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Van Laethem T, Kumari P, Boulanger B, Hubert P, Fillet M, Sacré PY, Hubert C. Uncertainty management for In Silico screening of reversed-phase liquid chromatography methods for small compounds. J Pharm Biomed Anal 2024; 249:116373. [PMID: 39047465 DOI: 10.1016/j.jpba.2024.116373] [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: 05/21/2024] [Revised: 07/18/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024]
Abstract
The process of developing new reversed-phase liquid chromatography methods can be both time-consuming and challenging. To meet this challenge, statistics-based strategies have emerged as cost-effective, efficient and flexible solutions. In the present study, we use a Bayesian response surface methodology, which takes advantage of the knowledge of the pKa values of the compounds present in the analyzed sample to model their retention behavior. A multi-criteria decision analysis (MCDA) was then developed to exploit the uncertainty information inherent in the model distributions. This strategic approach is designed to integrate seamlessly with quantitative structure retention relationship (QSRR) models, forming an initial in-silico screening phase. Of the two methods presented for MCDA, one showed promising results. The method development process was carried out with the optimization phase, generating a design space that corroborates the results of the selection phase.
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Affiliation(s)
- Thomas Van Laethem
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, Liège 4000, Belgium; Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, Liège 4000, Belgium.
| | - Priyanka Kumari
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, Liège 4000, Belgium; Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, Liège 4000, Belgium
| | | | - Philippe Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, Liège 4000, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, Liège 4000, Belgium
| | - Pierre-Yves Sacré
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, Liège 4000, Belgium
| | - Cédric Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, Liège 4000, Belgium.
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Van Laethem T, Kumari P, Boulanger B, Hubert P, Fillet M, Sacré PY, Hubert C. User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238306. [PMID: 36500399 PMCID: PMC9735675 DOI: 10.3390/molecules27238306] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022]
Abstract
In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.
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Affiliation(s)
- Thomas Van Laethem
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Correspondence: (T.V.L.); (C.H.)
| | - Priyanka Kumari
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | | | - Philippe Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | - Pierre-Yves Sacré
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | - Cédric Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Correspondence: (T.V.L.); (C.H.)
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Wiczling P, Kamedulska A, Kubik Ł. Application of Bayesian Multilevel Modeling in the Quantitative Structure-Retention Relationship Studies of Heterogeneous Compounds. Anal Chem 2021; 93:6961-6971. [PMID: 33905658 DOI: 10.1021/acs.analchem.0c05227] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Quantitative structure-retention relationships (QSRRs) are used in the field of chromatography to model the relationship between an analyte structure and chromatographic retention. Such models are typically difficult to build and validate for heterogeneous compounds because of their many descriptors and relatively limited analyte-specific data. In this study, a Bayesian multilevel model is proposed to characterize the isocratic retention time data collected for 1026 heterogeneous analytes. The QSRR considers the effects of the molecular mass and 100 functional groups (substituents) on analyte-specific chromatographic parameters of the Neue model (i.e., the retention factor in water, the retention factor in acetonitrile, and the curvature coefficient). A Bayesian multilevel regression model was used to smooth noisy parameter estimates with too few data and to consider the uncertainties in the model parameters. We discuss the benefits of the Bayesian multilevel model (i) to understand chromatographic data, (ii) to quantify the effect of functional groups on chromatographic retention, and (iii) to predict analyte retention based on various types of preliminary data. The uncertainty of isocratic and gradient predictions was visualized using uncertainty chromatograms and discussed in terms of usefulness in decision making. We think that this method will provide the most benefit in providing a unified scheme for analyzing large chromatographic databases and assessing the impact of functional groups and other descriptors on analyte retention.
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Affiliation(s)
- Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416 Gdańsk, Poland
| | - Agnieszka Kamedulska
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416 Gdańsk, Poland
| | - Łukasz Kubik
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416 Gdańsk, Poland
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Bouwmeester R, Martens L, Degroeve S. Generalized Calibration Across Liquid Chromatography Setups for Generic Prediction of Small-Molecule Retention Times. Anal Chem 2020; 92:6571-6578. [DOI: 10.1021/acs.analchem.0c00233] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
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Czyrski A, Sznura J. The application of Box-Behnken-Design in the optimization of HPLC separation of fluoroquinolones. Sci Rep 2019; 9:19458. [PMID: 31857613 PMCID: PMC6923357 DOI: 10.1038/s41598-019-55761-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 12/03/2019] [Indexed: 12/19/2022] Open
Abstract
Box-Behnken Design is a useful tool for the optimization of the chromatographic analysis. The goal of this study was to select the most significant factors that influenced the following parameters of the chromatographic separation: retention time, relative retention time, symmetry of the peaks, tailing factor, a number of theoretical plates, Foley – Dorsey parameter, resolution factor, peak width at half height. The results underwent the ANOVA test to find the statistically significant variables and interactions between them. The level of significance was for p < 0.05. The polynomial equations described quantitatively the statistically significant parameters and the interactions between them. The statistical analysis indicated both the best conditions for the separation of the compounds and the variables that were most influential for peaks’ parameters. The four-factor analysis performed for LEVO and MOXI indicated that ACN, TEA and pH are the most significant factors that influence the separation. The analysis for the pair CIPRO and LEVO required six factors. The statistical analysis proved that the most significant factors are ACN, MeOH and TEA. In the separation of these two compounds on the HPLC column, the interaction ACN × MeOH was also significant.
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Affiliation(s)
- Andrzej Czyrski
- Poznań University of Medical Sciences, Department of Physical Pharmacy and Pharmacokinetics, Święcickiego 6 Street, 60-781, Poznań, Poland.
| | - Justyna Sznura
- Poznań University of Medical Sciences, Department of Physical Pharmacy and Pharmacokinetics, Święcickiego 6 Street, 60-781, Poznań, Poland
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Kubik Ł, Kaliszan R, Wiczling P. Analysis of Isocratic-Chromatographic-Retention Data using Bayesian Multilevel Modeling. Anal Chem 2018; 90:13670-13679. [DOI: 10.1021/acs.analchem.8b04033] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Łukasz Kubik
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Generała Józefa Hallera 107, 80-416 Gdańsk, Poland
| | - Roman Kaliszan
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Generała Józefa Hallera 107, 80-416 Gdańsk, Poland
| | - Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Generała Józefa Hallera 107, 80-416 Gdańsk, Poland
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Amos RI, Haddad PR, Szucs R, Dolan JW, Pohl CA. Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography. Trends Analyt Chem 2018. [DOI: 10.1016/j.trac.2018.05.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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9
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Analyzing chromatographic data using multilevel modeling. Anal Bioanal Chem 2018; 410:3905-3915. [DOI: 10.1007/s00216-018-1061-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 03/20/2018] [Accepted: 04/03/2018] [Indexed: 11/26/2022]
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Wiczling P. Evaluation of sequential Bayesian-based method development procedures for chromatographic problems involving one, two, and three analytes. SEPARATION SCIENCE PLUS 2018. [DOI: 10.1002/sscp.201700037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics; Medical University of Gdańsk; Gdańsk Poland
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Taraji M, Haddad PR, Amos RIJ, Talebi M, Szucs R, Dolan JW, Pohl CA. Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review. Anal Chim Acta 2017; 1000:20-40. [PMID: 29289311 DOI: 10.1016/j.aca.2017.09.041] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 09/22/2017] [Accepted: 09/24/2017] [Indexed: 02/09/2023]
Abstract
With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.
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Affiliation(s)
- Maryam Taraji
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Paul R Haddad
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia.
| | - Ruth I J Amos
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Mohammad Talebi
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Roman Szucs
- Pfizer Global Research and Development, CT13 9NJ, Sandwich, UK
| | - John W Dolan
- LC Resources, 1795 NW Wallace Rd., McMinnville, OR 97128, USA
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Nekrasova NA, Kurbatova SV. Quantitative structure–chromatographic retention correlations of quinoline derivatives. J Chromatogr A 2017; 1492:55-60. [DOI: 10.1016/j.chroma.2017.02.063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 02/07/2017] [Accepted: 02/26/2017] [Indexed: 12/19/2022]
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Taraji M, Haddad PR, Amos RIJ, Talebi M, Szucs R, Dolan JW, Pohl CA. Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure-Retention Relationships and Design of Experiments. Anal Chem 2017; 89:1870-1878. [PMID: 28208251 DOI: 10.1021/acs.analchem.6b04282] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R2 > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model's prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.
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Affiliation(s)
- Maryam Taraji
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania , Private Bag 75, Hobart 7001, Australia
| | - Paul R Haddad
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania , Private Bag 75, Hobart 7001, Australia
| | - Ruth I J Amos
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania , Private Bag 75, Hobart 7001, Australia
| | - Mohammad Talebi
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania , Private Bag 75, Hobart 7001, Australia
| | - Roman Szucs
- Pfizer Global Research and Development, Sandwich, United Kingdom
| | - John W Dolan
- LC Resources, McMinnville, Oregon, United States
| | - Chris A Pohl
- Thermo Fisher Scientific, Sunnyvale, California, United States
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