QSRR models for potential local anaesthetic drugs using high performance liquid chromatography

Journal of Pharmaceutical and Biomedical Analysis

Volumn 59, Issue 1, 2012, Pages 209-216

  Durcekova, Tatiana ^a   Boronova, Katarina ^a   Mocak, Jan ^a   Lehotay, Jozef ^a,b   Cizmarik, Jozef ^c  

^a UNIVERSITY OF SS CYRIL AND METHODIUS   (Slovakia)

^b SLOVAK UNIVERSITY OF TECHNOLOGY   (Slovakia)

^c Physics and Informatics   (Slovakia)

Author keywords

ANN; HPLC retention factor; Local anaesthetics; QSRR

Indexed keywords

ACETONITRILE; ANESTHETIC AGENT; CARBAMIC ACID ESTER; METHANOL;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CARBON NUCLEAR MAGNETIC RESONANCE; CLUSTER ANALYSIS; CONTROLLED STUDY; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; LOCAL ANESTHESIA; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE;

ANESTHETICS, LOCAL; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; CLUSTER ANALYSIS; LINEAR MODELS; MODELS, CHEMICAL; MOLECULAR STRUCTURE; NEURAL NETWORKS (COMPUTER); PHENYLCARBAMATES; PREDICTIVE VALUE OF TESTS; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 82255192343     PISSN: 07317085     EISSN: 1873264X     Source Type: Journal    
DOI: 10.1016/j.jpba.2011.09.035     Document Type: Article

References (41)

1. Grover M., Singh B., Bakshi M., Singh S. Quantitative structure-property relationships in pharmaceutical research - part 2. Pharm. Sci. Technol. Today 2000, 3:50-57.
2. Bodzioch K., Durand A., Kaliszan R., Baczek T., Vander Heyden Y. Advanced QSRR modelling of peptides behaviour in RPLC. Talanta 2010, 81:1711-1718.
3. Héberger K. Quantitative structure-(chromatographic) retention relationships. J. Chromatogr. A 2007, 1158:273-305.
4. Nemeček P., Mocak J., Lehotay J., Waisser K. Prediction of HPLC retention factor of potential antituberculotics by QSRR. J. Liq. Chromatogr. 2011, 36:168-181.
5. Canós-Rius N., Martín-Biosca Y., Sagrado S., Villanueva-Camañas R.M., Medina-Hernández M.J. Estimation of the effect of the acidosis and alkalosis on the anesthetic potency of local anesthetics by biopartitioning micellar chromatography and micellar electrokinetic chromatography. Eur. J. Med. Chem. 2005, 40:215-223.
6. Bodzioch K., Baczek T., Kaliszan R., Vander Heyden Y. The molecular descriptor logSumAA and its alternatives in QSRR models to predict the retention of peptides. J. Pharm. Biomed. Anal. 2009, 50:563-569.
7. Carlucci G., D'Archivio A.A., Maggi M.A., 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.
8. Detroyer A., Vander Heyden Y., Carda-Broch S., Garcia-Alvarez-Coque M.C., Massart D.L. Quantitative structure-retention and retention-activity relationships of β-blocking agents by micellar liquid chromatography. J. Chromatogr. A 2001, 912:211-221.
9. Kaliszan R. Retention data from affinity high-performance liquid chromatography in view of chemometrics. J. Chromatogr. B 1998, 715:229-244.
10. Put R., Vander Heyden Y. Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships. Anal. Chim. Acta 2007, 602:164-172.
11. Borges de Melo E., Martins J.P.A., Jorge T.C.M., Friozi M.C., Ferreira M.M.C. Multivariate QSAR study on the antimutagenic activity of flavonoids against 3-NFA on Salmonella typhimurium TA98. Eur. J. Med. Chem. 2010, 45:4562-4569.
12. Qin L.-T., Liu S.-S., Liu H.-L., Tong J. Comparative multiple quantitative structure-retention relationships modelling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques. J. Chromatogr. A 2009, 1216:5302-5312.
13. Hancock T., Put R., Coomans D., Vander Heyden Y., Everingham Y. A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemometr. Intell. Lab. Syst. 2005, 76:185-196.
14. Mendyk A., Kleinebudde P., Thommes M., Yoo A., Szlek J., Jachowicz R. Analysis of pellet properties with use of artificial neural networks. Eur. J. Pharm. Sci. 2010, 41:421-429.
15. Fernández M., Caballero J., Tundidor-Camba A. Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors. Bioorg. Med. Chem. 2006, 14:4137-4150.
16. Tantishaiyakul V. Prediction of the aqueous solubility of benzylamine salts using QSPR model. J. Pharm. Biomed. Anal. 2005, 37:411-415.
17. Weekes D., Fogel G.B. Evolutionary optimization, backpropagation and data preparation issues in QSAR modeling of HIV inhibition by HEPT derivatives. BioSystems 2003, 72:149-158.
18. Dallos A., Ngo H.S., Kresz R., Héberger K. Cluster and principal component analysis for Kováts' retention indices on apolar and polar stationary phases in gas chromatography. J. Chromatogr. A 2008, 1177:175-182.
19. Kánya Z., Forgács E., Cserháti T., Illé Z. Reducing dimensionality in principal component analysis - a method comparison. Chromatographia 2006, 63:129-134.
20. Chen Y., Chen D., He C., Hu S. Quantitative structure-activity relationships study of herbicides using neural networks and different statistical methods. Chemom. Intell. Lab. Syst. 1999, 45:267-276.
21. Körtvélyesi T., Görgényi M., Héberger K. Correlation between retention indices and quantum-chemical descriptors of ketones and aldehydes on stationary phases of different polarity. Anal. Chim. Acta 2001, 428:73-82.
22. Olivero J., Kannan K. Quantitative structure-retention relationships of polychlorinated naphthalenes in gas chromatography. J. Chromatogr. A 1999, 849:621-627.
23. Búčiová Ľ., Borovanský A., Čižmárik J., Csöllei J., Švec P., Kozlovský J., Račanská E., Beneš L. Study of the effect of modification in the connective chain on the biological activity in a group of basic phenylcarbamates (in Slovak). Ceska Slov. Farm. 1987, 36:339-344.
24. Búčiová Ľ., Csöllei J., Borovanský A., Čižmárik J., Račanská E. Preparation and effectiveness of 1-etoxymetyl-2-(1-pyrolidinyl), 2-piperidino- and 2-(1-perhydroazepinyl)-etylesters of o- and m-alkoxyphenylcarbamic acid (in Slovak). Ceska Slov. Farm. 1991, 40:102-105.
25. VCCLAB, Virtual Computational Chemistry Laboratory, 2005, http://www.vcclab.org/.
26. Mannhold R., Poda G.I., Ostermann C., Tetko I.V. Calculation of molecular lipophilicity: state-of-the-art and comparison of logP methods on more than 96,000 compounds. J. Pharm. Sci. 2009, 98:861-893.
27. Tetko I.V., Gasteiger J., Todeschini R., Mauri A., Livingstone D., Ertl P., Palyulin V.A., Radchenko E.V., Zefirov N.S., Makarenko A.S., Tanchuk V.Y., Prokopenko V.V. Virtual computational chemistry laboratory - design and description. J. Comput. Aid. Mol. Des. 2005, 19:453-463.
28. ACDLabs, Advanced Chemistry Development, Inc., 2010, http://www.acdlabs.com/products/adh/.
29. Vandeginste B., Massart D.L., Buydens L., De Jong S., Lewi P., Smeyers-Verbeke J. Handbook of Chemometrics and Qualimetrics, Part B 1998, Elsevier, Amsterdam.
30. Jolliffe I.T. Principal Component Analysis 2002, Springer-Verlag, New York.
31. Otto M. Chemometrics: Statistics and Computer Application in Analytical Chemistry 1999, Wiley, Weinheim.
32. Massart D.L., Vandeginste B.G.M., Buydens L.M.C., De Jong S., Lewi P.J., Smeyers-Verbeke J. Handbook of Chemometrics and Qualimetrics, Part A 1997, Elsevier, Amsterdam.
33. Daszykowski M. From projection pursuit to other unsupervised chemometric techniques. J. Chemom. 2007, 21:270-279.
34. Daszykowski M., Walczak B., Massart D.L. Density-based clustering for exploration of analytical data. Anal. Bioanal. Chem. 2004, 380:370-372.
35. Bock H.H. Probabilistic models in cluster analysis. Comput. Stat. Data Anal. 1996, 23:5-28.
36. Zupan J., Gasteiger J. Neural Networks for Chemists 1993, Wiley, Weinheim.
37. Zupan J., Gasteiger J. Neural Networks in Chemistry and Drug Design 1999, Wiley, Weinheim.
38. Aitkin M., Foxall R. Statistical modelling of artificial neural networks using the multi-layer perceptron. Stat. Comput. 2003, 13:227-239.
39. StatSoft Inc., Statistica Neural Networks v.8.0 (software), Tulsa, USA, 2009.
40. Patterson D. Artificial Neural Networks 1996, Prentice Hall, Singapore.
41. Todeschini R., Consonni V. Molecular Descriptors for Chemoinformatics 2009, vols. 1-2. Wiley-VCH.