Statistical learning approaches for predicting pharmacological properties of pharmaceutical agents

STATISTICAL LEARNING APPROACHES FOR PREDICTING PHARMACOLOGICAL PROPERTIES OF PHARMACEUTICAL AGENTS LI HU (B.Sc, M.Sc, Jilin University) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF PHARMACY NATIONAL UNIVERSITY OF SINGAPORE 2007 ii Acknowledgements First and foremost, my heartfelt appreciation and thanks go to my supervisor and mentor, Associate Professor Chen Yu Zong. His innovative insights, excellent guidance, words of wisdom, constant support and patience throughout my study have been crucial to this research analysis. I would like to dedicate my thesis to my wife, Dr. Zhu Shizhen. The beautiful time and memories we have in Singapore are definitely great treasures in my life, I cherish it very much. And I am eternally grateful for everything you for me, I appreciate it very much. I want to praise my closest collaborator and friend Dr. Ung Choong Yong. I am proud that our collaboration could be another good story of inter-background collaboration. Many thanks to Dr. Xue Ying, Dr. Li Zerong and Dr. Yap Chun Wei, for their suggestions and contributions and my thanks to all the members in BIDD group, for their kind supports in one way or another. At the same time my thanks also to everyone else whose name may not be down here but knows who they are. I am lucky that I have the greatest families in the world. They always had total confidence in me that I could achieve what I set out to do. Thanks very much my dearest parents and all the family members. Finally, I am very grateful to the National University of Singapore for awarding me the Research Scholarship, the prestigious President Graduate Fellowship and the Best Graduate Researcher Award during my PhD candidature. iii Table of Contents Acknowledgements . ii Table of Contents . iii Summary viii List of Tables x List of Figures xiii List of Abbreviations xv List of Publications . xvii Chapter Introduction 1.1 Drug discovery and pharmacological properties of pharmaceutical agents . 1.2 Statistical learning methods in characterization of pharmacological properties of pharmaceutical agents . 1.3 Describing molecular properties using molecular descriptors 15 1.4 Feature selection methods . 15 1.5 Models studied in this work and the importance of these models 16 1.5.1 Pregnane X Receptor (PXR) activators 17 1.5.2 Blood brain barrier (BBB) agents .18 1.5.3 Estrogen receptor (ER) agonists .19 1.5.4 Genotoxicity agents 19 1.5.5 Tetrahymena pyriformis toxicity (TPT) agents .20 1.6 Objectives and outline of this work 21 Chapter Methods 26 2.1 Datasets . 26 2.1.1 Quality analysis .26 2.1.2 Statistical molecular design 27 iv 2.1.2.1 Introduction 27 2.1.2.2 Kennard and Stone algorithm 30 2.1.2.3 Removal-until-done algorithm .30 2.1.3 Diversity and representativity of datasets .31 2.2 Molecular descriptors 32 2.2.1 Types of molecular descriptors .32 2.2.2 Scaling .36 2.2.2.1 Auto-scaling .37 2.2.2.2 Range scaling (Normalization) 37 2.3 Feature selection method 38 2.3.1 Recursive feature elimination (RFE) 39 2.3.2 The procedure of RFE .41 2.4 Statistical learning methods 43 2.4.1 Methods .43 2.4.1.1 Logistic regression (LR) 43 2.4.1.2 Linear discriminate analysis (LDA) .43 2.4.1.3 C4.5 decision trees (DT) 45 2.4.1.4 k-nearest neighbor (k-NN) .47 2.4.1.5 Probabilistic neural network (PNN) 49 2.4.1.6 Support vector machine (SVM) 52 2.4.2 Parameters optimization 55 2.5 Model validation . 55 2.5.1 Performance evaluation of a pharmacological property prediction model .55 2.5.2 Performance evaluation methods 57 2.5.3 Overfitting .59 v Chapter Prediction of pharmacokinetics properties of pharmaceutical agents 62 3.1 Pregnane X receptor activators model 62 3.1.1 Introduction .62 3.1.2 Methods .66 3.1.2.1 Collection of PXR activators and non-activators 66 3.1.2.2 Construction of training and testing sets .68 3.1.2.3 Molecular descriptors 69 3.1.2.4 Computational parameters and performance evaluation 69 3.1.3 Results and discussion 70 3.1.3.1Promiscuity nature of PXR activator structures and the selected molecular descriptors for classifying PXR activators 70 3.1.3.2 Performance of SLMs for predicting PXR activators 77 3.1.3.3 Relevance of molecular descriptors to the activity of PXR activators .81 3.1.4 Conclusion 87 3.2 Blood brain barrier agents model 89 3.2.1 Introduction .89 3.2.2 Methods .91 3.2.2.1 Selection of BBB+ and BBB- agents 91 3.2.2.2 Construction of training and testing sets .92 3.2.2.3 Molecular descriptors 92 3.2.3 Results and discussion 93 3.2.3.1 Molecular descriptors selected for BBB penetration prediction .93 3.2.3.2 Prediction accuracy for BBB+ and BBB- agents 98 3.2.4 Conclusion 104 Chapter Prediction of pharmacodynamics properties of pharmaceutical agents . 106 vi 4.1. Introduction 106 4.2 Methods . 110 4.2.1 Data collection of ER agonists and ER non-agonists .110 4.2.2 Structural diversity 112 4.2.3 Construction of training and testing sets .112 4.2.4 Molecular descriptors 113 4.3 Results and discussion 113 4.3.1 Overall prediction accuracies and merit of the statistical learning methods 113 4.3.2 Molecular descriptors associated with ER agonism .119 4.3.3 Misclassified ER agonists and non-agonists from independent test sets 128 4.4 Conclusion 130 Chapter Prediction of toxicological properties of pharmaceutical agents . 132 5.1 Genotoxicity model . 132 5.1.1 Introduction .132 5.1.2 Methods .136 5.1.2.1 Selection of GT+ and GT- agents 136 5.1.2.2 Construction of training and testing sets .136 5.1.2.3 Molecular descriptors 137 5.1.2.4 Parameter for feature selection .137 5.1.3 Results and discussion 138 5.1.3.1 Overall prediction accuracies 138 5.1.3.2 Relevance of selected features to genotoxicty study 140 5.1.3.3 Performance evaluation .144 5.1.3.4 Misclassified GT+ and GT- agents from independent test sets .146 5.1.4 Conclusion 152 vii 5.2 Tetrahymena pyriformis toxicity Model . 154 5.2.1 Introduction .154 5.2.2 Methods .157 5.2.2.1 Selection of TPT and non-TPT- agents 157 5.2.2.2 Construction of training and testing sets .159 5.2.2.3 Molecular descriptors 159 5.2.3 Results and discussion 159 5.2.3.1 Overall prediction accuracies 159 5.2.3.2 Relevance of selected molecular descriptors to Tetrahymena pyriformis toxicity prediction 165 5.2.3.3 Performance evaluation .173 5.2.4 Conclusion 174 Chapter Concluding remarks . 176 6.1 Major findings . 176 6.1.1 Merits of SLMs in the studies of pharmacological properties 176 6.1.2 Merits of RFE in the studies of pharmacological properties 177 6.1.3 The pharmacokinetic models: PXR activators and BBB agents .177 6.1.4 The pharmacodynamic model: ER agonists .179 6.1.5 The toxicity models: Genotoxicity agents and TPT agents 180 6.2 Contributions . 181 6.3 Limitations 184 6.4 Suggestions for future studies . 189 Bibliography . 191 viii Summary Drug development is aimed at therapeutic agents that possess desirable pharmacological properties, which include pharmacokinetic, pharmacodynamic and toxicological profiles. Historically, inappropriate pharmacological properties have been one of the primary reasons for the failure of drug candidates in the later stages of drug development. Thus tools for predicting pharmacological properties in early drug discovery stages are desirable for fast elimination of agents with undesirable properties so that development efforts can be focused on the most promising candidates. As part of the efforts for developing such tools, computational approaches have been explored for predicting various pharmacological properties of pharmaceutical agents. In particular, statistical learning methods (SLMs) have shown promise for these tasks by statistically analyzing the correlation between chemical structures and a specific property to derive statistical models or rules for predicting whether an agent possesses a specific property or not. Previously, pharmacological property prediction models were frequently built upon limited number of structurally related compound sets and by using linear regression methods. Hence they may not be suitable for the prediction of pharmacological properties of structurally diverse compounds and for pharmacological properties that are regulated by multiple mechanisms. Moreover, some pharmacological properties, which are pharmacologically and clinically important, are insufficiently studied by different computational approaches. Thus it is of interest and necessary to examine the potential of using enlarged and more diverse groups of compounds and non-linear SLMs in improving the quality of pharmacological property prediction models and in applying the SLMs on those important but insufficiently studied pharmacological properties. This ix work aims at studying the applicability of SLMs, such as support vector machine (SVM), probabilistic neural network (PNN), k nearest neighbor (k-NN), C4.5 decision tree (C4.5 DT), linear discriminate analysis (LDA) and logistic regression (LR) to classify compounds of diverse structures into different pharmacological property categories. Specifically, the pharmacokinetic models explored in this work are activators for pregnane X receptor (PXR) and blood brain barrier (BBB) agents. The pharmacodynamic model studied in this work is agonists of estrogen receptor (ER) and the toxicity models studied are genotoxicity (GT) and Tetrahymena pyriformis toxicity (TPT) agents. A set of 199 molecular descriptors are used to describe the molecular pysicochemical properties of those pharmaceutical agents studied in this work. A feature selection method, recursive feature elimination (RFE), is incorporated to improve the prediction performance. The results show that SLMs could improve the quality of these pharmacological property prediction models by using enlarged and more diverse groups of compounds. RFE is able to identify a group of relevant molecular descriptors that reflect the pharmacological property of studied models and are consistent to quantitive structure activity relationship (QSAR), pharmacophore and X-ray crystallographic studies. In addition, selection of appropriate molecular descriptors can lead to substantially more balanced prediction accuracies and enhance the overall accuracies. Moreover, SLMs are found to be useful for developing prediction models and characterizing relevant physicochemical features for PXR activators and ER agonists, which are very important pharmacological properties of drug candidates but insufficiently explored in previous studies. x List of Tables Table 1.1 Performance of regression-based statistical learning methods for predicting compounds of specific pharmacokinetic, pharmacodynamic or toxicological property . Table 1.2 Performance of classification-based statistical learning methods for predicting compounds of specific pharmacokinetic, pharmacodynamic or toxicological property . 11 Table 2.1 Methods for selecting training and validation sets . 29 Table 2.2 Molecular descriptors used in this work 35 Table 2.3 Common descriptor selection methods used in pharmacological properties classification studies . 39 Table 2.4 Commonly used kernel functions . 53 Table 3.1 Diversity index (DI) for the compounds in several chemical groups, and the number of molecular descriptors selected by RFE for predicting each group of compounds by using a SLM classification system. 71 Table 3.2 RFE selected 83 molecular descriptors for SLMs classification of PXR activators . 73 Table 3.3 Performance of three statistical learning methods (k-NN, PNN and SVM) for predicting PXR and hPXR activators and non-activators determined by a 10fold cross validation study. . 77 Table 3.4 Performance of the PXR and hPXR activator prediction systems for predicting the 15 recently published hPXR activators . 80 Table 3.5 The Euclidean distance of the 15 PXR activators in the independent set to the 28 ambiguous PXR activators and the 98 human PXR activators 81 Table 3.6 Prediction accuracies of BBB penetrating (BBB+) and non-penetrating agents (BBB-) from different earlier studies reported in the literatures. . 90 Table 3.7 Thirty-seven molecular descriptors selected from the RFE feature selection method for classification of blood-brain barrier penetrating and nonpenetrating agents . 94 Table 3.8 Important descriptor classes selected for the prediction of blood-brain barrier penetrating and non-penetrating agents 97 Table 3.9 Differences in the values of descriptors important for distinguishing between blood-brain barrier penetrating (BBB+) agents and non-penetrating (BBB-) agents 98 BIBLIOGRAPHY 201 Kliewer SA, Goodwin B, Willson TM (2002). 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[...]... part of the efforts for developing such tools, computational methods have been explored for predicting various pharmacological properties of pharmaceutical agents SLMs are the computational approaches that are increasingly used for in silico HTS of compounds with diverse structures in early drug discovery stage 1.2 Statistical learning methods for characterization pharmacological properties of pharmaceutical. .. a decision tree process for the prediction of pharmaceutical agents of a particular pharmacological property from their structure by using a statistical learning method – C4.5 decision tree 46 Figure 2.4 Schematic diagrams illustrating the process of the prediction of pharmaceutical agents with a particular pharmacological property from its structure by using a statistical learning method — k-nearest... process of using feature selection method for selecting molecular descriptors most appropriate in the prediction of compounds of a particular pharmacological property by statistical learning methods 42 Figure 2.2 Schematic diagrams illustrating the process of the prediction of pharmaceutical agents with a particular pharmacological property from its structure by using a statistical learning. .. illustrating the process of the prediction of pharmaceutical agents with a particular pharmacological property from its structure by using a statistical learning method — probabilistic neural networks (PNN) 50 Figure 2.6 PNN four layers architecture 51 Figure 2.7 Schematic diagram illustrating the process of the prediction of pharmaceutical agents with a particular pharmacological property... INTRODUCTION 5 al 2004] have been the primary reasons for the failure of drug candidates in later stages of drug development Tools for predicting pharmacokinetic and pharmacodynamic properties as well as toxicological properties in the early drug design stages are needed for fast elimination of agents with undesirable properties so that development efforts can be focused on the most promising candidates... classification of genotoxic (GT+) and non-genotoxic (GT-) agents 141 Table 5.4 Overview of the prediction accuracies of genotoxic (GT+) and non-genotoxic (GT-) agents from this work as with those from other studies 145 xii Table 5.5 Comparison of the prediction accuracy of Tetrahymena pyriformis toxic (TPT) and non-toxic (non-TPT) agents by different statistical learning methods 161 Table 5.6 Performance... development of additional molecular descriptors for representing agents of more complex structures and for modeling more extensive sets of properties Detailed description of molecular descriptors will be presented in Chapter 2 1.4 Feature selection methods In practice, not all of those molecular descriptors are needed for representing features of a particular class of agents Among the descriptors some are irrelevant... Performance of support vector machines (SVM) and support vector machines with recursive feature elimination (SVM+RFE) for predicting Tetrahymena pyriformis toxic (TPT) and non-toxic (non-TPT) agents as evaluated by 5-fold cross validation 162 Table 5.7 Comparison of the prediction accuracy of Tetrahymena pyriformis toxic (TPT) and non-toxic (non-TPT) agents by different statistical learning. .. Li, C.W Yap, Y Xue, Z.R Li, C.Y Ung, L.Y Han, and Y.Z Chen Statistical Learning Approach for Predicting Specific Pharmacodynamic, Pharmacokinetic or Toxicological Properties of Pharmaceutical Agents Drug Dev Res 2006; 66 (4):245-259 5 H Li, C.W Yap, C.Y Ung, Y Xue, Z.R Li, L.Y Han, H.H Lin and Y.Z Chen Machine Learning Approaches for Predicting Compounds That Interact with Therapeutic and ADMET Related... capability of these statistical models The computed r2 values of these regression-based SLMs listed in Table 1.1 are in the range from 0.51 to 0.95 [Ertl et al 2000; Yamazaki et al 2004], which are at a level useful for predicting the activity values of compounds of particular pharmacological properties In an attempt to develop pharmacological property prediction models that cover more diverse ranges of structures . pharmacological properties of pharmaceutical agents 2 1.2 Statistical learning methods in characterization of pharmacological properties of pharmaceutical agents 5 1.3 Describing molecular properties using. STATISTICAL LEARNING APPROACHES FOR PREDICTING PHARMACOLOGICAL PROPERTIES OF PHARMACEUTICAL AGENTS LI HU (B.Sc, M.Sc, Jilin University) A THESIS SUBMITTED FOR THE. been explored for predicting various pharmacological properties of pharmaceutical agents. In particular, statistical learning methods (SLMs) have shown promise for these tasks by statistically