Héberger, Karoly

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  • Héberger, Karoly (8)

Author's Bibliography

Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y

Racz, Anita; Andrić, Filip; Bajusz, David; Héberger, Karoly

(Springer, New York, 2018)

TY  - BOOK
AU  - Racz, Anita
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Héberger, Karoly
PY  - 2018
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/3041
PB  - Springer, New York
T2  - Metabolomics
T1  - Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y
ER  - 
@book{
author = "Racz, Anita and Andrić, Filip and Bajusz, David and Héberger, Karoly",
year = "2018",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/3041",
publisher = "Springer, New York",
journal = "Metabolomics",
title = "Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y"
}
Racz, A., Andrić, F., Bajusz, D.,& Héberger, K. (2018). Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y.
Metabolomics
Springer, New York..
Racz A, Andrić F, Bajusz D, Héberger K. Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y. Metabolomics. 2018;
Racz Anita, Andrić Filip, Bajusz David, Héberger Karoly, "Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y" Metabolomics (2018)

Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods

Relić, Dubravka; Héberger, Karoly; Sakan, Sanja M.; Škrbić, Biljana; Popović, Aleksandar R.; Đorđević, Dragana S.

(Pergamon-Elsevier Science Ltd, Oxford, 2018)

TY  - JOUR
AU  - Relić, Dubravka
AU  - Héberger, Karoly
AU  - Sakan, Sanja M.
AU  - Škrbić, Biljana
AU  - Popović, Aleksandar R.
AU  - Đorđević, Dragana S.
PY  - 2018
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/2109
AB  - This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pancevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques. (C) 2018 Elsevier Ltd. All rights reserved.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Chemosphere
T1  - Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods
VL  - 198
SP  - 103
EP  - 110
DO  - 10.1016/j.chemosphere.2017.12.200
ER  - 
@article{
author = "Relić, Dubravka and Héberger, Karoly and Sakan, Sanja M. and Škrbić, Biljana and Popović, Aleksandar R. and Đorđević, Dragana S.",
year = "2018",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/2109",
abstract = "This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pancevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques. (C) 2018 Elsevier Ltd. All rights reserved.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Chemosphere",
title = "Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods",
volume = "198",
pages = "103-110",
doi = "10.1016/j.chemosphere.2017.12.200"
}
Relić, D., Héberger, K., Sakan, S. M., Škrbić, B., Popović, A. R.,& Đorđević, D. S. (2018). Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods.
Chemosphere
Pergamon-Elsevier Science Ltd, Oxford., 198, 103-110.
https://doi.org/10.1016/j.chemosphere.2017.12.200
Relić D, Héberger K, Sakan SM, Škrbić B, Popović AR, Đorđević DS. Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods. Chemosphere. 2018;198:103-110
Relić Dubravka, Héberger Karoly, Sakan Sanja M., Škrbić Biljana, Popović Aleksandar R., Đorđević Dragana S., "Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods" Chemosphere, 198 (2018):103-110,
https://doi.org/10.1016/j.chemosphere.2017.12.200 .
2
2
2

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Racz, Anita; Andrić, Filip; Bajusz, David; Héberger, Karoly

(Springer, New York, 2018)

TY  - JOUR
AU  - Racz, Anita
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Héberger, Karoly
PY  - 2018
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/2101
AB  - Introduction Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. Objectives The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Methods Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Results Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Conclusion Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.
PB  - Springer, New York
T2  - Metabolomics
T1  - Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
VL  - 14
IS  - 3
DO  - 10.1007/s11306-018-1327-y
ER  - 
@article{
author = "Racz, Anita and Andrić, Filip and Bajusz, David and Héberger, Karoly",
year = "2018",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/2101",
abstract = "Introduction Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. Objectives The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Methods Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Results Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Conclusion Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.",
publisher = "Springer, New York",
journal = "Metabolomics",
title = "Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles",
volume = "14",
number = "3",
doi = "10.1007/s11306-018-1327-y"
}
Racz, A., Andrić, F., Bajusz, D.,& Héberger, K. (2018). Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles.
Metabolomics
Springer, New York., 14(3).
https://doi.org/10.1007/s11306-018-1327-y
Racz A, Andrić F, Bajusz D, Héberger K. Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles. Metabolomics. 2018;14(3)
Racz Anita, Andrić Filip, Bajusz David, Héberger Karoly, "Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles" Metabolomics, 14, no. 3 (2018),
https://doi.org/10.1007/s11306-018-1327-y .
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How to compare separation selectivity of high-performance liquid chromatographic columns properly?

Andrić, Filip; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2017)

TY  - JOUR
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2017
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/2428
AB  - Comparison and selection of chromatographic columns is an important part of development as well as validation of analytical methods. Presently there is abundant number of methods for selection of the most similar and orthogonal columns, based on the application of limited number of test compounds as well as quantitative structure retention relationship models (QSRR), from among Snyder's hydrophobic subtraction model (HSM) have been most extensively used. Chromatographic data of 67 compounds were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), non-parametric ranking methods as sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM), both applied as a consensus driven comparison, and complemented by the comparison with one variable at a time (COVAT) approach. The aim was to compare the ability of the HSM approach and the approach based on primary retention data of test solutes (logic values) to differentiate among ten highly similar C18 columns. The ranking (clustering) pattern of chromatographic columns based on primary retention data and HSM parameters gave different results in all instances. Patterns based on retention coefficients were in accordance with expectations based on columns' physicochemical parameters, while HSM parameters provided a different clustering. Similarity indices calculated from the following dissimilarity measures: SRD, GPCM Fisher's conditional exact probability weighted (CEPW) scores; Euclidian, Manhattan, Chebyshev, and cosine distances; Pear son's, Spearman's, and Kendall's, correlation coefficients have been ranked by the consensus based SRD. Analysis of variance confirmed that the HSM model produced statistically significant increases of SRD values for the majority of similarity indices, i.e. HS transformation of original retention data yields significant loss of information, and finally results in lower performance of HSM methodology. The best similarity measures were obtained using primary retention data, and derived from Kendal's and Spearman's correlation coefficients, as well as GPCM and SRD score values. Selectivity function, Fs, originally proposed by Snyder, demonstrated moderate performance.
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Chromatography A
T1  - How to compare separation selectivity of high-performance liquid chromatographic columns properly?
VL  - 1488
SP  - 45
EP  - 56
DO  - 10.1016/j.chroma.2017.01.066
ER  - 
@article{
author = "Andrić, Filip and Héberger, Karoly",
year = "2017",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/2428",
abstract = "Comparison and selection of chromatographic columns is an important part of development as well as validation of analytical methods. Presently there is abundant number of methods for selection of the most similar and orthogonal columns, based on the application of limited number of test compounds as well as quantitative structure retention relationship models (QSRR), from among Snyder's hydrophobic subtraction model (HSM) have been most extensively used. Chromatographic data of 67 compounds were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), non-parametric ranking methods as sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM), both applied as a consensus driven comparison, and complemented by the comparison with one variable at a time (COVAT) approach. The aim was to compare the ability of the HSM approach and the approach based on primary retention data of test solutes (logic values) to differentiate among ten highly similar C18 columns. The ranking (clustering) pattern of chromatographic columns based on primary retention data and HSM parameters gave different results in all instances. Patterns based on retention coefficients were in accordance with expectations based on columns' physicochemical parameters, while HSM parameters provided a different clustering. Similarity indices calculated from the following dissimilarity measures: SRD, GPCM Fisher's conditional exact probability weighted (CEPW) scores; Euclidian, Manhattan, Chebyshev, and cosine distances; Pear son's, Spearman's, and Kendall's, correlation coefficients have been ranked by the consensus based SRD. Analysis of variance confirmed that the HSM model produced statistically significant increases of SRD values for the majority of similarity indices, i.e. HS transformation of original retention data yields significant loss of information, and finally results in lower performance of HSM methodology. The best similarity measures were obtained using primary retention data, and derived from Kendal's and Spearman's correlation coefficients, as well as GPCM and SRD score values. Selectivity function, Fs, originally proposed by Snyder, demonstrated moderate performance.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Chromatography A",
title = "How to compare separation selectivity of high-performance liquid chromatographic columns properly?",
volume = "1488",
pages = "45-56",
doi = "10.1016/j.chroma.2017.01.066"
}
Andrić, F.,& Héberger, K. (2017). How to compare separation selectivity of high-performance liquid chromatographic columns properly?.
Journal of Chromatography A
Elsevier Science Bv, Amsterdam., 1488, 45-56.
https://doi.org/10.1016/j.chroma.2017.01.066
Andrić F, Héberger K. How to compare separation selectivity of high-performance liquid chromatographic columns properly?. Journal of Chromatography A. 2017;1488:45-56
Andrić Filip, Héberger Karoly, "How to compare separation selectivity of high-performance liquid chromatographic columns properly?" Journal of Chromatography A, 1488 (2017):45-56,
https://doi.org/10.1016/j.chroma.2017.01.066 .
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11
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Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001

Andrić, Filip; Bajusz, David; Racz, Anita; Šegan, Sandra B.; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2016)

TY  - BOOK
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Racz, Anita
AU  - Šegan, Sandra B.
AU  - Héberger, Karoly
PY  - 2016
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/3577
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001
ER  - 
@book{
author = "Andrić, Filip and Bajusz, David and Racz, Anita and Šegan, Sandra B. and Héberger, Karoly",
year = "2016",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/3577",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001"
}
Andrić, F., Bajusz, D., Racz, A., Šegan, S. B.,& Héberger, K. (2016). Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001.
Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science Bv, Amsterdam..
Andrić F, Bajusz D, Racz A, Šegan SB, Héberger K. Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001. Journal of Pharmaceutical and Biomedical Analysis. 2016;
Andrić Filip, Bajusz David, Racz Anita, Šegan Sandra B., Héberger Karoly, "Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001" Journal of Pharmaceutical and Biomedical Analysis (2016)

Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices

Andrić, Filip; Bajusz, David; Racz, Anita; Šegan, Sandra B.; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2016)

TY  - JOUR
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Racz, Anita
AU  - Šegan, Sandra B.
AU  - Héberger, Karoly
PY  - 2016
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/2270
AB  - Needs for fast, yet reliable means of assessing the lipophilicities of diverse compounds resulted in the development of various in silico and chromatographic approaches that are faster, cheaper, and greener compared to the traditional shake-flask method. However, at present no accepted "standard" approach exists for their comparison and selection of the most appropriate one(s). This is of utmost importance when it comes to the development of new lipophilicity indices, or the assessment of the lipophilicity of newly synthesized compounds. In this study, 50 well-known, diverse compounds of significant pharmaceutical and environmental importance have been selected and examined. Octanol-water partition coefficients have been measured with the shake-flask method for most of them. Their retentions have been studied in typical reversed thin-layer chromatographic systems, involving the most frequently employed stationary phases (octadecyl- and cyano-modified silica), and acetonitrile and methanol as mobile phase constituents. Twelve computationally estimated logP-s and twenty chromatographic indices together with the shake-flask octanol-water partition coefficient have been investigated with classical chemometric approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), Pearson's and Spearman's correlation matrices, as well as novel non-parametric methods: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Novel SRD and GPCM methods have been introduced based on the Comparisons with One VAriable (lipophilicity metric) at a Time (COVAT). For the visualization of COVAT results, a heatmap format was introduced. Analysis of variance (ANOVA) was applied to reveal the dominant factors between computational logPs and various chromatographic measures. In consensus-based comparisons, the shake-flask method performed the best, closely followed by computational estimates, while the chromatographic estimates often overlap with in silico assessments, mostly with methods involving octadecyl-modified silica stationary phases. The ones that employ cyano-modified silica perform generally worse. The introduction of alternative coloring schemes for the covariance matrices and SRD/GPCM heatmaps enables the discovery of intrinsic relationships among lipophilicity scales and the selection of best/worst measures. Closest to the recommended logK(ow) values are ClogP and the first principal component scores obtained on octadecyl-silica stationary phase in combination with methanol-water mobile phase, while the usage of slopes derived from Soczewinski-Matyisik equation should be avoided. (C) 2016 Elsevier B.V. All rights reserved.
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices
VL  - 127
SP  - 81
EP  - 93
DO  - 10.1016/j.jpba.2016.04.001
ER  - 
@article{
author = "Andrić, Filip and Bajusz, David and Racz, Anita and Šegan, Sandra B. and Héberger, Karoly",
year = "2016",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/2270",
abstract = "Needs for fast, yet reliable means of assessing the lipophilicities of diverse compounds resulted in the development of various in silico and chromatographic approaches that are faster, cheaper, and greener compared to the traditional shake-flask method. However, at present no accepted "standard" approach exists for their comparison and selection of the most appropriate one(s). This is of utmost importance when it comes to the development of new lipophilicity indices, or the assessment of the lipophilicity of newly synthesized compounds. In this study, 50 well-known, diverse compounds of significant pharmaceutical and environmental importance have been selected and examined. Octanol-water partition coefficients have been measured with the shake-flask method for most of them. Their retentions have been studied in typical reversed thin-layer chromatographic systems, involving the most frequently employed stationary phases (octadecyl- and cyano-modified silica), and acetonitrile and methanol as mobile phase constituents. Twelve computationally estimated logP-s and twenty chromatographic indices together with the shake-flask octanol-water partition coefficient have been investigated with classical chemometric approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), Pearson's and Spearman's correlation matrices, as well as novel non-parametric methods: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Novel SRD and GPCM methods have been introduced based on the Comparisons with One VAriable (lipophilicity metric) at a Time (COVAT). For the visualization of COVAT results, a heatmap format was introduced. Analysis of variance (ANOVA) was applied to reveal the dominant factors between computational logPs and various chromatographic measures. In consensus-based comparisons, the shake-flask method performed the best, closely followed by computational estimates, while the chromatographic estimates often overlap with in silico assessments, mostly with methods involving octadecyl-modified silica stationary phases. The ones that employ cyano-modified silica perform generally worse. The introduction of alternative coloring schemes for the covariance matrices and SRD/GPCM heatmaps enables the discovery of intrinsic relationships among lipophilicity scales and the selection of best/worst measures. Closest to the recommended logK(ow) values are ClogP and the first principal component scores obtained on octadecyl-silica stationary phase in combination with methanol-water mobile phase, while the usage of slopes derived from Soczewinski-Matyisik equation should be avoided. (C) 2016 Elsevier B.V. All rights reserved.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices",
volume = "127",
pages = "81-93",
doi = "10.1016/j.jpba.2016.04.001"
}
Andrić, F., Bajusz, D., Racz, A., Šegan, S. B.,& Héberger, K. (2016). Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices.
Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science Bv, Amsterdam., 127, 81-93.
https://doi.org/10.1016/j.jpba.2016.04.001
Andrić F, Bajusz D, Racz A, Šegan SB, Héberger K. Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices. Journal of Pharmaceutical and Biomedical Analysis. 2016;127:81-93
Andrić Filip, Bajusz David, Racz Anita, Šegan Sandra B., Héberger Karoly, "Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices" Journal of Pharmaceutical and Biomedical Analysis, 127 (2016):81-93,
https://doi.org/10.1016/j.jpba.2016.04.001 .
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36

Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings

Andrić, Filip; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2015)

TY  - JOUR
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2015
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/1987
AB  - Lipophilicity is one of the most frequently used physicochemical properties that affects compound solubility, determines its passive transport through biological membranes, influences biodistribution, metabolism and pharmacokinetics. We compared, ranked and grouped chromatographic lipophilicity indices and computationally estimated logP-s by sensitive and robust non-parametric approaches: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Chromatographic indices of fourteen neurotoxins and twenty one 1,2,4-triazole compounds have been derived from typical reversed-phase thin-layer chromatography and micellar chromatography. They were compared with in silico estimated logP-s. Under typical reversed-phase conditions, octadecyl-, octyl-, and cyanopropyl-modified silica have clear advantage over ethyl-, aminopropyl, and diol-modified beds, i.e., the preferable choice of the stationary phase follows this order: octadecyl  gt  octyl  gt  cyanopropyl  gt  ethyl  gt  octadecyl wettable  gt  aminopropyl  gt  diol. Many of these indices outperform the majority of computationally estimated logP-s. Clear distinction can be made based on cross-validation and statistical tests. Oppositely, micellar chromatography may not be successfully used for the lipophilicity assessment, since retention parameters obtained from the typical reversed-phase conditions outperform the parameters obtained by micellar chromatography. Both ranking approaches, SRD and GPCM, although based on different background, provide highly similar variable ordering and grouping leading to the same, above mentioned conclusions. However, GPCM results in more degeneracy, i.e., in some cases it cannot distinguish the lipophilicity parameters whereas SRD and its cross-validated version can. On the other hand GPCM produces a more characteristic grouping. Both methods can be successfully used for selection of the most and least appropriate lipophilicity measures.
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings
VL  - 115
SP  - 183
EP  - 191
DO  - 10.1016/j.jpba.2015.07.006
ER  - 
@article{
author = "Andrić, Filip and Héberger, Karoly",
year = "2015",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/1987",
abstract = "Lipophilicity is one of the most frequently used physicochemical properties that affects compound solubility, determines its passive transport through biological membranes, influences biodistribution, metabolism and pharmacokinetics. We compared, ranked and grouped chromatographic lipophilicity indices and computationally estimated logP-s by sensitive and robust non-parametric approaches: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Chromatographic indices of fourteen neurotoxins and twenty one 1,2,4-triazole compounds have been derived from typical reversed-phase thin-layer chromatography and micellar chromatography. They were compared with in silico estimated logP-s. Under typical reversed-phase conditions, octadecyl-, octyl-, and cyanopropyl-modified silica have clear advantage over ethyl-, aminopropyl, and diol-modified beds, i.e., the preferable choice of the stationary phase follows this order: octadecyl  gt  octyl  gt  cyanopropyl  gt  ethyl  gt  octadecyl wettable  gt  aminopropyl  gt  diol. Many of these indices outperform the majority of computationally estimated logP-s. Clear distinction can be made based on cross-validation and statistical tests. Oppositely, micellar chromatography may not be successfully used for the lipophilicity assessment, since retention parameters obtained from the typical reversed-phase conditions outperform the parameters obtained by micellar chromatography. Both ranking approaches, SRD and GPCM, although based on different background, provide highly similar variable ordering and grouping leading to the same, above mentioned conclusions. However, GPCM results in more degeneracy, i.e., in some cases it cannot distinguish the lipophilicity parameters whereas SRD and its cross-validated version can. On the other hand GPCM produces a more characteristic grouping. Both methods can be successfully used for selection of the most and least appropriate lipophilicity measures.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings",
volume = "115",
pages = "183-191",
doi = "10.1016/j.jpba.2015.07.006"
}
Andrić, F.,& Héberger, K. (2015). Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings.
Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science Bv, Amsterdam., 115, 183-191.
https://doi.org/10.1016/j.jpba.2015.07.006
Andrić F, Héberger K. Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings. Journal of Pharmaceutical and Biomedical Analysis. 2015;115:183-191
Andrić Filip, Héberger Karoly, "Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings" Journal of Pharmaceutical and Biomedical Analysis, 115 (2015):183-191,
https://doi.org/10.1016/j.jpba.2015.07.006 .
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Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation

Andrić, Filip; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2015)

TY  - JOUR
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2015
UR  - http://cherry.chem.bg.ac.rs/handle/123456789/1650
AB  - Lipophilicity (logP) represents one of the most studied and most frequently used fundamental physicochemical properties. At present there are several possibilities for its quantitative expression and many of them stems from chromatographic experiments. Numerous attempts have been made to compare different computational methods, chromatographic methods vs. computational approaches, as well as chromatographic methods and direct shake-flask procedure without definite results or these findings are not accepted generally. In the present work numerous chromatographically derived lipophilicity measures in combination with diverse computational methods were ranked and clustered using the novel variable discrimination and ranking approaches based on the sum of ranking differences and the generalized pair correlation method. Available literature logP data measured on HILIC, and classical reversed-phase combining different classes of compounds have been compared with most frequently used multivariate data analysis techniques (principal component and hierarchical cluster analysis) as well as with the conclusions in the original sources. Chromatographic lipophilicity measures obtained under typical reversed-phase conditions outperform the majority of computationally estimated logPs. Oppositely, in the case of HILIC none of the many proposed chromatographic indices overcomes any of the computationally assessed logPs. Only two of them (logk(min) and k(mm)) may be selected as recommended chromatographic lipophilicity measures. Both ranking approaches, sum of ranking differences and generalized pair correlation method, although based on different backgrounds, provides highly similar variable ordering and grouping leading to the same conclusions.
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Chromatography A
T1  - Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation
VL  - 1380
SP  - 130
EP  - 138
DO  - 10.1016/j.chroma.2014.12.073
ER  - 
@article{
author = "Andrić, Filip and Héberger, Karoly",
year = "2015",
url = "http://cherry.chem.bg.ac.rs/handle/123456789/1650",
abstract = "Lipophilicity (logP) represents one of the most studied and most frequently used fundamental physicochemical properties. At present there are several possibilities for its quantitative expression and many of them stems from chromatographic experiments. Numerous attempts have been made to compare different computational methods, chromatographic methods vs. computational approaches, as well as chromatographic methods and direct shake-flask procedure without definite results or these findings are not accepted generally. In the present work numerous chromatographically derived lipophilicity measures in combination with diverse computational methods were ranked and clustered using the novel variable discrimination and ranking approaches based on the sum of ranking differences and the generalized pair correlation method. Available literature logP data measured on HILIC, and classical reversed-phase combining different classes of compounds have been compared with most frequently used multivariate data analysis techniques (principal component and hierarchical cluster analysis) as well as with the conclusions in the original sources. Chromatographic lipophilicity measures obtained under typical reversed-phase conditions outperform the majority of computationally estimated logPs. Oppositely, in the case of HILIC none of the many proposed chromatographic indices overcomes any of the computationally assessed logPs. Only two of them (logk(min) and k(mm)) may be selected as recommended chromatographic lipophilicity measures. Both ranking approaches, sum of ranking differences and generalized pair correlation method, although based on different backgrounds, provides highly similar variable ordering and grouping leading to the same conclusions.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Chromatography A",
title = "Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation",
volume = "1380",
pages = "130-138",
doi = "10.1016/j.chroma.2014.12.073"
}
Andrić, F.,& Héberger, K. (2015). Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation.
Journal of Chromatography A
Elsevier Science Bv, Amsterdam., 1380, 130-138.
https://doi.org/10.1016/j.chroma.2014.12.073
Andrić F, Héberger K. Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation. Journal of Chromatography A. 2015;1380:130-138
Andrić Filip, Héberger Karoly, "Chromatographic and computational assessment of lipophilicity using sum of ranking differences and generalized pair-correlation" Journal of Chromatography A, 1380 (2015):130-138,
https://doi.org/10.1016/j.chroma.2014.12.073 .
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