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Rutan SC, Kempen T, Dahlseid T, Kruger Z, Pirok B, Shackman JG, Zhou Y, Wang Q, Stoll DR. Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity. J Chromatogr A 2024; 1731:465127. [PMID: 39053256 DOI: 10.1016/j.chroma.2024.465127] [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: 04/08/2024] [Revised: 06/13/2024] [Accepted: 06/28/2024] [Indexed: 07/27/2024]
Abstract
Reversed-phase (RP) liquid chromatography is an important tool for the characterization of materials and products in the pharmaceutical industry. Method development is still challenging in this application space, particularly when dealing with closely-related compounds. Models of chromatographic selectivity are useful for predicting which columns out of the hundreds that are available are likely to have very similar, or different, selectivity for the application at hand. The hydrophobic subtraction model (HSM1) has been widely employed for this purpose; the column database for this model currently stands at 750 columns. In previous work we explored a refinement of the original HSM1 (HSM2) and found that increasing the size of the dataset used to train the model dramatically reduced the number of gross errors in predictions of selectivity made using the model. In this paper we describe further work in this direction (HSM3), this time based on a much larger solute set (1014 solute/stationary phase combinations) containing selectivities for compounds covering a broader range of physicochemical properties compared to HSM1. The molecular weight range was doubled, and the range of the logarithm of the octanol/water partition coefficients was increased slightly. The number of active pharmaceutical ingredients and related synthetic intermediates and impurities was increased from four to 28, and ten pairs of closely related structures (e.g., geometric and cis-/trans- isomers) were included. The HSM3 model is based on retention measurements for 75 compounds using 13 RP stationary phases and a mobile phase of 40/60 acetonitrile/25 mM ammonium formate buffer at pH 3.2. This data-driven model produced predictions of ln α (chromatographic selectivity using ethylbenzene as the reference compound) with average absolute errors of approximately 0.033, which corresponds to errors in α of about 3 %. In some cases, the prediction of the trans-/cis- selectivities for positional and geometric isomers was relatively accurate, and the driving forces for the observed selectivity could be inferred by examination of the relative magnitudes of the terms in the HSM3 model. For some geometric isomer pairs the interactions mainly responsible for the observed selectivities could not be rationalized due to large uncertainties for particular terms in the model. This suggests that more work is needed in the future to explore other HSM-type models and continue expanding the training dataset in order to continue improving the predictive accuracy of these models. Additionally, we release with this paper a much larger data set (43,329 total retention measurements) at multiple mobile phase compositions, to enable other researchers to pursue their own lines of inquiry related to RP selectivity.
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Affiliation(s)
- Sarah C Rutan
- Department of Chemistry, Virginia Commonwealth University, Box 842006, Richmond, VA 23284-2006, USA
| | - Trevor Kempen
- Department of Chemistry, Gustavus Adolphus College, 800 W. College Ave., St. Peter, MN 56082, USA
| | - Tina Dahlseid
- Department of Chemistry, Gustavus Adolphus College, 800 W. College Ave., St. Peter, MN 56082, USA
| | - Zachary Kruger
- Department of Chemistry, Gustavus Adolphus College, 800 W. College Ave., St. Peter, MN 56082, USA
| | - Bob Pirok
- Department of Chemistry, Gustavus Adolphus College, 800 W. College Ave., St. Peter, MN 56082, USA
| | - Jonathan G Shackman
- Chemical Process Development, Bristol Myers Squibb, 1 Squibb Dr., New Brunswick, NJ 08903, USA
| | - Yiyang Zhou
- Chemical Process Development, Bristol Myers Squibb, 1 Squibb Dr., New Brunswick, NJ 08903, USA
| | - Qinggang Wang
- Chemical Process Development, Bristol Myers Squibb, 1 Squibb Dr., New Brunswick, NJ 08903, USA
| | - Dwight R Stoll
- Department of Chemistry, Gustavus Adolphus College, 800 W. College Ave., St. Peter, MN 56082, USA.
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Beck AG, Fine J, Aggarwal P, Regalado EL, Levorse D, De Jesus Silva J, Sherer EC. Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals. J Chromatogr A 2024; 1730:465109. [PMID: 38968662 DOI: 10.1016/j.chroma.2024.465109] [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: 03/25/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/07/2024]
Abstract
The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.
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Affiliation(s)
- Armen G Beck
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Jonathan Fine
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Pankaj Aggarwal
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA.
| | - Erik L Regalado
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Dorothy Levorse
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | | | - Edward C Sherer
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
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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] [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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Szucs R, Brown R, Brunelli C, Hradski J, Masár M. Impact of structural similarity on the accuracy of retention time prediction. J Chromatogr A 2023; 1707:464317. [PMID: 37634261 DOI: 10.1016/j.chroma.2023.464317] [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: 06/16/2023] [Revised: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 08/29/2023]
Abstract
Quantitative Structure-Retention Relationships offer a valuable tool for de-risking chromatographic methods in relation to newly formed or hypothetical compounds, arising from synthetic processes or formulation activities. They can also be used to identify optimal separation conditions, or in support of structural elucidation. In this contribution, we provide a systematic study of the relationship between the accuracy of the retention model, the size of the training set and its structural similarity to the predicted compound. We compare structural similarity expressed either on a fingerprint basis (e.g., Tanimoto index), or by Euclidean distance calculated from of subset of molecular descriptors. The results presented indicate that accurate and predictive models can be built from a small dataset containing as few as 25 compounds, provided that the training set is structurally similar to the test compound. When the training set contains compounds selected by minimizing the Euclidean distance calculated from 3 descriptors most correlated with the retention time, root mean square error of 0.48 min and correlation coefficient of 0.9464 were observed for the test sets of 104 compounds. Moreover, these models meet the Tropsha predictivity criteria. These findings potentially bring the prediction of retention times within the practical reach of pharmaceutical analysts involved in chromatographic method development. We also present an optimisation approach to select algorithm settings in order to minimize the prediction error and ensure model predictivity.
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Affiliation(s)
- Roman Szucs
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University Bratislava, Ilkovičova 6, SK-84215 Bratislava, Slovakia.
| | - Roland Brown
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, UK
| | | | - Jasna Hradski
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University Bratislava, Ilkovičova 6, SK-84215 Bratislava, Slovakia
| | - Marián Masár
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University Bratislava, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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Kumari P, Van Laethem T, Hubert P, Fillet M, Sacré PY, Hubert C. Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy. Molecules 2023; 28:molecules28041696. [PMID: 36838689 PMCID: PMC9964055 DOI: 10.3390/molecules28041696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/05/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.
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Affiliation(s)
- Priyanka Kumari
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Correspondence: (P.K.); (C.H.); Tel.: +32-(0)-43664326 (C.H.)
| | - Thomas Van Laethem
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Philippe Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Pierre-Yves Sacré
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Cédric Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Correspondence: (P.K.); (C.H.); Tel.: +32-(0)-43664326 (C.H.)
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Metabolite Profile of Athrixia phylicoides DC. (Bush Tea) and Determination of Inhibitory Mechanism against Tyrosinase Enzyme from Mushroom. COSMETICS 2022. [DOI: 10.3390/cosmetics9060133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Athrixia phylicoides DC. (Bush tea) is a shrub harvested in the north-eastern mountain regions of South Africa and belongs to the Asteraceae family. Generally, A. phylicoides is consumed as a hot tea beverage for its associated health benefits. The use of bush tea extracts for beauty enhancement has not been investigated even though several ethnobotanical reports have indicated its usage against skin imperfections. Therefore, the aim of the study was to assess plant metabolites of A. phylicoides for their inhibition of tyrosinase from mushroom and determine their inhibitory mechanism. Methanolic extracts (80% v/v) of A. phylicoides were evaluated using a tyrosinase-based TLC (thin-layer chromatography) autography technique. The inhibitory mechanism of active metabolites against the enzyme was determined using Lineweaver–Burk plots. Quercetin and an unknown metabolite with a retention factor (Rf) value of 0.73 inhibited melanogenesis. However, the IC50 value for Quercetin was reported as 51.07 ± 2.43 µg/mL higher than that of kojic acid of 5.22 ± 1.44 µg/mL. Chlorogenic acid was reported to have a similar Rf value as kojic acid, suggesting similarities in polarity and affinity towards the adsorbent material. However, chlorogenic acid with an IC50 value of 15.25 ± 1.18 µg/mL and an inhibitory strength of 2.92 could not inhibit melanogenesis with a similar intensity as kojic acid. The inhibitory mechanism for A. phylicoides extract was reported to be mixed inhibition (competitive/uncompetitive). The IC50 value for A. phylicoides was recorded as 20.65 ± 0.14 µg/mL with an inhibitory strength of 3.96. These results suggest that A. phylicoides extracts could be used against dark spots associated with scarring and ageing through modulation of tyrosinase activity.
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Van Laethem T, Kumari P, Hubert P, Fillet M, Sacré PY, Hubert C. A pharmaceutical-related molecules dataset for reversed-phase chromatography retention time prediction built on combining pH and gradient time conditions. Data Brief 2022; 42:108017. [PMID: 35310817 PMCID: PMC8931345 DOI: 10.1016/j.dib.2022.108017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/21/2022] [Accepted: 03/02/2022] [Indexed: 11/23/2022] Open
Abstract
There is a rising interest in the modeling and predicting of chromatographic retention. The progress towards more complex and comprehensive models emphasized the need for broad reliable datasets. The present dataset comprises small pharmaceutical compounds selected to cover a wide range in terms of physicochemical properties that are known to impact the retention in reversed-phase liquid chromatography. Moreover, this dataset was analyzed at five pH with two gradient slopes. It provides a reliable dataset with a diversity of conditions and compounds to support the building of new models. To enhance the robustness of the dataset, the compounds were injected individually, and each sequence of injections included a quality control sample. This unambiguous detection of each compound as well as a systematic analysis of a quality control sample ensured the quality of the reported retention times. Moreover, three different liquid chromatographic systems were used to increase the robustness of the dataset.
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Affiliation(s)
- Thomas Van Laethem
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +3, Avenue Hippocrate, 15, Liège 4000, Belgium.,Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +2, Avenue Hippocrate, 15, Liège 4000, Belgium
| | - Priyanka Kumari
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +3, Avenue Hippocrate, 15, Liège 4000, Belgium.,Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +2, Avenue Hippocrate, 15, Liège 4000, Belgium
| | - Philippe Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +2, Avenue Hippocrate, 15, Liège 4000, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +3, Avenue Hippocrate, 15, Liège 4000, Belgium
| | - Pierre-Yves Sacré
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +2, Avenue Hippocrate, 15, Liège 4000, Belgium
| | - Cédric Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, B36 Tower 4 (route 688 CHU) +2, Avenue Hippocrate, 15, Liège 4000, Belgium
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8
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Suspect and non-target screening of chemicals in clothing textiles by reversed-phase liquid chromatography/hybrid quadrupole-Orbitrap mass spectrometry. Anal Bioanal Chem 2021; 414:1403-1413. [PMID: 34786606 PMCID: PMC8724091 DOI: 10.1007/s00216-021-03766-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 11/27/2022]
Abstract
The global manufacturing of clothing is usually composed of multistep processes, which include a large number of chemicals. However, there is generally no information regarding the chemical content remaining in the finished clothes. Clothes in close and prolonged skin contact may thus be a significant source of daily human exposure to hazardous compounds depending on their ability to migrate from the textiles and be absorbed by the skin. In the present study, twenty-four imported garments on the Swedish market were investigated with respect to their content of organic compounds, using a screening workflow. Reversed-phase liquid chromatography coupled to electrospray ionization/high-resolution mass spectrometry was used for both suspect and non-target screening. The most frequently detected compound was benzothiazole followed by quinoline. Nitroanilines with suspected mutagenic and possible skin sensitization properties, and quinoline, a carcinogenic compound, were among the compounds occurring at the highest concentrations. In some garments, the level of quinoline was estimated to be close to or higher than 50,000 ng/g, the limit set by the REACH regulation. Other detected compounds were acridine, benzotriazoles, benzothiazoles, phthalates, nitrophenols, and organophosphates. Several of the identified compounds have logP and molecular weight values enabling skin uptake. This pilot study indicates which chemicals and compound classes should be prioritized for future quantitative surveys and control of the chemical content in clothing as well as research on skin transfer, skin absorption, and systemic exposure. The results also show that the current control and prevention from chemicals in imported garments on the Swedish market is insufficient.
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Sagandykova G, Buszewski B. Perspectives and recent advances in quantitative structure-retention relationships for high performance liquid chromatography. How far are we? Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116294] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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10
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Taraji M, Haddad PR. Method Optimisation in Hydrophilic-Interaction Liquid Chromatography by Design of Experiments Combined with Quantitative Structure–Retention Relationships. Aust J Chem 2021. [DOI: 10.1071/ch21102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate prediction of the separation conditions for a set of target analytes with no retention data available is fundamental for routine analytical assays but remains a very challenging task. In this paper, a quality by design (QbD) optimisation workflow capable of discovering the optimal chromatographic conditions for separation of new compounds in hydrophilic-interaction liquid chromatography (HILIC) is introduced. This workflow features the application of quantitative structure−retention relationship (QSRR) methodology in conjunction with design of experiments (DoE) principles and was used to carry out a two-level full factorial DoE optimisation for a mixture of pharmaceutical analytes on zwitterionic, amide, amine, and bare silica HILIC stationary phases, with mobile phases containing varying acetonitrile content, mobile phase pH, and salt concentration. A dual-filtering approach that considers both retention time (tR) and structural similarity was used to identify the optimal set of analytes to train the QSRR in order to maximise prediction accuracy. Highly predictive retention models (average R2 of 0.98) were obtained and statistical analysis of the prediction performance of the QSRR models demonstrated their ability to predict the retention times of new compounds based solely on their molecular structures, with root-mean-square errors of prediction in the range 7.6–11.0 %. Further, the obtained retention data for pharmaceutical test compounds were used to compute their separation selectivity, which was used as input into a DoE optimiser in order to select the optimal separation conditions. Experimental separations performed under the chosen optimal working conditions showed good agreement with the theoretical predictions. To the best of our knowledge, this is the first study of a QbD optimisation workflow assisted with dual-filtering-based retention modelling to facilitate the method development process in HILIC.
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Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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12
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Passarin PBS, Lourenço FR. Modeling an in silico platform to predict chromatographic profiles of UV filters using ChromSimulator. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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13
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Park SH, De Pra M, Haddad PR, Grosse S, Pohl CA, Steiner F. Localised quantitative structure–retention relationship modelling for rapid method development in reversed-phase high performance liquid chromatography. J Chromatogr A 2020; 1609:460508. [DOI: 10.1016/j.chroma.2019.460508] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/21/2019] [Accepted: 09/02/2019] [Indexed: 10/26/2022]
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14
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Luo C, Hu G, Huang M, Zou J, Jiang Y. Prediction on separation factor of chiral arylhydantoin compounds and recognition mechanism between chiral stationary phase and the enantiomers. J Mol Graph Model 2020; 94:107479. [DOI: 10.1016/j.jmgm.2019.107479] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 09/24/2019] [Accepted: 10/17/2019] [Indexed: 01/06/2023]
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Fitch WL, Khojasteh C, Aliagas I, Johnson K. Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification. Drug Metab Lett 2019; 12:93-100. [PMID: 30070179 PMCID: PMC6350196 DOI: 10.2174/1872312812666180802093347] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Revised: 06/13/2018] [Accepted: 07/31/2018] [Indexed: 11/26/2022]
Abstract
Background: There is a continued need for improvements in the efficiency of metabolite structure elucidation. Objective: We propose to take LC Retention Time (RT) into consideration during the process of structure determination. Methods: Herein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a pre-dictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature exam-ples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransfor-mation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding). Results: The CHI system for retention indexing is shown to be practical and simple to implement. A da-tabase of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an addi-tional 17 datasets from the literature. Application of this database is illustrated. Conclusion: In our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.
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Affiliation(s)
- William L Fitch
- Department of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, United States
| | - Cyrus Khojasteh
- Department of Drug Metabolism and Pharmacokinetics 1 DNA Way MS 412a, Genentech Inc., South San Francisco, CA 94080, United States
| | - Ignacio Aliagas
- Discovery Chemistry, 1 DNA Way, Genentech Inc., South San Francisco, CA 94080, United States
| | - Kevin Johnson
- Department of Drug Metabolism and Pharmacokinetics 1 DNA Way MS 412a, Genentech Inc., South San Francisco, CA 94080, United States
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16
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D’Atri V, Fekete S, Clarke A, Veuthey JL, Guillarme D. Recent Advances in Chromatography for Pharmaceutical Analysis. Anal Chem 2018; 91:210-239. [DOI: 10.1021/acs.analchem.8b05026] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Valentina D’Atri
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, CMU-Rue Michel Servet 1, 1211 Geneva 4, Switzerland
| | - Szabolcs Fekete
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, CMU-Rue Michel Servet 1, 1211 Geneva 4, Switzerland
| | - Adrian Clarke
- Novartis Pharma AG, Technical Research and Development, Chemical and Analytical Development (CHAD), Basel, CH4056, Switzerland
| | - Jean-Luc Veuthey
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, CMU-Rue Michel Servet 1, 1211 Geneva 4, Switzerland
| | - Davy Guillarme
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, CMU-Rue Michel Servet 1, 1211 Geneva 4, Switzerland
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17
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Schmidtsdorff S, Schmidt AH, Parr MK. Structure assisted impurity profiling for rapid method development in liquid chromatography. J Chromatogr A 2018; 1577:38-46. [DOI: 10.1016/j.chroma.2018.09.044] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/20/2018] [Accepted: 09/22/2018] [Indexed: 10/28/2022]
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18
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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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19
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Wen Y, Amos RIJ, Talebi M, Szucs R, Dolan JW, Pohl CA, Haddad PR. Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics. Anal Chem 2018; 90:9434-9440. [PMID: 29952550 DOI: 10.1021/acs.analchem.8b02084] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.
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Affiliation(s)
- Yabin Wen
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry , University of Tasmania , Private Bag 75 , Hobart , 7001 Tasmania , 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 Tasmania , Australia
| | - Mohammad Talebi
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry , University of Tasmania , Private Bag 75 , Hobart , 7001 Tasmania , Australia
| | - Roman Szucs
- Pfizer Global Research and Development , Sandwich CT139NJ , U.K
| | - John W Dolan
- LC Resources , McMinnville , Oregon 97128 , United States
| | | | - Paul R Haddad
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry , University of Tasmania , Private Bag 75 , Hobart , 7001 Tasmania , Australia
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20
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Sousa PFM, de Waard A, Åberg KM. Elucidation of chromatographic peak shifts in complex samples using a chemometrical approach. Anal Bioanal Chem 2018; 410:5229-5235. [PMID: 29947907 PMCID: PMC6061714 DOI: 10.1007/s00216-018-1173-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/04/2018] [Accepted: 05/29/2018] [Indexed: 11/08/2022]
Abstract
Chromatographic retention time peak shifts between consecutive analyses is a well-known fact yet not fully understood. Algorithms have been developed to align peaks between runs, but with no specific studies considering the causes of peak shifts. Here, designed experiments reveal chromatographic shift patterns for a complex peptide mixture that are attributable to the temperature and pH of the mobile phase. These results demonstrate that peak shifts are highly structured and are to a high degree explained by underlying differences in physico-chemical parameters of the chromatographic system and also provide experimental support for the alignment algorithm called the generalized fuzzy Hough transform which exploits this fact. It can be expected that the development of alignment algorithms enters a new phase resulting in increasingly accurate alignment by considering the latent structure of the peak shifts.
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Affiliation(s)
- Pedro F M Sousa
- Unit for Analytical Chemistry, Department of Environmental and Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden.
| | - Angela de Waard
- Unit for Analytical Chemistry, Department of Environmental and Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden
| | - K Magnus Åberg
- Unit for Analytical Chemistry, Department of Environmental and Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden
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21
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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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22
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Parr MK, Schmidt AH. Life cycle management of analytical methods. J Pharm Biomed Anal 2018; 147:506-517. [DOI: 10.1016/j.jpba.2017.06.020] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 06/10/2017] [Accepted: 06/12/2017] [Indexed: 11/30/2022]
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23
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Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography. J Chromatogr A 2017; 1523:173-182. [DOI: 10.1016/j.chroma.2017.02.054] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 02/20/2017] [Accepted: 02/23/2017] [Indexed: 11/19/2022]
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24
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Amos RIJ, Tyteca E, Talebi M, Haddad PR, Szucs R, Dolan JW, Pohl CA. Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure–Retention Relationships Studies. J Chem Inf Model 2017; 57:2754-2762. [DOI: 10.1021/acs.jcim.7b00346] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- 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
| | - Eva Tyteca
- Australian
Centre for Research on Separation Science (ACROSS), School of Physical
Sciences−Chemistry, University of Tasmania, Private Bag
75, Hobart 7001, Australia
- Analytical
Chemistry, AgroBioChem Department, Gembloux Agro-Biotech, University of Liège, 2 Passage des Deportes, Gembloux 5030, Belgium
| | - Mohammad Talebi
- 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
| | - Roman Szucs
- Pfizer Global Research and Development, Ramsgate Road, Sandwich CT13 9ND, U.K
| | - John W. Dolan
- LC Resources, 1795 NW Wallace
Road, McMinnville, Oregon 97128, United States
| | - Christopher A. Pohl
- Thermo Fisher Scientific, 490
Lakeside Drive, Sunnyvale, California 94085, United States
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25
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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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26
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Park SH, Haddad PR, Amos RI, Talebi M, Szucs R, Pohl CA, Dolan JW. Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, log P , and retention factor ratio to identify optimal analyte training sets for ion chromatography. J Chromatogr A 2017; 1520:107-116. [DOI: 10.1016/j.chroma.2017.09.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 09/02/2017] [Accepted: 09/06/2017] [Indexed: 11/17/2022]
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27
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Bobály B, D’Atri V, Beck A, Guillarme D, Fekete S. Analysis of recombinant monoclonal antibodies in hydrophilic interaction chromatography: A generic method development approach. J Pharm Biomed Anal 2017. [DOI: 10.1016/j.jpba.2017.06.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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28
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Ranjbar L, Talebi M, Haddad PR, Park SH, Cabot JM, Zhang M, Smejkal P, Foley JP, Breadmore MC. In Silico Screening of Two-Dimensional Separation Selectivity for Ion Chromatography × Capillary Electrophoresis Separation of Low-Molecular-Mass Organic Acids. Anal Chem 2017; 89:8808-8815. [DOI: 10.1021/acs.analchem.7b01187] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Leila Ranjbar
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Mohammad Talebi
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Paul R. Haddad
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Soo Hyun Park
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Joan M. Cabot
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
- ARC
Centre of Excellence for Electromaterials Science, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Min Zhang
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Petr Smejkal
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
| | - Joe P. Foley
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
- Department
of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Michael C. Breadmore
- Australian
Centre for Research on Separation Science, School of Physical Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia
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29
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Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems. J Chromatogr A 2017; 1507:53-62. [DOI: 10.1016/j.chroma.2017.05.044] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 05/17/2017] [Accepted: 05/18/2017] [Indexed: 01/31/2023]
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30
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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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