Aqueous solubility prediction of organic compounds Item Type text; Dissertation-Reproduction (electronic) Authors Yang, Gang Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 01/10/2021 15:23:34 Link to Item http://hdl.handle.net/10150/298795 AQUEOUS SOLUBILITY PREDICTION OF ORGANIC COMPOUNDS by Gang Yang A Dissertation Submitted to the Faculty of the DEPARTMENT OF PHARMACEUTICAL SCIENCES In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2005 UMI Number: 3158221 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. 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Box 1346 Ann Arbor, Ml 48106-1346 2 The University of Arizona ® Graduate College As members of the Final Examination Committee, we certify that we have read the dissertation prepared by Gang Yang entitled Aqueous Solubility Prediction of Organic Compounds and recommend that it be acceptable as fulfilling the dissertation requirement for the Degree of Doctor of Philosophv Samuel HTy alkowsky, Ph.D. date /'QC Michael Mayersohn, Prf®. date Paul B. Myrdal, Ph.Dr^ date date date Final approval and acceptance of this dissertation is contigent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fi^filling the disseration requirement. Dissertation DirectorfSamuel H. Yalkowsky, Ph.D. date 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. SIGNED: 4 ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my advisor, Dr. Samuel Yalkowsky, for giving me the opportunity and financial support to study in the field of Pharmaceutical Sciences, for his expert guidance and mentorship, and for his constant support and encouragement over the years at Arizona. He has made it a challenging and fun experience that I will cherish for years to come. I would like to thank members of my major committee. Dr. Michael Mayersohn and Dr. Paul Myrdal, for reviewing drafts of the dissertation on a very short notice and providing helpful comments on the dissertation. My appreciations also go to the members of my minor committee. Dr. Srini Raghavan and Dr. Scott Saavedra, for dedicating their time to serve on my committee. During the years at Arizona, I had the pleasure to work with many friends and colleagues including Neera, Jeff, Debra, Yingqing, Tapan, Ahbi, Yan, Jenny, Akash, Kia, Stephen, Ritesh, Yvonne, Huadong, Abhi, Kelly, Phil, Julie, Eric and Will. I would like to thank all of them for being there for me when I needed help. My special thanks go to Dr. Wolf-Dietrich Dilenfeldt for his fragmentation program. I am greatly indebted to many teachers back in China, in particularly, Xinfang Liu and Manguo Tao for keeping me interested in science and helping me excel. At last, I would like to thank my family: my fiancee Hui for her love and encouragement throughout my study; my parents and sister for their years of loving support and understanding. 5 DEDICATION To my parents and Hui 6 TABLE OF CONTENTS LIST OF FIGURES 10 LIST OF TABLES 11 ABSTRACT 12 CHAPTER 1: INTRODUCTION 14 CHAPTER 2: AQUEOUS SOLUBILITY PREDICTION METHODS 21 INTRODUCTION 21 THE IDEAL SOLUTION AND REGULAR SOLUTION THEORY 21 Ideal Solution 21 Regular Solution 22 Aqueous Solution 22 Crystalline Solute 23 REVIEW OF LITERATURE 24 Group Contribution Approach 24 Mobile Order Theory 25 Linear Solvation Energy Relationship (LSER) 26 Atom Type Electrotopological State Indices 27 MODEL DEVELOPMENT 28 General Solubility Equation 28 The AQUAFAC Method 29 A New Model 30 CHAPTER 3: DATA COLLECTION AND ANALYSIS 32 INTRODUCTION 32 DATA 33 Solubility Data 33 Melting Point (MP) 36 Octanol-Water Partition Coefficient (P) 36 RELATIONSHIPS 40 7 TABLE OF CONTENTS - Continued Regression Analysis 40 CLOGP Prediction Evaluation 41 Predicted versus Experimental Octanol-Water Partition Coefficient 42 Solubility and Octanol-Water Partition Coefficient 44 Solubility and Melting Point 50 CHAPTER 4: STRUCTURAL FRAGMENTATION 52 INTRODUCTION 52 SMILES 54 SMARTS 57 STRUCTURAL FRAGMENTATION PROGRAM 57 FRAGMENTATION SCHEME 59 CHAPTER 5: EVALUATION OF SOLUBILITY PREDICTION BY THE GENERAL SOLUBILITY EQUATION ON A SET OF DIVERSE COMPOUNDS 64 INTRODUCTION 64 METHODS 65 Data Set 65 Evaluation 66 Regression Analysis 68 RESULTS AND DISCUSSION 69 Overall Performance 69 Solid Versus Liquid 76 Non-electrolyte Versus Weak Electrolyte 78 Regression Analysis 82 SUMMARY 83 CHAPTER 6: COMPARISON OF THE GENERAL SOLUBILITY EQUATION AND THE METHOD USING AN AMENDED SOLVATION ENERGY RELATIONSHIP. 84 INTRODUCTION 84 METHODS 86 8 TABLE OF CONTENTS - Continued RESULTS 87 Octanol-Water Partition Coefficients 87 Melting Point 89 Solubility 90 DISCUSSION 93 SUMMARY 98 CHAPTER 7: DEVELOPMENT OF THE EXTENDED AQUAFAC MODEL 99 INTRODUCTION 99 METHODS 101 Data Set 101 Aqueous Activity Coefficient 101 Structural Fragmentation 102 Regression Analysis 102 RESULTS 103 Fragmentation Performance 103 Regression Analysis 103 Group Contribution Values qi 103 Aqueous Solubility Prediction 107 CHAPTER 8: DEVELOPMENT OF A NEW GROUP CONTRIBUTION MODEL FOR AQUEOUS SOLUBILITY PREDICTION 108 INTRODUCTION 108 METHODS 110 Data Set 110 Structural Fragmentation 110 Regression Analysis 110 RESULTS Ill Fragmentation Performance Ill Regression Analysis Ill 9 TABLE OF CONTENTS - Continued Group Contribution Values mi 112 CHAPTER 9: COMPARISON OF AQUEOUS SOLUBILITY PREDICTION MODELS 116 PREDICTION COMPARISON 117 SUMMARY 121 APPENDIX A. Experimental Solubility Collection with Melting Point, log P and Predicted Solubility Using the General Solubility Equation 122 APPENDIX B. Comparison of Predicted Solubility Using the General Solubility Equation and the Amended Solvation Energy Relationship 161 APPENDIX C. Predicted Solubility for Test Set 1 175 APPENDIX D. Predicted Solubility for Test Set 2 176 APPENDIX E. Predicted Solubility for Test Set 3 177 REFERENCES 179 10 LIST OF FIGURES Figure 3.1: Log solubility (log Sw) distribution of 1804 compounds in the data set 35 Figure 3.2: Melting point (MP) distribution of 1037 solids in the data set 37 Figure 3.3: Calculated log octanol-water partition coefficient (Clog?) distribution of 1804 compounds in the data set 38 Figure 3.4: Experimental log octanol-water partition coefficient (MlogP) distribution of 1226 compounds in the data set 39 Figure 3.5: Plot of calculated log octanol-water partition coefficients (ClogP) versus experimental log octanol-water partition coefficients (MlogP) 43 Figure 3.6: Scatterplot of log versus calculated log octanol-water partition coefficients (ClogP) for 597 liquids or gases in the data set 46 Figure 3.7: Scatterplot of log Sw versus experimental log octanol-water partition coefficients (MlogP) for 393 liquids or gases with available experimental log P in the data set 47 Figure 3.8: Scatterplot of log aqueous solubilities (log Sw) versus calculated log octanol-water partition coefficients (ClogP) for all 1804 compounds in the data set 48 Figure 3.9: Scatterplot of log aqueous solubilities (log Sw) versus experimental log octanol-water partition coefficients (MlogP) for 1225 compounds with available experimental log P in the data set 49 Figure 3.10: Scatterplot of log Sw versus experimental melting point for 1525 compounds with available melting point information in the data set 51 Figure 4.1: An example illustrating the expression of Benzocaine structure in SMILES 56 Figure 4.2: Fragmentation of benzocaine structure according to the AQUAFAC Scheme 63 Figure 5.1: Plot of predicted log Sw versus experimental log Sw 71 Figure 5.2: Absolute prediction error distribution 72 Figure 5.3: Plot of prediction error against experimental log Sw 73 Figure 5.4: Plot of prediction error against calculated log P (ClogP) 74 Figure 5.5: Plot