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Pharmacological Activity and Membrane Interactions of Antiarrhythmics: 4D-QSAR/QSPR Analysis

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Abstract

Purpose. This study was done to explore the relationships of both macroscopic and molecular level physicochemical properties to in-vivo antiarrhythmic activity and interactions with phospholipid membranes for a set of cationic-amphiphilic analogs.

Methods. The 4D-QSAR method, recently developed by Hopfinger and co-workers (1), was employed to establish 3D-QSAR/QSPR models. Molecular dynamics simulations provided the set of conformational ensembles which were analyzed using partial least squares regression in combination with the Genetic Function Approximation algorithm to construct QSAR and QSPR models.

Results. Significant QSAR models for in-vivo antiarrhythmic activity were constructed in which logP (the partition coefficient), and specific grid cell occupancy (spatial) descriptors are the main activity correlates. LogP is the most significant QSAR descriptor. 4D-QSPR models were also developed for two analog-membrane interaction properties, the change in a membrane transition temperature and the ability of the analogs to displace adsorbed Ca2+-ions from phosphatidylserine mono-layers.

Conclusions. Spatial features, represented by grid cell occupancy descriptors, supplement partition coefficient, which is the most important determinant of in-vivo antiarrhythmic activity, to provide a comprehensive model for drug action. The QSPR models are less significant in statistical measures, and limited to interpretation of possible molecular mechanisms of action.

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REFERENCES

  1. A. J. Hopfinger, S. Wang, J. S. Tokarski, B. Jin, M. Albuquerque, P. Madhav, and C. Duraiswami. J. Am. Chem. Soc., 119:10509–10524 (1997).

    Google Scholar 

  2. R. H. de Jong. Local Anesthetics. Mosby-Year Books Ltd., St. Louis, 1994.

    Google Scholar 

  3. H. Lüllmann, K. Mohr, A. Ziegler, and D. Bieger. Pocket Atlas of Pharmacology. Thieme Medical Publishers, Inc., New York, 1993.

    Google Scholar 

  4. R. S. Sheldon, N. J. Cannon, A. S. Nies, and H. J. Duff. J. Pharmacol. Exp. Ther. 33:327–331 (1988).

    Google Scholar 

  5. L. M. Hondeghem and J. W. Mason. In: B. G. Katzung [ed.], Basic and Clinical Pharmacology, pp 151–168. Appleton & Lange, Norwalk (1987)

    Google Scholar 

  6. G. R. Strichartz. Handbook of Experimental Pharmacology, Vol. 81: Local Anesthetics. Springer Verlag, Berlin, Germany, 1987.

    Google Scholar 

  7. E. Overton. Vierteljahresschrift Naturforsch. Ges. Zürich, 44:88–135 (1899).

    Google Scholar 

  8. A. G. Lee. Nature 262:545–548 (1976).

    Google Scholar 

  9. M. Klingmüller. Ph.D. Thesis, Kiel, Germany, 1990.

  10. S. I. Landmann. Med. Thesis., Kiel, Germany (1996).

  11. S. Girke, K. Mohr, and S. Schrape. Biochem. Pharmacol. 38:2487–2496 (1989).

    Google Scholar 

  12. H. Lüllmann, H. Plösch, and A. Ziegler. Biochem. Pharmacol. 29:2969–2974 (1980).

    Google Scholar 

  13. B. Kursch, H. Löllmann, and K. Mohr. Biochem. Pharmacol. 32:2589–2594 (1983).

    Google Scholar 

  14. R. Hanpft and K. Mohr. Biochim. Biophys. Acta 814:156–162 (1985).

    Google Scholar 

  15. R. Hanpft. Ph.D. Thesis, Kiel, Germany, 1987.

  16. R. D. Cramer, D. E. Patterson, and J. D. Bunce. J. Am. Chem. Soc. 110:5959–5967 (1988).

    Google Scholar 

  17. W. G. Glen, W. J. Dunn III, and D. R. Scott. Tetrahedron Computer Methodology 2:349–376 (1989).

    Google Scholar 

  18. D. Rogers and A. J. Hopfinger. J. Chem. Inf. Comput. Sci. 34:854 (1994).

    Google Scholar 

  19. W. J. Dunn III, and D. Rogers. Genetic partial least-squares in QSAR. In J. Devillers (ed.), Genetic Algorithms in Molecular Modeling, Academic Press, London, 1996, pp. 109–130.

    Google Scholar 

  20. K. Hasegawa, Y. Miyashita, and K. Funatsu. J. Chem. Inf. Comp. Sci. 37:306–310 (1997).

    Google Scholar 

  21. CHEMLAB-II, Revision 11.0. Molecular Simulations Incorporated, San Diego, CA (1993).

  22. J. J. P. Stewart. Mopac 6.0 Manual. Frank J. Seiler Research Laboratory, United States Air Force Academy, CO 80840 (1990).

    Google Scholar 

  23. Molsim Version 3.0. The Chem21 Group, Inc., Lake Forest, IL 60045 (1994).

  24. D. Rogers. WOLF 5.5 Release Notes (1993).

  25. WOLF 6.2 GFA Program. D. Rogers and Molecular Simulations Inc., San Diego, CA (1994).

  26. J. Friedman. Multivariate Adaptive Regression Splines. Technical report No. 102, Laboratory for Computational Statistics, Dept. of Statistics, Stanford University (Nov. 1988).

  27. D. O. Rauls and J. K. Baker. J. Med. Chem. 22:81–86 (1979).

    Google Scholar 

  28. H. Lüllmann, P. Timmermans, and A. Ziegler. Eur. J. Pharmacol. 60:277–285 (1979).

    Google Scholar 

  29. K. Borchardt, D. Heber, M. Klingmüller, K. Mohr, and B. Müller. Biochem. Pharmacol. 42(Suppl.):S61–S65 (1991).

    Google Scholar 

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Klein, C.D.P., Hopfinger, A.J. Pharmacological Activity and Membrane Interactions of Antiarrhythmics: 4D-QSAR/QSPR Analysis. Pharm Res 15, 303–311 (1998). https://doi.org/10.1023/A:1011983005813

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  • DOI: https://doi.org/10.1023/A:1011983005813

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