3D-QSAR and Physical Property Modeling Using Quantum-Mechanically- Derived Molecular Surface Properties A Dissertation Kendall Byler 2007 3D-QSAR and Physical Property Modeling Using Quantum Mechanically Derived Molecular Surface Properties Den Naturwissenschaftlichen Fakultäten der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades vorgelegt von Kendall Grant Byler aus Huntsville Als Dissertation genehmigt von den naturwissenschaftlichen Fakultäten der Friedrich-Alexander-Universität Erlangen-Nürnberg. Tag der mündlichen Prüfung: 11.05.2007 Vorsitzender der Promotionskomission: Prof. Dr. E. Bänsch Erstberichterstatter: Prof. Dr. T. Clark Zweitberichterstatter: Prof. Dr. P. Gmeiner Acknowledgements I would like to thank those but for whom this work would not have been possible. The first of these is Professor Dr. Tim Clark, who provided the opportunity and the guidance in my study of computational chemistry. And thanks go to the members of the Clark group who helped me in my endeavors: Dr. Nico van Eikema Hommes, Dr. Harald Lanig, Dr. Ralph Puchta, Dr. Matthias Hennemann, Matthias Brüstle, Anselm Horn, Dr. Olaf Othersen, Dr. Gudrun Schürer, Dr. Tatyana Shubina, Florian Haberl, Kirsten Höhfeld, Catalin Rusu, Jr-Hung Lin, Hakan Kayi, and Sergio Sanchez. And also to members of the Gasteiger group for their assistance: Dr. Simon Spycher, Prof. Dr. Fernando da Costa, Dimitar Hristozov, Dr. Christof Schwab, and Dr. Thomas Engel, and of course Adrian Jung of the Kirsch group. Thanks also to the Pfizer Corporation for their financial support of this research. I would thank my family: my parents, Paul and Carol Byler, my sister, Ashley, my grandparents, Henry and Martha Snoddy, Elza and Emma Byler, and my beautiful wife, Anastasia. And I would thank the friends everywhere that stayed friends despite the separations of time and distance. i Contents 1 Introduction ................................................................................................1 1.1 Drug Discovery............................................................................................1 1.2 Property Modeling ......................................................................................3 1.3 A Quantum-Mechanical, Molecular Orbital Approach..........................4 2 Surface-Integral QSPR Models: Local Energy Properties ....................7 2.1 Introduction.................................................................................................7 2.1.1 Local Molecular Properties...............................................................8 2.1.2 Surface-Integral Models....................................................................9 2.2 Methods......................................................................................................15 2.3 Results ........................................................................................................16 2.3.1 Octanol/Water Partition Coefficient ...............................................16 2.3.2 Free Energy of Solvation ................................................................23 2.3.2.1 Free Energy of Solvation in Octanol ...................................................... 23 2.3.2.2 Free Energy of Solvation in Water ......................................................... 28 2.3.3 Acid Dissociation Constant.............................................................33 2.3.4 Boiling Point ...................................................................................36 2.3.5 Glass Transition Temperature.........................................................40 2.3.6 Aqueous Solubility..........................................................................44 2.4 Discussion...................................................................................................48 2.5 Conclusions................................................................................................51 ii 3 Support Vector Classification of Phospholipidosis-Inducing Drugs... 52 3.1 Introduction...............................................................................................52 3.1.1 Phospholipidosis .............................................................................52 3.1.2 Phospholipidosis Models ................................................................54 3.1.3 Surface Autocorrelations ................................................................56 3.1.4 Statistical Methods..........................................................................57 3.1.4.1 Support Vector Machines......................................................................57 3.1.4.2 Multivariate Adaptive Regression Splines............................................60 3.2 Methods......................................................................................................61 3.3 Results........................................................................................................62 3.3.1 Support Vector Machines ...............................................................63 3.3.2 Multivariate Adaptive Regression Splines Using Autocorrelation Indices.......................................................68 3.4 Discussion ..................................................................................................70 3.5 Conclusions................................................................................................73 4 3D-QSAR Using Local Properties .......................................................... 74 4.1 Introduction...............................................................................................74 4.1.1 Comparative Molecular Field Analysis ..........................................74 4.1.2 Partial Least Squares Regression....................................................76 4.1.3 Local Properties ..............................................................................77 4.2 Computational Methods...........................................................................79 4.3 Results and Discussion..............................................................................80 4.3.1 Serotonin Receptor Agonists/Antagonists......................................80 4.3.2 Adrenergic Receptor Agonists/Antagonists....................................84 4.3.3 Dopamine D4 Antagonists..............................................................86 4.3.4 Avian Influenza Neuraminidase Inhibitors.....................................89 4.3.5 Mutagenic Tertiary Amides............................................................92 iii 4.3.6 The Effect of Grid Orientation on Predictivity ...............................96 4.4 Conclusions..............................................................................................101 5 Conclusions and Outlook.......................................................................103 5.1 Conclusions..............................................................................................103 5.2 Outlook.....................................................................................................104 6 Summary .................................................................................................106 7 Zusammenfassung ..................................................................................110 Appendix A..................................................................................................114 Appendix B..................................................................................................151 References....................................................................................................152 iv Chapter 1 Introduction 1.1 Drug Discovery It has been estimated1 that, out of a pool of millions of compounds screened, 10,000 reach the animal testing phase, which will then likely produce ten drug candidates for human clinical trials, of which only one will reach the market. It may also require 15 years and 750,000 U.S. dollars in the process. Drug candidates that fail late in the testing process will never produce a return for the company that has invested so much time and money. Pharmaceutical companies must offset these losses by recouping the expenditure from among the several successfully tested drugs they produce. In an effort to minimize the potential loss from focusing on compounds that will never result in a marketable drug, much preliminary research and testing are done. The rational drug-design approach2 to this problem begins by identifying a molecular target involved in a pathophysiological process and characterizing its structure and function; then begins the search for a lead compound. This is usually achieved by means of an array of in vitro screens for biological activity. Large groups of compounds may be evaluated simultaneously in this way and the procedure is referred to as high-throughput screening (HTS). Once a lead compound is discovered, it may also be found to have some undesirable properties such as high toxicity, poor bioavailability or pharmacokinetics. Libraries of compounds may be synthesized that have modifications to the general structure of the lead compound in an effort to modulate the desirable and undesirable 1 Introduction effects. Structure-activity relationships (SAR’s) may be observed concurrently with the study of the combinatorial library that point to a common chemical substructure that produces the pharmacological effect. The medicinal chemist can then make various modifications to the pharmacophore in order to improve its properties. Kubinyi3