TOWARDS UNDERSTANDING MODE-OF-ACTION OF TRADITIONAL MEDICINES BY USING IN SILICO TARGET PREDICTION Siti Zuraidah Binti Mohamad Zobir Hughes Hall April 2017 This dissertation is submitted for the degree of Doctor of Philosophy of the University of Cambridge Supervisor: Dr Andreas Bender Name: SITI ZURAIDAH BINTI MOHAMAD ZOBIR Title: IN SILICO TARGET PREDICTION: TOWARDS UNDERSTANDING MODE-OF-ACTION OF TRADITIONAL MEDICINES Abstract Traditional medicines (TM) have been used for centuries to treat illnesses, but in many cases their modes-of-action (MOAs) remain unclear. Given the increasing data of chemical ingredients of traditional medicines and the availability of large-scale bioactivity data linking chemical structures to activities against protein targets, we are now in a position to propose computational hypotheses for the MOAs using in silico target prediction. The MOAs were established from supporting literature. The in silico target prediction, which is based on the “Molecular Similarity Principle”, was modelled via two models: a Naïve Bayes Classifier and a Random Forest Classifier. Chapter 2 discovered the relationship of 46 traditional Chinese medicine (TCM) therapeutic action subclasses by mapping them into a dendrogram using the predicted targets. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase non-receptor type 2 (PTPN2) and digestive system such as mineral absorption. Two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif. Chapter 3 compared the chemical and bioactivity space of 97 anti-cancer plants’ compounds of TCM, Ayurveda and Malay traditional medicine. The comparison of the chemical space revealed that benzene, anthraquinone, flavone, sterol, pentacyclic triterpene and cyclohexene were the most frequent scaffolds in those TM. The annotation of the bioactivity space with target classes showed that kinase class was the most significant target class for all groups. From a phylogenetic tree of the anti-cancer plants, only eight pairs of plants were phylogenetically related at either genus, family or order level. Chapter 4 evaluated synergy score of pairwise compound combination of Shexiang Baoxin Pill (SBP), a TCM formulation for myocardial infarction. The score was measured from the topological properties, pathway dissimilarity and mean distance of all the predicted targets of a combination on a representative network of the disease. The method found four synergistic combinations, ginsenoside Rb3 and cholic acid, ginsenoside Rb2 and ginsenoside Rb3, ginsenoside Rb3 and 11-hydroxyprogesterone and ginsenoside Rb2 and ginsenoside Rd agreed with the experimental results. The modulation of androgen receptor, epidermal growth factor and caspases were proposed for the synergistic actions. Altogether, in silico target prediction was able to discover the bioactivity space of different TMs and elucidate the MOA of multiple formulations and two major health concerns: cancer and myocardial infarction. Hence, understanding the MOA of the traditional medicine could be beneficial in providing testable hypotheses to guide towards finding new molecular entities. i This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee. ii Acknowledgements I would like to gratefully acknowledge various people who have been helping me throughout my journey of producing this thesis. Firstly, I would like to express my sincerest appreciation to my supervisor, Dr Andreas Bender for the opportunity to be part of his group and all his guidance and deep insights at each step of my study. I’ve learned extensively from him including how to critically approach a research problem, make a rational decision and present a good research output. Special thanks should be given to Dr. Tai-Ping Fan for the tremendous help in explaining the concept of traditional Chinese medicine (TCM) and pharmacology. Also, to Dr Xian Jun Fu, a visiting researcher from China, for his input on TCM and Ranjoo Choi for her patience and dedication to run the experiments in the lab to validate TCM compound combinations. I want to thank past members of the group, Fazlin Mohd Fauzi, Gergios Drakakis and Sonia Linggi for their helps during the challenging first year to adapt to this field of cheminformatics. Many thanks go out to Avid Afzal, Azedine Zoufir and Lewis Mervin for answering all my statistics-related questions. I would also like to thank Deszo Modos for the biological network tutorial sessions and Krishna Bulusu for the thoughtful comments on my research projects. A thanks also goes to Fatima Baldo for proofreading the thesis. Not to forget, to all members in the group, thank you for all the helps I have received. The group has been a source of good advice, diverse leaning experience, and friendships. I would like to express my appreciation to Susan Begg and Chloe Baker for being so helpful and providing their administrative assistance. I also appreciate the financial support of Majlis Amanah Rakyat (Malaysia) and Malaysian Institute of Pharmaceuticals (IPharm). Finally, I would like to thank my family for all their love and encouragement. To both my parents, thank you for the annual visit to Cambridge and to all my siblings, thank you for all practical support in all those things beyond doing a PhD. I’m also grateful for many new friends I have made during these four years. Thank you for the fun we had in Cambridge or during our travels to several European countries. iii Correspondence to previous publications One part of this thesis has been published previously in refereed journals. Please find the article corresponding to the chapter of this thesis below. Chapter 2: Zobir, S. Z. M.; Fauzi, F. M.; Liggi, S.; Drakakis, G.; Fu, X.; Fan, T.-P.; Bender, A., Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes. Evidance-Based Complementary and Alternative Medicine 2016, 2016, 25 pages iv Contents Abstract i Acknowledgements iii CHAPTER 1: Introduction 1.1 A brief history of traditional medicine 2 1.2 Natural products in drug discovery 2 1.3 Approaches to identify modes-of-action of natural products 4 1.4 Ligand-based in silico target prediction 6 1.4.1 The principle behind ligand-based in silico target prediction 1.4.2 Applications of in silico target prediction 1.4.3 Other methods of in silico target prediction 1.5 Systems biology to elucidate modes-of-action 13 1.6 Limitations of in silico target prediction 16 1.7 Structure of this thesis 17 CHAPTER 2: Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improve Mechanistic Understanding and Discovers Relationships Between Therapeutic Action (Sub-)Classes 2.1 Introduction 18 2.2 Materials and Methods 21 2.3 Results and Discussions 27 2.4 Conclusion 49 CHAPTER 3: Exploring the Chemical Space and Bioactivity Space of Traditional Medicine from China, India and Malaysia for Treating Cancers 3.1 Introduction 50 3.2 Materials and Methods 53 3.3 Results and Discussions 61 3.4 Conclusion 80 CHAPTER 4: Evaluating Synergistic Pairwise Combinations of Shexiang Baoxin Pill (SBP) For Coronary Heart Disease From Network Topology 4.1 Introduction 81 4.2 Materials and Methods 84 4.3 Results and Discussions 94 4.4 Conclusion 114 CHAPTER 5: Concluding Remarks 115 References 117 Appendices 131 1 Chapter 1: Introduction 1.1 A brief history of traditional medicine Humanity is dependent on natural products and has been so for millennia. These plants, animal parts and minerals are used both to treat many diseases (1, 2). Indeed, the knowledge of curative plants may be traced back at least 60,000 years where remains of medicinal plants such as opium poppies, ephedra, and cannabis were documented during archaeological excavations of Neanderthals burial sites at Shanidar in Iraq (3). The discovery of natural products with healing properties posed a massive challenge to early humans. Empirical discoveries of both healing and harmful properties of natural products resulted from trial and error, which can be fatal when experimenting poisonous natural products. Progressively, the knowledge of empirical discoveries was collected in the form of traditional medicine (4). The first written record of the use of natural products dates from 2600 BC of Mesopotamia, in which hundreds of clay tablets in cuneiform described, among others, the oils of Cedrus species (cedar) and Cupressus sempevirens (cypress), Glycyrrhiza glabra (licorice), Commiphora species (myrrh), and Papaver somniferum (poppy juice) were used for treating cold, cough, and inflammation (2, 4, 5). “Ebers Papyrus”, an ancient Egyptian pharmaceutical record (1500 BC), documented not only plants but also animal parts and minerals to constitute its 700 drugs (2, 4, 5). In other parts of the world, the ancient Greeks also documented the compilation of the uses of natural products for medicinal purposes (2, 4, 5). In Asia, traditional Chinese medicine (TCM) and Ayurveda (India) have been developed. These are two of the most extensively documented traditional medicines and so are considered in detail in this study (2, 4, 5).