Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions

Authors

  • Slavica Oljacic University of Belgrade-Faculty of Pharmacy, Deparment of Pharmaceutical Chemistry https://orcid.org/0000-0001-9128-6072
  • Mitja Križman Laboratory of Food Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia https://orcid.org/0009-0001-3859-7338
  • Marija Popovic-Nikolic Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia https://orcid.org/0000-0002-8902-3211
  • Irena Vovk Laboratory of Food Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia https://orcid.org/0000-0002-4738-2849
  • Katarina Nikolic Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia https://orcid.org/0000-0002-3656-9245
  • Danica Agbaba Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia https://orcid.org/0000-0001-5907-9823

DOI:

https://doi.org/10.5599/admet.2278

Keywords:

Quantitative structure-retention relationship, quantitative structure-mobility relationship, liquid chromatography, capillary electrophoresis
Graphical Abstract

Abstract

Background and Purpose: The ligands of the imidazoline and α-adrenergic receptors are mainly imidazoline and guanidine derivatives, known as centrally-acting antihypertensives and compounds with potential use in various neurological disorders. The extent of their ionisation has a major influence on their behaviour in the different analytical systems. The main objective of this work was to compare the mechanism of chromatographic retention and electrophoretic mobility under acidic, neutral and basic conditions. Experimental Approach: Multiple Linear Regression and Partial Least Squares Regression were applied for the QSRR (quantitative structure-retention relationship) and QSMR (quantitative structure-mobility relationship) modelling and to select the most important molecular parameters describing the chromatographic and electrophoretic behaviour of the investigated compounds. Key Results: The most important molecular descriptors, such as the chemical composition of the compounds, lipophilicity, polarizability and molecular branching, in the selected QSRR models showed that an important insight into the retention behaviour can be derived from the 0D-, 1D- and 2D-descriptors. The electrophoretic mobility could be explained by 2D- and 3D-descriptors, which provide information on the molecular mass, size and complexity, as well as on the influence of charge transfer and electronic properties on the migration behaviour. Conclusion: All created QSRR/QSMR models met the stringent validation criteria and showed high potential in describing the chromatographic and electrophoretic behaviour of investigated compounds.

Downloads

Download data is not yet available.

References

C. Dardonville, I. Rozas. Imidazoline binding sites and their ligands: an overview of the different chemical structures. Medicinal Research Reviews 24 (2004) 639-661. https://doi.org/10.1002/med.20007

G.A. Head, D.N. Mayorov. Imidazoline receptors, novel agents and therapeutic potential. Hematological Agents in Medicinal Chemistry 4 (2006) 17-32. https://doi.org/10.2174/187152506775268758

P. Bousquet, A. Hudson, J.A. García-Sevilla, J.X. Li. Imidazoline receptor system: the past, the present, and the future. Pharmacological Reviews 72 (2020) 50-79. https://doi.org/10.1124/pr.118.016311

K. Nikolic, D. Agbaba. Imidazoline antihypertensive drugs: Selective I1-Imidazoline receptors activation. Cardiovascular Therapeutics 30 (2012) 209-216. https://doi.org/10.1111/j.1755-5922.2011.00269.x

G.A. Head, S.L. Burke. I1 imidazoline receptors in cardiovascular regulation: the place of rilmenidine. American Journal of Hypertension 13 (2000) 89S-98S. https://doi.org/10.1016/S0895-7061(00)00224-7

J.X. Li, Y. Zhang. Imidazoline I2 receptors: target for new analgesics? European Journal of Pharmacology 658 (2011) 49-56. https://doi.org/10.1016/j.ejphar.2011.02.038

J.H. Li. Imidazoline I2 receptors: An update. Pharmacology & Therapeutics 178 (2017) 48-56. http://dx.doi.org/10.1016/j.pharmthera.2017.03.009

A. Bagán, S. Rodriguez-Arévalo, T. Taboada-Jara, C. Griñán-Ferré, M. Pallàs, I. Brocos-Mosquera, L. F. Callado, J.A. Morales-García, B. Pérez, C. Diaz, R. Fernández-Godino, O. Genilloud, M. Beljkas, S. Oljacic, K. Nikolic, C. Escolano. Preclinical Evaluation of an Imidazole-Linked Heterocycle for Alzheimer’s Disease. Pharmaceutics 215 (2023) 2381. https://doi.org/10.3390/pharmaceutics15102381

N.G. Morgan, S.L. Chan. Imidazoline binding sites in the endocrine pancreas: can they fulfill their potential as targets for the development of new insulin secretagogues? Current Pharmaceutical Design 7 (2001) 1413-1431. https://doi.org/10.2174/1381612013397366

H. van de Waterbeemd, E. Gifford. ADMET in silico modelling: towards prediction paradise? Nature Reviews Drug Discovery 2 (2003) 192-204. https://doir.org/10.1038/nrd1032

K. Vucićević, G. Popović, K. Nikolić, I. Vovk, D. Agbaba. An experimental design approach to selecting the optimum HPLC conditions for the determination of 2-arylimidazoline derivatives. Liquid Chromatography & Related Technologies 32 (2009) 656-667. https://doi.org/10.1080/10826070802711113

S. Filipić, D. Ružić, J. Vucićević, K. Nikolić, D. Agbaba. Quantitative structure-retention relationship of selected imidazoline derivatives on ɑ1-acid glycoprotein column. Journal of Pharmaceutical and Biomedical Analysis 12 (2016) 101-111. http://dx.doi.org/10.1016/j.jpba.2016.02.053

D. Obradović, S. Oljačić, K. Nikolić, D. Agbaba. Investigation and prediction of retention characteristics of imidazoline and serotonin receptor ligands and their related compounds on mixed-mode stationary phase. Journal of Chromatography A 1585 (2019) 92-104. https://doi.org/10.1016/j.chroma.2018.11.051

S. Filipic, K. Nikolic, I. Vovk, M. Krizman, D. Agbaba. Quantitative structure-mobility relationship analysis of imidazoline receptor ligands in CDs-mediated CE. Electrophoresis 34 (2013) 471-482. https://doi.org/10.1002/elps.201200171

R. Kaliszan. QSRR: Quantitative Structure-(Chromatographic) Retention Relationships. Chemical Reviews 107 (2007) 3212–3246. https://doi.org/10.1021/cr068412z

R. Kaliszan. Chromatography and capillary electrophoresis in modelling the basic processes of drug action. TrAC Trends in Analytical Chemistry 18 (1999) 400-410. https://doi.org/10.1016/S0165-9936(99)00120-X

J.X. Soares, Á. Santos, C. Fernandes, M.M.M. Pinto. Liquid chromatography on the different methods for the determination of lipophilicity: an essential analytical tool in medicinal chemistry. Chemosensors 10 (2022) 10, 340. https://doi.org/10.3390/chemosensors10080340

D. Obradović, Ł. Komsta, V.M. Petrović, I. Stojković, S. Lazović. An alternative biomimetic tool – Dual hydrophilic/reversed-phase interaction mode. Microchemical Journal 193 (2023) 108967 https://doi.org/10.1016/j.microc.2023.108967

M. Koba, T. Bączek, M.P. Marszałł. Importance of retention data from affinity and reverse-phase high-performance liquid chromatography on antitumor activity prediction of imidazoacridinones using QSAR strategy. Journal of Pharmaceutical and Biomedical Analysis 64-65 (2012) 87-93. https://doi.org/10.1016/j.jpba.2012.02.010

K. Nikolic, S. Filipic, A. Smoliński, R. Kaliszan, D. Agbaba. Partial least square and hierarchical clustering in ADMET modelling: prediction of blood – brain barrier permeation of α-adrenergic and imidazoline receptor ligands. The Journal of Pharmacy & Pharmaceutical Sciences 16 (2013) 622–647. https://doi.org/10.18433/j3jk5p

J. Vucićević, K. Nikolić, V. Dobričić, D. Agbaba. Prediction of blood–brain barrier permeation of a-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis. European Journal of Pharmaceutical Sciences 68 (2015) 94-105. http://dx.doi.org/10.1016/j.ejps.2014.12.014

A. Golbraikh, A. Tropsha. Beware of q2! Journal of Molecular Graphics and Modelling 20 (2002) 269-276. https://doi.org/10.1016/S1093-3263(01)00123-1

A. Tropsha, Best practices for QSAR model development, validation and exploitation, Mol. Inform. 29 (2010) 476–488. https://doi.org/10.1002/minf.201000061

P.K. Ojha, I. Mitra, R.N. Das, K. Roy. Further exploring rm2 metrics for validation of QSPR models data set. Chemometrics and Intelligent Laboratory Systems 107 (2011) 194–205. https://doi.org/10.1016/j.chemolab.2011.03.011

A.D. Becke. Density-functional thermochemistry. III. The role of exact exchange. The Journal of Chemical Physics 98 (1993) 5648-5652. https://doi.org/10.1063/1.464913

C. Lee, W. Yang, R.G. Parr. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B 37 (1988) 785-789. https://doi.org/10.1103/physrevb.37.785

M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, V.G. Zakrzewski, J.A.Jr. Montgomery, R.E. Stratmann, J.C. Burant, S. Dapprich, J.M. Millam, A.D. Daniels, K.N. Kudin, M.C. Strain, O. Farkas, J. Tomasi, V. Barone, M. Cossi, R. Cammi, B. Mennucci, , C. Pomelli, C. Adamo, S. Clifford, J. Ochterski, G.A. Petersson, P.Y. Ayala, Q. Cui, K. Morokuma, D.K. Malick, A.D. Rabuck, K. Raghavachari, J. B. Foresman, J. Cioslowski, J.V. Ortiz, A.G. Baboul, B.B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R. Gomperts, R.L. Martin, D.J. Fox, T. Keith, M.A. Al-Laham, C.Y Peng, A. Nanayakkara, C. Gonzalez, M. Challacombe, P.M.W. Gill, B.G. Johnson, W. Chen, M.W. Wong, J.L. Andres, M. Head-Gordon, E.S. Replogle, J.A. Pople. Gaussian 98 (revision A.7), Gaussian, Inc., Pittsburgh, PA 1998. https://gaussian.com/

ChemAxon Marvin 5.5.1.0 program, Budapest, Hungary, 2011. www.chemaxon.com/products.html

Dragon 6, TALETE srl, Via V. Pisani, 13 - 20124 Milano – Italy. http://www.talete.mi.it

CS Chem3D Ultra 7.0, Cambridge Soft Corporation, (Property Picker ActiveX Control), 100 Cambridge Park Dr. Cambridge, MA 02140-2317 U.S.A., 2001. http://www.cambridgesoft.com/

R.G. Parr, R.G. Pearson. Absolute hardness: companion parameter to absolute electronegativity. Journal of the American Chemical Society 105 (1983) 7512-7516. https://doi.org/10.1021/ja00364a005

A. Rácz, D. Bajusz, K. Héberger. Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR. Molecular Informatics 38 (2019) 1800154. https://doi.org/10.1002/minf.201800154

Umetrics AB, SIMCA P+ program, Version 12.0.0.0, Umeå, May 20, 2008, www.umetrics.com.

S. Wold, E. Johansson, M. Cocchi, in: H. Kubinyi, (Ed.), 3D QSAR in Drug Design, Theory, Methods, and Applications, ESCOM Science Publishers, Leiden 1993, pp. 523-550.

P.K. Ojha, K. Roy. Comparative QSARs for antimalarial endochins:importance of descriptor thinning and noise reduction prior to feature selection. Chemometrics and Intelligent Laboratory Systems 109 (2011) 146-161. https://doi.org/10.1016/j.chemolab.2011.08.007

STATISTICA Neural Networks 4.0, StatSoft, Inc., Tulsa, OK, 1998.

L. Eriksson, E. Johansson, N. Kettaneh-Wold, J. Trygg, C. Wikstrom, S. Wol (eds.), Multi-and Mega-variate Data Analysis. Basic Principles and Applications I, 2nd edn. Umetrics Academy, Umeå, 2001.

C. Giaginis, A. Tsantili-Kakoulidou. Current state of the art in HPLC methodology for lipophilicity assessment of basic drugs. A Review. Journal of Liquid Chromatography & Related Technologies 31 (2008) 79-96. https://doi.org/10.1080/1082607070166562679

F. Burden. Molecular Identification Number for Substructure Searches. The Journal for Chemical Information and Computer scientists 29 (1989) 225-227. https://doi.org/10.1021/ci00063a011

E. Estrada. Spectral Moments of the Edge Adjacency Matrix in Molecular Graphs. 1. Definition and Applications to the Prediction of Physical Properties of Alkanes. The Journal for Chemical Information and Computer scientists 36 (1996) 844-849. https://doi.org/10.1021/ci950187r

S. Marković, I. Gutman. Spectral Moments of the Edge Adjacency Matrix in Molecular Graphs. Benzenoid Hydrocarbons. The Journal for Chemical Information and Computer scientists 39 (1999) 289-293. https://doi.org/10.1021/ci980032u

K. Bodzioch, A. Durand, R. Kaliszan, T. Baczek, Y. Vander Heyden. Advanced QSRR modelling of peptides behaviour in RPLC. Talanta 81 (2010) 1711–1718. https://doi.org/10.1016/j.talanta.2010.03.028

M. Randić. Wiener-Hosoya Index A Novel Graph Theoretical Molecular Descriptor. The Journal for Chemical Information and Computer scientists 44 (2004) 373-377. https://doi.org/10.1021/ci030425f

M. Randić. On Characterization of Cyclic Structures. The Journal for Chemical Information and Computer scientists 37 (1997) 1063-1071. https://doi.org/10.1021/ci9702407

R. Todeschini, V. Consonni. Handbook of Molecular Descriptors, Wiley-VCH, Weinheim, 2000.

B. Lučić, A. Miličević, S. Nikolić, N. Trinajstić. Harary Index – Twelve Years Later. Croatica Chemica Acta 75 (2002) 847-868. https://hrcak.srce.hr/131738

V. Consonni, T. Todeschini, M. Pavan. Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors. The Journal for Chemical Information and Computer scientists 42 (2002) 682-692. https://doi.org/10.1021/ci015504a

Published

04-05-2024 — Updated on 03-05-2024

How to Cite

Oljacic, S., Križman, M., Popovic-Nikolic, M., Vovk, I., Nikolic, K., & Agbaba, D. (2024). Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions. ADMET and DMPK, 12(5), 737–757. https://doi.org/10.5599/admet.2278

Issue

Section

Original Scientific Articles

Funding data