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Si-Hung L, Izumi Y, Nakao M, Takahashi M, Bamba T. Investigation of supercritical fluid chromatography retention behaviors using quantitative structure-retention relationships. Anal Chim Acta 2022; 1197:339463. [DOI: 10.1016/j.aca.2022.339463] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/02/2022] [Accepted: 01/06/2022] [Indexed: 12/11/2022]
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2
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Fesenko I, Azarkina R, Kirov I, Kniazev A, Filippova A, Grafskaia E, Lazarev V, Zgoda V, Butenko I, Bukato O, Lyapina I, Nazarenko D, Elansky S, Mamaeva A, Ivanov V, Govorun V. Phytohormone treatment induces generation of cryptic peptides with antimicrobial activity in the Moss Physcomitrella patens. BMC PLANT BIOLOGY 2019; 19:9. [PMID: 30616513 PMCID: PMC6322304 DOI: 10.1186/s12870-018-1611-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 12/20/2018] [Indexed: 06/01/2023]
Abstract
BACKGROUND Cryptic peptides (cryptides) are small bioactive molecules generated via degradation of functionally active proteins. Only a few examples of plant cryptides playing an important role in plant defense have been reported to date, hence our knowledge about cryptic signals hidden in protein structure remains very limited. Moreover, little is known about how stress conditions influence the size of endogenous peptide pools, and which of these peptides themselves have biological functions is currently unclear. RESULTS Here, we used mass spectrometry to comprehensively analyze the endogenous peptide pools generated from functionally active proteins inside the cell and in the secretome from the model plant Physcomitrella patens. Overall, we identified approximately 4,000 intracellular and approximately 500 secreted peptides. We found that the secretome and cellular peptidomes did not show significant overlap and that respective protein precursors have very different protein degradation patterns. We showed that treatment with the plant stress hormone methyl jasmonate induced specific proteolysis of new functional proteins and the release of bioactive peptides having an antimicrobial activity and capable to elicit the expression of plant defense genes. Finally, we showed that the inhibition of protease activity during methyl jasmonate treatment decreased the secretome antimicrobial potential, suggesting an important role of peptides released from proteins in immune response. CONCLUSIONS Using mass-spectrometry, in vitro experiments and bioinformatics analysis, we found that methyl jasmonate acid induces significant changes in the peptide pools and that some of the resulting peptides possess antimicrobial and regulatory activities. Moreover, our study provides a list of peptides for further study of potential plant cryptides.
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Affiliation(s)
- Igor Fesenko
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Regina Azarkina
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Ilya Kirov
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Andrei Kniazev
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Anna Filippova
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Ekaterina Grafskaia
- Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow region Russia
| | - Vassili Lazarev
- Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow region Russia
| | - Victor Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
| | - Ivan Butenko
- Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
| | - Olga Bukato
- Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
| | - Irina Lyapina
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Dmitry Nazarenko
- Department of Analytical Chemistry, Faculty of Chemistry, Lomonosov Moscow State University, Moscow, Russia
| | - Sergey Elansky
- Biological Faculty, Lomonosov Moscow State University, Moscow, Russia
| | - Anna Mamaeva
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Vadim Ivanov
- Laboratory of Proteomics, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Vadim Govorun
- Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia
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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: 59] [Impact Index Per Article: 8.4] [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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Barron LP, McEneff GL. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods. Talanta 2016; 147:261-70. [DOI: 10.1016/j.talanta.2015.09.065] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 09/22/2015] [Accepted: 09/27/2015] [Indexed: 12/01/2022]
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5
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Shafigulin RV, Safonova IA, Bulanova AV. Effect of the physicochemical parameters of benzimidazole molecules on their retention by a nonpolar sorbent from an aqueous acetonitrile solution. RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY A 2015. [DOI: 10.1134/s0036024415090289] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Munro K, Miller TH, Martins CP, Edge AM, Cowan DA, Barron LP. Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data. J Chromatogr A 2015; 1396:34-44. [DOI: 10.1016/j.chroma.2015.03.063] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 02/27/2015] [Accepted: 03/23/2015] [Indexed: 02/07/2023]
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Kouskoura MG, Kachrimanis KG, Markopoulou CK. Modeling the drugs' passive transfer in the body based on their chromatographic behavior. J Pharm Biomed Anal 2014; 100:94-102. [PMID: 25151230 DOI: 10.1016/j.jpba.2014.07.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Revised: 07/24/2014] [Accepted: 07/25/2014] [Indexed: 10/24/2022]
Abstract
One of the most challenging aims in modern analytical chemistry and pharmaceutical analysis is to create models for drugs' behavior based on simulation experiments. Since drugs' effects are closely related to their molecular properties, numerous characteristics of drugs are used in order to acquire a model of passive absorption and transfer in the human body. Importantly, such direction in innovative bioanalytical methodologies is also of stressful need in the area of personalized medicine to implement nanotechnological and genomics advancements. Simulation experiments were carried out by examining and interpreting the chromatographic behavior of 113 analytes/drugs (400 observations) in RP-HPLC. The dataset employed for this purpose included 73 descriptors which are referring to the physicochemical properties of the mobile phase mixture in different proportions, the physicochemical properties of the analytes and the structural characteristics of their molecules. A series of different software packages was used to calculate all the descriptors apart from those referring to the structure of analytes. The correlation of the descriptors with the retention time of the analytes eluted from a C4 column with an aqueous mobile phase was employed as dataset to introduce the behavior models in the human body. Their evaluation with a Partial Least Squares (PLS) software proved that the chromatographic behavior of a drug on a lipophilic stationary and a polar mobile phase is directly related to its drug-ability. At the same time, the behavior of an unknown drug in the human body can be predicted with reliability via the Artificial Neural Networks (ANNs) software.
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Affiliation(s)
- Maria G Kouskoura
- Laboratory of Pharmaceutical Analysis, Department of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Greece
| | - Kyriakos G Kachrimanis
- Department of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Greece
| | - Catherine K Markopoulou
- Laboratory of Pharmaceutical Analysis, Department of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Greece.
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Miller TH, Musenga A, Cowan DA, Barron LP. Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks. Anal Chem 2013; 85:10330-7. [DOI: 10.1021/ac4024878] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Thomas H. Miller
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Alessandro Musenga
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - David A. Cowan
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Leon P. Barron
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
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9
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Support vector regression based QSPR for the prediction of retention time of pesticide residues in gas chromatography–mass spectroscopy. Microchem J 2013. [DOI: 10.1016/j.microc.2012.05.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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10
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Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods. J CHEM-NY 2013. [DOI: 10.1155/2013/908586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A quantitative structure-retention relationships (QSRRs) method is employed to predict the retention time of 300 pesticide residues in animal tissues separated by gas chromatography-mass spectroscopy (GC-MS). Firstly, a six-parameter QSRR model was developed by means of multiple linear regression. The six molecular descriptors that were considered to account for the effect of molecular structure on the retention time are number of nitrogen, Solvation connectivity index-chi 1, BalabanYindex, Moran autocorrelation-lag 2/weighted by atomic Sanderson electronegativity, total absolute charge, and radial distribution function-6.0/unweighted. A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. The standard error values of ANN model for training, test, and validation sets are 1.559, 1.517, and 1.249, respectively, which are less than those obtained reveals by multiple linear regressions model (2.402, 1.858, and 2.036, resp.). Results obtained the reliability and good predictability of nonlinear QSRR model to predict the retention time of pesticides.
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D’Archivio AA, Maggi MA, Ruggieri F. Quantitative structure/eluent–retention relationships in reversed-phase high-performance liquid chromatography based on the solvatochromic method. Anal Bioanal Chem 2012; 405:755-66. [DOI: 10.1007/s00216-012-6191-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2012] [Revised: 06/08/2012] [Accepted: 06/11/2012] [Indexed: 11/24/2022]
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12
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Zarei K, Fatemi L. PREDICTION OF RETENTION OF PESTICIDES IN REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY USING CLASSIFICATION AND REGRESSION TREE ANALYSIS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS. J LIQ CHROMATOGR R T 2012. [DOI: 10.1080/10826076.2011.613140] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Kobra Zarei
- a School of Chemistry, Damghan University , Damghan , Iran
| | - Leila Fatemi
- a School of Chemistry, Damghan University , Damghan , Iran
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Markopoulou CK, Kouskoura MG, Koundourellis JE. Modelling by partial least squares the relationship between the HPLC mobile phases and analytes on phenyl column. J Sep Sci 2011; 34:1489-502. [DOI: 10.1002/jssc.201100091] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2011] [Revised: 03/17/2011] [Accepted: 03/17/2011] [Indexed: 11/09/2022]
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14
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Liao L, Qing D, Li J, Lei G. Structural characterization and Kovats retention index prediction for oxygen-containing organic compounds. J Mol Struct 2010. [DOI: 10.1016/j.molstruc.2010.05.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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15
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Fatemi MH, Ghorbanzad'e M, Baher E. Quantitative Structure Retention Relationship Modeling of Retention Time for Some Organic Pollutants. ANAL LETT 2010. [DOI: 10.1080/00032710903486294] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Artificial neural network modelling of retention of pesticides in various octadecylsiloxane-bonded reversed-phase columns and water–acetonitrile mobile phase. Anal Chim Acta 2009; 646:47-61. [DOI: 10.1016/j.aca.2009.05.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2008] [Revised: 03/12/2009] [Accepted: 05/15/2009] [Indexed: 11/18/2022]
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17
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Evaluating the performances of quantitative structure-retention relationship models with different sets of molecular descriptors and databases for high-performance liquid chromatography predictions. J Chromatogr A 2009; 1216:5030-8. [DOI: 10.1016/j.chroma.2009.04.064] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2009] [Revised: 04/17/2009] [Accepted: 04/21/2009] [Indexed: 11/17/2022]
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18
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Quantitative structure–retention relationships of pesticides in reversed-phase high-performance liquid chromatography based on WHIM and GETAWAY molecular descriptors. Anal Chim Acta 2008; 628:162-72. [DOI: 10.1016/j.aca.2008.09.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2008] [Revised: 09/05/2008] [Accepted: 09/08/2008] [Indexed: 11/24/2022]
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19
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Jinno K, Quiming NS, Denola NL, Saito Y. Modeling of retention of adrenoreceptor agonists and antagonists on polar stationary phases in hydrophilic interaction chromatography: a review. Anal Bioanal Chem 2008; 393:137-53. [DOI: 10.1007/s00216-008-2329-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2008] [Revised: 07/29/2008] [Accepted: 07/31/2008] [Indexed: 11/28/2022]
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Quiming NS, Denola NL, Saito Y, Jinno K. Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography. J Sep Sci 2008; 31:1550-63. [PMID: 18435511 DOI: 10.1002/jssc.200800077] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The development of retention prediction models for the seven ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2, and Rb1 on a polyamine-bonded stationary phase in hydrophilic interaction chromatography (HILIC) is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable for four temperature conditions (0, 10, 25, and 40 degrees C). Using stepwise MLR, the retention of the analytes in all the temperature conditions was satisfactorily described by a two-predictor model wherein the predictors were the percentage of ACN (%ACN) in the mobile phase and local dipole index (LDI) of the compounds. These predictors account for the contribution of the solute-related variable (LDI) and the influence of the mobile phase composition (%ACN) on the retention behavior of the ginsenosides. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R(2) values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.
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Affiliation(s)
- Noel S Quiming
- School of Materials Science, Toyohashi University of Technology, Toyohashi, Japan
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Aschi M, D’Archivio AA, Mazzeo P, Pierabella M, Ruggieri F. Modelling of the effect of solute structure and mobile phase pH and composition on the retention of phenoxy acid herbicides in reversed-phase high-performance liquid chromatography. Anal Chim Acta 2008; 616:123-37. [DOI: 10.1016/j.aca.2008.04.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2007] [Revised: 03/19/2008] [Accepted: 04/08/2008] [Indexed: 10/22/2022]
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Quiming NS, Denola NL, Samsuri SRB, Saito Y, Jinno K. Development of retention prediction models for adrenoreceptor agonists and antagonists on a polyvinyl alcohol-bonded stationary phase in hydrophilic interaction chromatography. J Sep Sci 2008; 31:1537-49. [DOI: 10.1002/jssc.200700598] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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23
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Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure–retention relationships. Anal Chim Acta 2007; 602:164-72. [DOI: 10.1016/j.aca.2007.09.014] [Citation(s) in RCA: 98] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2007] [Revised: 09/03/2007] [Accepted: 09/04/2007] [Indexed: 11/22/2022]
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Carlucci G, D'Archivio AA, Maggi MA, Mazzeo P, Ruggieri F. Investigation of retention behaviour of non-steroidal anti-inflammatory drugs in high-performance liquid chromatography by using quantitative structure–retention relationships. Anal Chim Acta 2007; 601:68-76. [PMID: 17904471 DOI: 10.1016/j.aca.2007.08.026] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2007] [Revised: 08/18/2007] [Accepted: 08/21/2007] [Indexed: 11/22/2022]
Abstract
In this paper, a quantitative structure-retention relationship (QSRR) method is employed to model the retention behaviour in reversed-phase high-performance liquid chromatography of arylpropionic acid derivatives, largely used non-steroidal anti-inflammatory drugs (NSAIDs). Computed molecular descriptors and the organic modifier content in the mobile phase are associated into a comprehensive model to describe the effect of both solute structure and eluent composition on the isocratic retention of these drugs in water-acetonitrile mobile phases. Multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is used to extract from a large set of computed 3D descriptors an optimal subset. Based on GA-MLR analysis, a five-dimensional QSRR model is identified. All the four selected molecular descriptors belong to the category of GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors. The related multilinear model exhibits a quite good fitting and predictive performance. This model is further improved using an artificial neural network (ANN) learned by error back-propagation. Finally, the ANN-based model displays a remarkably better performance as compared with the MLR counterpart and, based on external validation, is able to predict with good accuracy the behaviour of unknown arylpropionic NSAIDs in the range of mobile phase composition of analytical interest (between 35 and 75% acetonitrile (v/v)).
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Affiliation(s)
- Giuseppe Carlucci
- Università degli Studi G. D'Annunzio di Chieti, Dipartimento di Scienze del Farmaco,Via dei Vestini, 66100 Chieti, Italy
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