiently develop high performance liquid chromatography (HPLC) methods, whilst adhering to quality by design principles is of paramount importance when it comes to impurity detection in the synthesis of active pharmaceutical ingredients. This study highlights a novel approach that fully automates HPLC method development using black-box single and multi-objective Bayesian optimization algorithms. Three continuous variables including the initial isocratic hold time, initial organic modifier concentration and the gradient time were adjusted to simultaneously optimize the number of peaks detected, the resolution between peaks and the method length. Two mixtures of analytes, one with seven compounds and one with eleven compounds, were investigated. The system explored the design space to find a global optimum in chromatogram quality without human assistance, and methods that gave baseline resolution were identified. Optimal operating conditions were typically reached within just 13 experiments. The single and multi-objective Bayesian optimization algorithms were compared to show that multi-objective optimization was more suitable for HPLC method development. This allowed for multiple chromatogram acceptance criteria to be selected without having to repeat the entire optimization, making it a useful tool for robustness testing. Work in this paper presents a fully “operator-free” and closed loop HPLC method optimization process that can find optimal methods quickly when compared to other modern HPLC optimization techniques such as design of experiments, linear solvent strength models or quantitative structure retention relationships.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 3.0). |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering RF2122-21-200 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jul 2024 09:35 |
Last Modified: | 30 Sep 2024 13:49 |
Status: | Published |
Publisher: | Royal Society of Chemistry |
Identification Number: | 10.1039/d4dd00062e |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214196 |