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Quantitative Structure-Activity Relationship Modeling to Predict Drug-Drug Interactions Between Acetaminophen and Ingredients in Energy Drinks

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Quantitative Structure-Activity Relationship Modeling to Predict Drug-Drug Interactions Between Acetaminophen and Ingredients in Energy Drinks by Emese Somogyvari A thesis submitted to the School of Computing in conformity with the requirements for the degree of Master of Science Queen's University Kingston, Ontario, Canada August 2014 Copyright c Emese Somogyvari, 2014 Abstract The evaluation of drug-drug interactions (DDI) is a crucial step in pharmaceutical drug discovery and design. Unfortunately, if adverse effects are to occur between the co-administration of two or more drugs, they are often difficult to test for. Tradi- tional methods rely on in vitro studies as a basis for further in vivo assessment which can be a slow and costly process that may not detect all interactions. Here is pre- sented a quantitative structure-activity relationship (QSAR) modeling approach that may be used to screen drugs early in development and bring new, beneficial drugs to market more quickly and at a lesser cost. A data set of 6,532 drugs was obtained from DrugBank for which 292 QSAR descriptors were calculated. The multi-label support vector machines (SVM) method was used for classification and the K-means method was used to cluster the data. The model was validated in vitro by exposing Hepa1-6 cells to select compounds found in energy drinks and assessing cell death. Model accuracy was found to be 99%, predicting 50% of known interactions despite being biased to predicting non-interacting drug pairs. Cluster analysis revealed in- teresting information, although current progress shows that more data is needed to better analyse results, and tools that bring various drug information together would be beneficial. Non-transfected Hepa1-6 cells exposed to acetaminophen, pyridoxine, creatine, L-carnitine, taurine and caffeine did not reveal any significant drug-drug i interactions, nor were they predicted by the model. ii Co-Authorship This research was conducted by Emese Somogyvari, under the supervision of Dr. Selim G. Akl and Dr. Louise M. Winn. iii Acknowledgments I would like to thank my supervisors Dr. Selim Akl of the School of Computing, Queens University and Dr. Louise Winn of the Department of Biomedical and Molecular Sciences, Queens University for their support and guidance throughout this project, as well as faithful lab members Chrissy, Christina, Margo, Nikki, and Sam for their patience and constant assistance. iv Contents Abstract i Co-Authorship iii Acknowledgments iv Contents v List of Tables viii List of Figures x List of Abbreviations xi Chapter 1: Introduction 1 1.1 Motivation . 1 1.2 Problem . 2 1.3 Hypothesis . 2 1.4 Objectives . 3 1.5 Contributions . 3 1.5.1 QSAR modeling to predict drug-drug interactions . 3 1.5.2 Acetaminophen and ingredients in energy drinks . 4 1.6 Outline of thesis . 4 Chapter 2: Background 5 2.1 Classification QSAR modeling . 5 2.2 Classification model using support vector machines . 5 2.2.1 Preprocessing . 6 2.2.2 Classification . 8 2.3 Multi-label classification . 9 2.3.1 Problem transformation . 10 2.3.2 Algorithm adaptation . 11 v 2.4 Drug-drug interactions . 11 2.4.1 Pharmacodynamic . 12 2.4.2 Pharmacokinetic . 16 Chapter 3: Predicting Drug-Drug Interactions 20 3.1 In vitro evaluation to predict metabolism-mediated drug-drug interac- tions in vivo ................................ 20 3.1.1 Predicting P450 substrates . 21 3.1.2 Predicting P450 inducers . 21 3.1.3 Predicting P450 inhibitors . 21 3.2 In silico modeling to predict drug-drug interactions . 22 3.2.1 Classification . 22 3.2.2 Data mining . 22 3.2.3 Descriptor-based . 23 3.2.4 Similarity measure . 25 3.2.5 Network-based . 27 3.2.6 QSAR modeling to predict drug-drug interactions between ac- etaminophen and ingredients in energy drinks . 28 Chapter 4: Materials and Methods 29 4.1 QSAR modeling . 31 4.1.1 Data collection . 31 4.1.2 Preprocessing . 32 4.1.3 Classification . 33 4.1.4 Model evaluation . 34 4.2 In vitro model validation . 35 4.2.1 Materials . 35 4.2.2 Cell culture . 36 4.2.3 Transfection . 36 4.2.4 Exposure . 37 4.2.5 Cytotoxicity . 38 4.2.6 Data analysis . 38 Chapter 5: Results 40 5.1 Model evaluation . 40 5.1.1 SVM . 40 5.1.2 K-means and DDI network . 52 5.2 In vitro model validation . 59 5.2.1 Transfection . 59 5.2.2 Cytotoxicity . 59 vi Chapter 6: Conclusions and Future Work 61 6.1 Summary and discussion . 61 6.1.1 SVM . 61 6.1.2 K-means and DDI network . 63 6.1.3 In vitro model validation . 63 6.2 Future work . 64 6.2.1 Validating predictions . 64 6.2.2 Additional information for network analysis . 64 6.2.3 Indirect interactions . 64 6.3 Conclusion . 65 Bibliography 66 Appendix A: Additional Information 75 A.1 Complete list of QSAR descriptors . 75 vii List of Tables 5.1 Interactions predicted by QSAR model . 43 5.2 Known interactions for acetaminophen and select ingredients in energy drinks . 44 5.3 Overview of top predicted interactions for acetaminophen . 46 5.4 Overview of top predicted interactions for pyridoxine . 47 5.5 Overview of top predicted interactions for creatine . 48 5.6 Overview of top predicted interactions for L-carnitine . 49 5.7 Overview of top predicted interactions for taurine . 50 5.8 Overview of top predicted interactions for caffeine . 51 5.9 Cluster information for k=2 . 52 5.10 Cluster scores for k=2 . 52 5.11 Cluster information for k=3 . 53 5.12 Cluster scores for k=3 . 53 5.13 Cluster information for k=5 . 53 5.14 Cluster scores for k=5 . 54 5.15 Cluster information for k=7 . 54 5.16 Cluster scores for k=7 . 55 5.17 Cluster information for k=10 . 55 5.18 Cluster scores for k=10 . 56 viii 5.19 P450 content of clusters for k=2 . 58 5.20 LDH assay results . 60 ix List of Figures 2.1 Example of a multi-label data set . 10 2.2 Receptor agonist . 13 2.3 Receptor antagonist . 14 2.4 Signal transduction . 15 2.5 P450 metabolism of acetaminophen in the liver . 18 4.1 Model design process . 30 4.2 Drug interactions matrix . 32 5.1 Confusion matrix for QSAR model . 41 5.2 Summary of confusion matrices . 42 5.3 DDI network . 57 6.1 Indirect interactions . 65 x List of Abbreviations β-NADH β-Nicotinamide Adenine Dinucleotide ADMET Absorption, distribution, metabolism, excretion and transport ADR Adverse Drug Reaction AERS Adverse Event Reporting System ANOVA Analysis of Variance ATCC American Type Culture Collection BSA Bovine Serum Albumin CDK Chemistry Development Kit DDI Drug-Drug Interaction DMEM Dulbecco's Modified Eagle's Serum DT Decision Trees FBS Fetal Bovine Serum xi FDA Food and Drug Administration IPF Interaction Profile Fingerprint kNN Kappa Nearest Neighbour LDH Lactate Dehydrogenase NAPQI N-acetyl-p-benzoquinone-imine NCE New Chemical Entity OvR One-Vs.Rest P450 Cytochrome P450 PB Phosphate Buffer PBS Phosphate Buffered Saline PCA Principal Components Analysis QSAR Quantitative Structure-Activity Relationship RBF Radial Basis Function RF Random Forest SDS-PAGE Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis SRS Spontaneous Reporting System xii SVM Support Vector Machines TBS-T Tris-Buffered Saline and Tween 20 WEKA Waikato Environment for Knowledge Analysis xiii 1 Chapter 1 Introduction 1.1 Motivation Bringing a new therapeutic drug to market is expensive and time consuming. In the United States, bringing a single drug to market typically takes 10-15 years, and its cost can range from $800 million to $1 billion. For every one drug approved, there have been up to 10,000 drugs rejected and even after a drug has made it to market, there is still a potential for it to be removed [40]. Every year over 2 million people are affected by an adverse drug reaction (ADR) [24], out of which 26% can be attributed to drug-drug interactions (DDI) [29]. Elderly and cancer patient groups specifically, are at a higher risk from DDIs because they are administered multiple medications. Identifying DDIs early in the drug discovery and development process can reduce the number of hospitalizations due to drug in- teractions and allow more beneficial drugs to reach the market more quickly and at a lower cost. 1.2. PROBLEM 2 1.2 Problem Unfortunately, DDIs are difficult to test for due to their complex nature. Drugs are known to interact through a diverse array of mechanisms with the additional complication of affecting several different organs, tissues and pathways. This not only makes testing for all possible interactions difficult, but the prediction of DDIs is also often generalised to one mechanism of interaction, further limiting the discovery of new interactions. Current computational methods in predicting DDIs are aimed at expediting and enhancing the drug discovery and development process and offer several approaches. Various computational approaches have shown to be successful, but similar to in vitro and in vivo methods are often limited to only one of the many possible ways that drugs can interact. 1.3 Hypothesis It was hypothesized that computing could be used to build a classification model that predicts DDIs by analyzing and comparing the structure of drugs. Specifically, using a data set of molecular descriptors, computer modeling may be used to identify structural properties of drugs that lead to DDIs. Furthermore, it was hypothesized that pairs of drugs that were predicted to interact would lead to a change in cell death in vitro. 1.4. OBJECTIVES 3 1.4 Objectives A model based on the principle that structure determines function may present a solu- tion that encompasses multiple drug interaction mechanisms by stripping underlying DDIs to the molecular level.
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