Skip to main content
SpringerLink
Log in
Book cover

Recent Advances in QSAR Studies pp 223–259Cite as

QSAR in Chromatography: Quantitative Structure–Retention Relationships (QSRRs)

  • Chapter
  • First Online:

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 8))

Abstract

To predict a given physicochemical or biological property, the relationships can be identified between the chemical structure and the desired property. Ideally these relationships should be described in reliable quantitative terms. To obtain statistically significant relationships, one needs relatively large series of property parameters. Chromatography is a unique method which can provide a great amount of quantitatively precise, reproducible, and comparable retention data for large sets of structurally diversified compounds (analytes). On the other hand, chemometrics is recognized as a valuable tool for accomplishing a variety of tasks in a chromatography laboratory. Chemometrics facilitates the interpretation of large sets of complex chromatographic and structural data. Among various chemometric methods, multiple regression analysis is most often performed to process retention data and to extract chemical information on analytes. And the methodology of quantitative structure–(chromatographic) retention relationships (QSRRs) is mainly based on multiple regression analysis. QSRR can be a valuable source of knowledge on both the nature of analytes and of the macromolecules forming the stationary phases. Therefore, quantitative structure–retention relationships have been considered as a model approach to establish strategy and methods of property predictions.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Kaliszan R (1992) Quantitative structure–retention relationships. Anal Chem 64:619A–631A

    Article  CAS  Google Scholar 

  2. Kaliszan R, Foks H (1977) The relationship between the Rm values and the connectivity indices for pyrazine carbothioamide derivatives. Chromatographia 10:346–349

    Article  CAS  Google Scholar 

  3. Kaliszan R (1977) Correlation between the retention indices and the connectivity indices of alcohols and methyl esters with complex cyclic structure. Chromatographia 10:529–531

    Article  CAS  Google Scholar 

  4. Michotte Z, Massart DL (1977) Molecular connectivity and retention indexes. J Pharm Sci 66:1630–1632

    Article  CAS  Google Scholar 

  5. Kaliszan R (1987) Quantitative structure–chromatographic retention relationships. Wiley, New York

    Google Scholar 

  6. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1043

    Article  CAS  Google Scholar 

  7. Kaliszan R (2000) Recent advances in quantitative structure–retention relationships. In: Valko K (ed) Separation methods in drug synthesis and purification. Elsevier, Amsterdam

    Google Scholar 

  8. Kaliszan R (2007) QSRR: Quantitative structure–(chromatographic) retention relationships. Chem Rev 107:3212–3246.

    Article  CAS  Google Scholar 

  9. Smith RM (1995) Retention and selectivity in liquid chromatography. Elsevier, Amsterdam

    Google Scholar 

  10. Jurs PC (1996) Computer software applications in chemistry. Wiley, New York

    Google Scholar 

  11. Forgacs E, Cserhati T (1997) Molecular bases of chromatographic separations. CRC Press, Boca Raton

    Google Scholar 

  12. Kaliszan R (1997) Structure and retention in chromatography. A chemometric approach. Harwood, Amsterdam

    Google Scholar 

  13. Jinno K (ed) (1997) Chromatographic separations based on molecular recognition. Wiley, New York

    Google Scholar 

  14. Hansch C, Fujita T (1964) ρ-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626

    Article  CAS  Google Scholar 

  15. Albert A (1965) Selective toxicity. Wiley, New York

    Google Scholar 

  16. Seydel JK, Schaper K-J (1979) Chemische struktur und biologische aktivität von wirkstoffen. Verlag Chemie, Weinheim

    Google Scholar 

  17. Iwasa J, Fuijta T, Hansch CJ (1965) Substituent constants for aliphatic functions obtained from partition coefficients. J Med Chem 8:150–153

    Article  CAS  Google Scholar 

  18. Wang QS, Zhang L (1999) Review of research on quantitative structure–retention relationships in thin-layer chromatography. J Liq Chrom Rel Techn 22:1–14

    Article  Google Scholar 

  19. Sadek PC, Carr PW, Doherty RM et al. (1985) Study of retention processes in reversed-phase high-performance liquid chromatography by the use of the solvatochromic comparison method. Anal Chem 57:2971–2978

    Article  CAS  Google Scholar 

  20. Carr PW, Doherty RM, Kamlet MJ et al. (1986) Study of temperature and mobile-phase effects in reversed-phase high-performance liquid chromatography by the use of the solvatochromic comparison method. Anal Chem 58:2674–2680

    Article  CAS  Google Scholar 

  21. Snyder LR, Carr PW, Rutan SC (1993) Solvatochromically based solvent-selectivity triangle. J Chromatogr A 656:537–547

    Article  CAS  Google Scholar 

  22. Hasan MN, Jurs PC (1990) Prediction of gas and liquid chromatographic retention indices of polyhalogenated biphenyls. Anal Chem 62:2318–2323

    Article  CAS  Google Scholar 

  23. Katritzky AR, Karelson M, Lobanov VS (1997) QSPR as a means of predicting and understanding chemical and physical properties in terms of structure. Pure Appl Chem 69:245–248

    Article  CAS  Google Scholar 

  24. Lucic B, Trinajstic N, Sild S et al. (1999) A new efficient approach for variable selection based on multiregression: Prediction of gas chromatographic retention times and response factors. J Chem Inf Comput Sci 39:610–621

    CAS  Google Scholar 

  25. Katritzky AR, Chen K, Maran U et al. (2000) QSPR correlation and predictions of GC retention indexes for methyl-branched hydrocarbons produced by insects. Anal Chem 72:101–109

    Article  CAS  Google Scholar 

  26. Ignatz-Hoover F, Petrukhin R, Karelson M et al. (2001) QSRR correlation of free-radical polymerization chain-transfer constants for styrene. J Chem Inf Comput Sci 41:295–299

    CAS  Google Scholar 

  27. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim

    Book  Google Scholar 

  28. Todeschini R, Consonni V, Pavan M (http://www.disat.unimib.it/chm/dragon.htm) Dragon software version 5.0, 2004

  29. Ivanciuc O, Ivanciuc T, Cabrol-Bass D et al. (2000) Comparison of weighting schemes for molecular graph descriptors: Application in quantitative structure–retention relationship models for alkylphenols in gas-liquid chromatography. J Chem Inf Comput Sci 40:723–743

    Google Scholar 

  30. Junkes BS, Amboni RDMC, Yunes RA et al. (2003) Prediction of the chromatographic retention of saturated alcohols on stationary phases of different polarity applying the novel semi-empirical topological index. Anal Chim Acta 477:29–39

    Article  CAS  Google Scholar 

  31. Ośmiałowski K, Halkiewicz J, Kaliszan R (1986) Quantum chemical parameters in correlation analysis of gas–liquid chromatographic retention indices of amines. J Chromatogr 361:63–69

    Article  Google Scholar 

  32. Randić M (1975) On characterization of molecular branching. J Am Chem Soc 97:6609–6615

    Article  Google Scholar 

  33. Hanai T (2005) Chromatography in silico, basic concept in reversed-phase liquid chromatography. Anal Bioanal Chem 382:708–717

    Article  CAS  Google Scholar 

  34. Salo M, Sirén H, Volin P et al. (1996) Structure–retention relationships of steroid hormones in reversed-phase liquid chromatography and micellar electrokinetic capillary chromatography. J Chromatogr A 728:83–88

    Article  CAS  Google Scholar 

  35. Makino M (1999) An analysis of the chromatographic behavior of the environmental contaminants polychlorinated naphthalenes using electrical and topological descriptors. Toxicol Environ Chem 73:117–128

    Article  CAS  Google Scholar 

  36. Dimov N, Stoev S (1999) A new approach to structure–retention relationships. Acta Chromatogr 9:17–18

    Google Scholar 

  37. Zhang L, Zhang M, Tang GZ et al. (2000) Retention prediction system of O-aryl,O-(1-methylthioethylidene-amino)phosphates on RP-HPLC. HRC J High Resol Chromatogr 23:445–448

    Article  CAS  Google Scholar 

  38. Ledesma EB, Wornat MJ (2000) QSRR prediction of chromatographic retention of ethynyl-substituted PAH from semiempirically computed solute descriptors. Anal Chem 72:5437–5443

    Article  CAS  Google Scholar 

  39. Kaliszan R, Ośmiałowski K, Tomellini SA et al. (1986) Quantitative retention relationships as a function of mobile and C18 stationary phase composition for non-cogeneric solutes. J Chromatogr 352:141–155

    Article  CAS  Google Scholar 

  40. Kaliszan R, Lamparczyk H, Radecki A (1979) A relationship between regression of dimethylnitrosamine-demethylase by polycyclic aromatic hydrocarbons and their shape. Biochem Pharmacol 28:123–125

    Article  CAS  Google Scholar 

  41. Wise SA, Bonnett WJ, Guenther FR et al. (1981) A relationship between reversed-phase C18 liquid chromatographic retention and the shape of polycyclic aromatic hydrocarbons. J Chromatogr Sci 19:457–465

    CAS  Google Scholar 

  42. Lippa KA, Sander LC, Mountain RD (2005) Molecular dynamics simulations of alkylsilane stationary-phase order and disorder. 1. Effects of surface coverage and bonding chemistry. Anal Chem 77:7852–7861

    Article  CAS  Google Scholar 

  43. Montaña MP, Pappao B, Debattista NB et al. (2000) High-performance liquid chromatography of chalcones: Quantitative structure–retention relationships using partial least-squares (PLS) modeling. Chromatographia 51:727–735

    Article  Google Scholar 

  44. Silva MF, Chipre LF, Raba J et al. (2001) Amino acids characterization by reversed-phase liquid chromatography. Partial least-squares modeling of their transport properties. Chromatographia 53:392–400

    Article  CAS  Google Scholar 

  45. Csiktusnádi-Kiss GA, Forgács E, Markuszewski M (1998) Application of multivariate mathematical–statistical methods to compare reversed-phase thin-layer and liquid chromatographic behaviour of tetrazolium salts in Quantitative Structure–Retention Relationships (QSRR) studies. Analusis 26:400–406

    Article  Google Scholar 

  46. Åberg MK, Jacobsson SP (2001) Pre-processing of three-way data by pulse-coupled neural networks – An imaging approach. Chemom Intell Lab Sys 57:25–36

    Article  Google Scholar 

  47. Tham SY, Agatonovic-Kustrin S (2000) Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives. J Pharm Biomed Anal 28:581–590

    Article  Google Scholar 

  48. Ruggieri F, D’Archivio AA, Carlucci G et al. (2005) Application of artificial neural networks for prediction of retention factors of triazine herbicides in reversed-phase liquid chromatography. J Chromatogr A 1076:163–169

    Article  CAS  Google Scholar 

  49. Bączek T, Kaliszan R (2002) Combination of linear solvent strength model and quantitative structure–retention relationships as a comprehensive procedure of approximate prediction of retention in gradient liquid chromatography. J Chromatogr A 962:41–55

    Article  Google Scholar 

  50. Kaliszan R, Bączek T, Buciński A et al. (2003) Prediction of gradient retention from the linear solvent strength (LSS) model, quantitative structure–retention relationships (QSRR), and artificial neural networks (ANN). J Sep Sci 26:271–282

    Article  CAS  Google Scholar 

  51. Snyder R, Dolan JW (1998) The linear-solvent-strength model of gradient elution. Adv Chromatogr 38:115–185

    CAS  Google Scholar 

  52. Haber P, Bączek, T, Kaliszan R et al. (2000) Computer simulation for the simultaneous optimization of any two variables and any chromatographic procedure. J Chromatogr Sci 38:386–392

    CAS  Google Scholar 

  53. Al-Haj MA, Kaliszan R, Nasal A (1999) Test analytes for studies of the molecular mechanism of chromatographic separations by quantitative structure–retention relationships. Anal Chem 71:2976–2985

    Article  CAS  Google Scholar 

  54. Schefzick S, Kibbey C, Bradley MP (2004) Prediction of HPLC conditions using QSPR techniques: An effective tool to improve combinatorial library design. J Comb Chem 6:916–927

    Article  CAS  Google Scholar 

  55. Cramer RD III, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967

    Article  CAS  Google Scholar 

  56. Luo H, Cheng Y-K (2005) Quantitative structure–retention relationship of nucleic-acid bases revisited. CoMFA on purine RPLC retention. QSAR Comb Sci 24:969–975

    Article  CAS  Google Scholar 

  57. Sybyl (Linux) 6.9.1 (2003) Tripos, Inc. St. Louis, MO, USA

    Google Scholar 

  58. Sutter JM, Peterson TA, Jurs PC (1997) Prediction of gas chromatographic retention indices of alkylbenzenes. Anal Chim Acta 342:113–122

    Article  CAS  Google Scholar 

  59. Massart DL, Vandeginste BGM, Buydens LMC et al. (1997) Handbook of chemometrics and qualimetrics: Part A. Elsevier, Amsterdam

    Google Scholar 

  60. Vandeginste BGM, Massart DL, Buydens LMC et al. (1998) Handbook of chemometrics and qualimetrics: Part B. Elsevier, Amsterdam

    Google Scholar 

  61. Ros F, Pintore M, Chrétien JR (2002) Molecular descriptor selection combining genetic algorithms and fuzzy logic: Application to database mining procedures. Chemometr Intell Lab Sys 63:15–26

    Article  CAS  Google Scholar 

  62. Put R, Perrin C, Questier F et al. (2003) Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure–retention relationship studies. J Chromatogr A 988:261–276

    Article  CAS  Google Scholar 

  63. Put R, Xu QS, Massart DL, Vander Heyden Y (2004) Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure–retention relationship studies. J Chromatogr A 1055:11–19

    Article  CAS  Google Scholar 

  64. Abraham MH, Chadha HS, Whiting GS et al. (1994) Hydrogen bonding. 32. An analysis of water–octanol and water–alkane partitioning and the Δlog P parameter of seiler. J Pharm Sci 83:1085–1100

    Article  CAS  Google Scholar 

  65. Abraham MH, Treiner C, Rosés M et al. (1996) Linear free energy relationship analysis of microemulsion electrokinetic chromatographic determination of liophilicity. J Chromatogr A 752:243–249

    Article  CAS  Google Scholar 

  66. Abraham MH, Chadha HS, Leitao RAE et al. (1997) Determination of solute lipophilicity, as log P(octanol) and log P(alkane) using poly(styrene-divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography. J Chromatogr A 766:35–47

    Article  CAS  Google Scholar 

  67. Abraham MH (1997) On characterization of some GLC chiral stationary phases: LFER analysis. Anal Chem 69:613–617

    Article  CAS  Google Scholar 

  68. Abraham MH (2004) The factors that influence permeation across the blood–brain barrier. Eur J Med Chem 39:235–240

    Article  CAS  Google Scholar 

  69. Abraham MH, Poole CF, Poole SK (1999) Classification of stationary phases and other materials by gas chromatography. J Chromatogr A 842:79–114

    Article  CAS  Google Scholar 

  70. Abraham MH, Ibrahim A, Zissimos AM (2004) Determination of sets of solute descriptors from chromatographic measurements. J Chromatogr A 1037:29–47

    Article  CAS  Google Scholar 

  71. Wilson NS, Nelson MD, Dolan JW et al. (2002) Column selectivity in reversed-phase liquid chromatography: I. A general quantitative relationship. J Chromatogr A 961:171–193

    Article  CAS  Google Scholar 

  72. Zhao J, Carr PW (2000) A comparative study of the chromatographic selectivity of polystyrene-coated zirconia and related reversed-phase materials. Anal Chem 72:302–309

    Article  CAS  Google Scholar 

  73. Park JH, Yoon MH, Ryu YK et al. (1998) Characterization of some normal-phase liquid chromatographic stationary phases based on linear solvation energy relationships. J Chromatogr A 796:249–258

    Article  CAS  Google Scholar 

  74. Rosés M, Bolliet D, Poole CF (1998) Comparison of solute descriptors for predicting retention of ionic compounds (phenols) in reversed-phase liquid chromatography using the solvation parameter model. J Chromatogr A 829:29–40

    Article  Google Scholar 

  75. Li J, Cai B (2001) Evaluation of the retention dependence on the physicochemical properties of solutes in reversed-phase liquid chromatographic linear gradient elution based on linear solvation energy relationships. J Chromatogr A 905:35–46

    Article  CAS  Google Scholar 

  76. Li J, Sun J, Cui Z et al. (2006) Quantitative structure–retention relationship studies using immobilized artificial membrane chromatography I: Amended linear solvation energy relationships with the introduction of a molecular electronic factor. J Chromatogr A 1132:174–182

    Article  CAS  Google Scholar 

  77. Vonk EC, Lewandowska K, Claessens HA et al. (2003) Quantitative structure–retention relationships in reversed-phase liquid chromatography using several stationary and mobile phases. J Sep Sci 26:777–792

    Article  CAS  Google Scholar 

  78. Poole CF, Poole SK (2002) Column selectivity from the perspective of the solvation parameter model. J Chromatogr A 965:263–299

    Article  CAS  Google Scholar 

  79. Vitha M, Carr PC (2006) The chemical interpretation and practice of linear solvation energy relationships in chromatography. J Chromatogr A 1126:143–194

    Article  CAS  Google Scholar 

  80. Jackson PT, Schure MR, Weber TP et al. (1997) Intermolecular interactions involved in solute retention on carbon media in reversed-phase high-performance liquid chromatography. Anal Chem 69:416–425

    Article  CAS  Google Scholar 

  81. Sandi A, Bede A, Szepesy I et al. (1997) Characterization of different RP-HPLC columns by a gradient elution technique. Chromatographia 45:206–214

    Article  CAS  Google Scholar 

  82. Tan LC, Carr PW (1998) Study of retention in reversed-phase liquid chromatography using linear solvation energy relationships II. The mobile phase. J Chromatogr A 799:1–19

    Article  CAS  Google Scholar 

  83. Snyder LR, Dolan JW, Carr PW (2004) The hydrophobic-subtraction model of reversed-phase column selectivity. J Chromatogr A 1060:77–116

    CAS  Google Scholar 

  84. Bączek T, Kaliszan R, Novotná K et al. (2005) Comparative characteristics of HPLC columns based on quantitative structure–retention relationships (QSRR) and hydrophobic-subtraction model. J Chromatogr A 1075:109–115

    Article  CAS  Google Scholar 

  85. Kaliszan R, van Straten MA, Markuszewski M et al. (1999) Molecular mechanism of retention in reversed-phase high-performance liquid chromatography and classification of modern stationary phases by using quantitative structure–retention relationships. J Chromatogr A 855:455–486

    Article  CAS  Google Scholar 

  86. Al-Haj MA, Kaliszan R, Buszewski B (2001) Quantitative structure–retention relationships with model analytes as a means of an objective evaluation of chromatographic columns. J Chromatogr Sci 39:29–38

    CAS  Google Scholar 

  87. Bączek T, Kaliszan R (2003) Predictive approaches to gradient retention based on analyte structural descriptors from calculation chemistry. J Chromatogr A 987:29–37

    Article  Google Scholar 

  88. Kaliszan R, Wiczling P, Markuszewski MJ (2004) pH gradient reversed-phase HPLC. Anal Chem 76:749–760

    Article  CAS  Google Scholar 

  89. Meek JL (1980) Prediction of peptide retention times in high-pressure liquid chromatography on the basis of amino acid composition. Proc Natl Acad Sci USA 77:1632–1636

    Article  CAS  Google Scholar 

  90. Palmblad M, Ramström M, Markides KE et al. (2002) Prediction of chromatographic retention and protein identification in liquid chromatography/mass spectrometry. Anal Chem 74:5826–5830

    Article  CAS  Google Scholar 

  91. Petritis K, Kangas LJ, Ferguson PL et al. (2003) Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses. Anal Chem 75:1039–1048

    Article  CAS  Google Scholar 

  92. Kaliszan R, Bączek T, Cimochowska A et al. (2005) Prediction of high-performance liquid chromatography retention of peptides with the use of quantitative structure–retention relationships. Proteomics 5:409–415

    Article  CAS  Google Scholar 

  93. Bączek T, Wiczling P, Marszałł M et al. (2005) Prediction of peptide retention at different HPLC conditions from multiple linear regression models. J Proteome Res 4:555–563

    Article  CAS  Google Scholar 

  94. Bączek T (2006) Chemometric evaluation of relationships between retention and physicochemical parameters in terms of multidimensional liquid chromatography of peptides. J Sep Sci 29:547–554

    Article  CAS  Google Scholar 

  95. Bączek T (2005) Improvement of peptides identification in proteomics with the use of new analytical and bioinformatic strategies. Curr Pharm Anal 1:31–40

    Article  Google Scholar 

  96. Put R, Daszykowski M, Bączek T et al. (2006) Retention prediction of peptides based on uninformative variable elimination by partial least squares. J Proteome Res 5:1618–1625

    Article  CAS  Google Scholar 

  97. Mazza CB, Sukumar N, Breneman CM et al. (2001) Prediction of protein retention in ion-exchange systems using molecular descriptors obtained from crystal structure. Anal Chem 73:5457–5461

    Article  CAS  Google Scholar 

  98. Ladiwala A, Rege K, Breneman CM et al. (2003) Investigation of mobile phase salt type effects on protein retention and selectivity in cation-exchange systems using quantitative structure–retention relationship models. Langmuir 19:8443–8454

    Article  CAS  Google Scholar 

  99. Tugcu N, Song M, Breneman CM et al. (2003) Prediction of the effect of mobile-phase salt type on protein retention and selectivity in anion-exchange systems. Anal Chem 75:3563–3572

    Article  CAS  Google Scholar 

  100. Ladiwala A, Xia F, Luo Q et al. (2006) Investigation of protein retention and selectivity in HIC systems using quantitative structure–retention relationship models. Biotechnol Bioeng 93:836–850

    Article  CAS  Google Scholar 

  101. Chen J, Luo Q, Breneman CM et al. (2007) Classification of protein adsorption and recovery at low salt conditions in hydrophobic interaction chromatographic systems. J Chromatogr A 1139:236–246

    Article  CAS  Google Scholar 

  102. Song MH, Breneman CM, Bi JB et al. (2002) Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J Chem Inf Comput Sci 42:1347–1357

    CAS  Google Scholar 

  103. Heberger K (1999) Evaluation of polarity indicators and stationary phases by principal component analysis in gas–liquid chromatography. Chemom Intell Lab Sys 47:41–49

    Article  CAS  Google Scholar 

  104. Markuszewski M, Krass JD, Hippe T et al. (1998) Separation of nitroaromatics and their transformation products in soil around ammunition plants: New high performance liquid chromatographic charge transfer stationary phases. Chemosphere 37:559–575

    Article  CAS  Google Scholar 

  105. Jiskra J, Claessens HA, Cramers CA et al. (2002) Quantitative structure–retention relationships in comparative studies of behavior of stationary phases under high-performance liquid chromatography and capillary electrochromatography conditions. J Chromatogr A 977:193–206

    Article  CAS  Google Scholar 

  106. Cserháti T, Forgacs E, Payer K et al. (1998) Quantitative structure–retention relationships in separation mechanism studies on polyethylene-coated silica and alumina stationary phases. LCGC Int 4:240–252

    Google Scholar 

  107. Abraham MH, Rosés M, Poole CF et al. (1997) Hydrogen bonding. 42. Characterization of reversed-phase high-performance liquid chromatographic C18 stationary phases. J Phys Org Chem 10:358–368

    Article  CAS  Google Scholar 

  108. Sandi A, Szepesy L (1998) Characterization of various reversed-phase columns using the linear free energy relationship. I. Evaluation based on retention factors. J Chromatogr A 818:1–17

    Article  CAS  Google Scholar 

  109. Valko K, Plass M, Bevan C et al. (1998) Relationships between the chromatographic hydrophobicity indices and solute descriptors obtained by using several reversed-phase, diol, nitrile, cyclodextrin and immobilised artificial membrane-bonded high-performance liquid chromatography columns. J Chromatogr A 797:41–55

    Article  CAS  Google Scholar 

  110. Baranowska I, Zydroń, M (2002) Quantitative structure–retention relationships of xanthines in RP HPLC systems with the new Chromolith RP-18e stationary phases. Anal Bioanal Chem 373:889–892

    Article  CAS  Google Scholar 

  111. Topliss JG, Edwards RP (1979) Chance factors in studies of quantitative structure–activity relationships. J Med Chem 22:1238–1244

    Article  CAS  Google Scholar 

  112. Jiskra J, Claessens HA, Cramers CA et al. (2002) Quantitative structure–retention relationships in comparative studies of behavior of stationary phases under high-performance liquid chromatography and capillary electrochromatography conditions. J Chromatogr A 977:193–206

    Article  CAS  Google Scholar 

  113. Rohrschneider L (1999) Prototypical test substances for a reversed phase in a retention parameter model. HRC J High Resol Chromatogr 22:454–458

    Article  CAS  Google Scholar 

  114. Kaliszan R (2000) Quantitative structure–retention relationships (QSRR) in chromatography. In: Wilson ID, Adlard ER, Cooke M (eds) et al. Encyclopedia of separation science, vol 9. Academic Press, San Diego

    Google Scholar 

  115. Pliška V, Testa B, van de Waaterbemd H (1996) Lipophilicity in drug action and toxicology. VCH, Weinheim

    Book  Google Scholar 

  116. Berthelot M, Jungfleisch E (1872) Sur les lois qui pre´ sident au partage d’un corps entre deux dissolvants (expe´ riences). Ann Chim Phys 26:396–407

    Google Scholar 

  117. Martin AJP (1941) A new form of chromatogram employing two liquid phases. Biochem J 35:1358–1368

    CAS  Google Scholar 

  118. Cserháti T, Valko K (1994) Chromatographic determination of molecular interactions. Applications in biochemistry, chemistry and biophysics. CRC Press, Boca Raton

    Google Scholar 

  119. Poole SK, Poole CF (2003) Separation methods for estimating octanol–water partition coefficients. J Chromatogr B 797:3–19

    Article  CAS  Google Scholar 

  120. Wang QS, Zhang L, Yang HZ (1999) Lipophilicity determination of some potential photosystem ii inhibitors on reversed-phase high-performance thin-layer chromatography. J Chromatogr Sci 37:41–44

    CAS  Google Scholar 

  121. Nasal A, Siluk D, Kaliszan R (2003) Chromatographic retention parameters in medicinal chemistry and molecular pharmacology. Curr Med Chem 10:381–426

    CAS  Google Scholar 

  122. Kaliszan R, Nasal A, Markuszewski MJ (2003) New approaches to chromatographic determination of lipophilicity of xenobiotics. Anal Bioanal Chem 377:803–811

    Article  CAS  Google Scholar 

  123. Nasal, A, Kaliszan R (2006) Progress in the use of HPLC for evaluation of lipophilicity. Curr Comp-Aided Drug Design 2:327–340

    Article  CAS  Google Scholar 

  124. Bączek T, Markuszewski M, Kaliszan R, van Straten, MA, Claessens HA (2000) Linear and quadratic relationships between retention and organic modifier content in eluent in reversed phase high-performance liquid chromatography: A systematic comparative statistical study. HRC J High Resol Chromatogr 23:667–676

    Article  Google Scholar 

  125. Poole CF, Gunatilleka AD, Poole SK (2000) In search of a chromatographic model for biopartitioning. Adv Chromatogr 40:159–230

    CAS  Google Scholar 

  126. Kaliszan R (1986) Quantitative relationships between molecular structure and chromatographic retention. Implications in physical, analytical, and medicinal chemistry. CRC Crit Rev Anal Chem 16:323–383

    CAS  Google Scholar 

  127. Braumann T (1986) Determination of hydrophobic parameters by reversed-phase liquid chromatography: Theory, experimental techniques, and application in studies on quantitative structure–activity relationships. J Chromatogr 373:191–225

    Article  CAS  Google Scholar 

  128. Kaliszan R, Haber P, Bączek T et al. (2001) Gradient HPLC in the determination of drug lipophilicity and acidity. Pure Appl Chem 73:1465–1475

    Article  CAS  Google Scholar 

  129. Kaliszan R, Haber P, Bączek T et al. (2002) Lipophilicity and pKa estimates from gradient high-performance liquid chromatography. J Chromatogr A 965:117–127

    Article  CAS  Google Scholar 

  130. Wiczling P, Markuszewski MJ, Kaliszan R (2004) Determination of pKa by pH gradient reversed-phase HPLC. Anal Chem 76:3069–3077

    Article  CAS  Google Scholar 

  131. Wiczling P, Markuszewski MJ, Kaliszan M et al. (2005) pH/organic solvent double-gradient reversed-phase HPLC. Anal Chem 77:449–458

    Article  CAS  Google Scholar 

  132. Kaliszan R, Wiczling P (2005) Theoretical opportunities and actual limitations of pH gradient HPLC. Anal Bioanal Chem 382:718–727

    Article  CAS  Google Scholar 

  133. Wiczling P, Kawczak P, Nasal A et al. (2006) Simultaneous determination of pKa and lipophilicity by gradient RP HPLC. Anal Chem 78:239–249

    Article  CAS  Google Scholar 

  134. Subirats X, Bosch E, Rosés M (2007) Retention of ionisable compounds on high-performance liquid chromatography XVII. Estimation of the pH variation of aqueous buffers with the change of the methanol fraction of the mobile phase. J Chromatogr A 1138:203–215

    Article  CAS  Google Scholar 

  135. Novotny L, Abdel-Hamid M, Hamza H (2000) Inosine and 2'-deoxyinosine and their synthetic analogues: Lipophilicity in the relation to their retention in reversed-phase liquid chromatography and the stability characteristics. J Pharm Biomed Anal 24:125–132

    Article  CAS  Google Scholar 

  136. Dai J, Jin S, Yao S, Wang LS (2001) Prediction of partition coefficient and toxicity for benzaldehyde compounds by their capacity factors and various molecular descriptors. Chemosphere 42:899–907

    Article  CAS  Google Scholar 

  137. Donovan SF, Pescatore MC (2002) Method for measuring the logarithm of the octanol–water partition coefficient by using short octadecyl-poly(vinyl alcohol) high-performance liquid chromatography columns. J Chromatogr A 952:47–61

    Article  CAS  Google Scholar 

  138. Gulyaeva N, Zaslavsky A, Lechner P et al. (2003) Relative hydrophobicity and lipophilicity of drugs measured by aqueous two-phase partitioning, octanol-buffer partitioning and HPLC. A simple model for predicting blood–brain distribution. Eur J Med Chem 38:391–396

    Article  CAS  Google Scholar 

  139. Welerowicz T, Buszewski B (2005) The effect of stationary phase on lipophilicity determination of β-blockers using reverse-phase chromatographic systems. Biomed Chromatogr 19:725–736

    Article  CAS  Google Scholar 

  140. Vervoort RJM, Debets AJJ, Claessens HA et al. (2000) Optimisation and characterisation of silica-based reversed-phase liquid chromatographic systems for the analysis of basic pharmaceuticals. J Chromatogr A 897:1–22

    Article  CAS  Google Scholar 

  141. Buszewski B Gadzała-Kopciuch RM Kaliszan R et al. (1998) Polyfunctional chemically bonded stationary phase for reversed phase high-performance liquid chromatography. Chromatographia 48:615–622

    Article  CAS  Google Scholar 

  142. Lombardo F, Shalaeva MY, Tupper KA et al. (2001) ElogDoct: A tool for lipophilicity determination in drug discovery. 2. Basic and neutral compounds. J Med Chem 44:2490–2497

    Article  CAS  Google Scholar 

  143. Kaliszan R, Marszałł MP, Markuszewski MJ et al. (2004) Suppression of deleterious effects of free silanols in liquid chromatography by imidazolium tetrafluoroborate ionic liquids. J Chromatogr A 1030:263–271

    Article  CAS  Google Scholar 

  144. Marszałł MP, Bączek T, Kaliszan R (2006) Evaluation of the silanol-suppressing potency of ionic liquids. J Sep Sci 29:1138–1145

    Article  CAS  Google Scholar 

  145. Pidgeon C, Venkataram UV (1989) Immobilized artificial membrane chromatography: Supports composed of membrane lipids. Anal Biochem 176:36–47

    Article  CAS  Google Scholar 

  146. Thurnhofer H, Schnabel J, Betz M et al. (1991) Cholesterol-transfer protein located in the intestinal brush-border membrane. Partial purification and characterization. Biochim Biophys Acta 1064:275–286

    Article  CAS  Google Scholar 

  147. Kaliszan R, Kaliszan A, Wainer IW (1993) Deactivated hydrocarbonaceous silica and immobilized artificial membrane stationary phases in high-performance liquid chromatographic determination of hydrophobicities of organic bases: Relationship to log P and CLOGP. J Pharm Biomed Anal 11:505–511

    Article  CAS  Google Scholar 

  148. Ducarme A, Neuwels M, Goldstein S et al. (1998) IAM retention and blood–brain barrier penetration. Eur J Med Chem 33:215–223

    Article  CAS  Google Scholar 

  149. Kępczyńska E, Bojarski J, Haber P et al. (2000) Retention of barbituric acid derivatives on immobilized artificial membrane stationary phase and its correlation with biological activity. Biomed Chromatogr 14:256–260

    Article  Google Scholar 

  150. Vrakas D, Hadjipavlou-Litina D, Tsantili-Kakoulidou A (2005) Retention of substituted coumarins using immobilized artificial membrane (IAM) chromatography: A comparative study with n-octanol partitioning and reversed-phase HPLC and TLC. J Pharm Biomed Anal 39:908–913

    Article  CAS  Google Scholar 

  151. Kaliszan R (1998) Retention data from affinity high-performance liquid chromatography in view of chemometrics. J Chromatogr B 715:229–244

    Article  CAS  Google Scholar 

  152. Bertucci C, Bartolini M, Gotti R et al. (2003) Drug affinity to immobilized target bio-polymers by high-performance liquid chromatography and capillary electrophoresis. J Chromatogr B 797:111–129

    Article  CAS  Google Scholar 

  153. Domenici E, Bertucci C, Salvadori P et al. (1990) Synthesis and chromatographic properties of an HPLC chiral stationary phase based upon human serum albumin. Chromatographia 29:170–176

    Article  CAS  Google Scholar 

  154. Domenici E, Bertucci C, Salvadori P et al. (1991) Use of a human serum albumin-based high-performance liquid chromatography chiral stationary phase for the investigation of protein binding: Detection of the allosteric interaction between warfarin and benzodiazepine binding sites. J Pharm Sci 80:164–166

    Article  CAS  Google Scholar 

  155. Noctor TAG, Pham CD, Kaliszan R et al. (1992) Stereochemical aspects of benzodiazepine binding to human serum albumin. I. Enantioselective high performance liquid affinity chromatographic examination of chiral and achiral binding interactions between 1,4-benzodiazepines and human serum albumin. Mol Pharmacol 42:506–511

    CAS  Google Scholar 

  156. Kaliszan R, Nasal A, Turowski M (1995) Binding site for basic drugs on α1-acid glycoprotein as revealed by chemometric analysis of biochromatographic data. Biomed Chromatogr 9:211–215

    Article  CAS  Google Scholar 

  157. Kaliszan R, Nasal A, Turowski M (1996) Quantitative structure–retention relationships in the examination of the topography of the binding site of antihistamine drugs on α1-acid glycoprotein. J Chromatogr A 722:25–32

    Article  CAS  Google Scholar 

  158. Goolkasian DL, Slaughter RL, Edwards DJ et al. (1983) Displacement of lidocaine from serum α1-acid glycoprotein binding sites by basic drugs. Eur J Clin Pharmacol 25:413–417

    Article  CAS  Google Scholar 

Download references

Acknowledgement

The work was supported by the Polish State Committee for Scientific Research Project N N405 1040 33.

Author information

Authors and Affiliations

  1. Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80416, Gdańsk, Poland

    Roman Kaliszan & Tomasz Bączek

Authors
  1. Roman Kaliszan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomasz Bączek
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. Chemistry, University of Gdansk, ul. Jana Sobieskiego 18, Gdansk, 80-952, Poland

    Tomasz Puzyn

  2. Dept. Chemistry, Jackson State University, J. R. Lynch St. 1325, Jackson, 39217, U.S.A.

    Jerzy Leszczynski

  3. Dept. Chemistry, John Moores University, Byrom St., Liverpool, L3 3AF, United Kingdom

    Mark T. Cronin

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Kaliszan, R., Bączek, T. (2010). QSAR in Chromatography: Quantitative Structure–Retention Relationships (QSRRs). In: Puzyn, T., Leszczynski, J., Cronin, M. (eds) Recent Advances in QSAR Studies. Challenges and Advances in Computational Chemistry and Physics, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9783-6_8

Download citation

Publish with us

Policies and ethics

Search

Navigation