Automatic Generation of Drug Metabolic Pathways from ADME Ontology on OWL-DL Konagaya Akihiko RIKEN Genomic Sciences Center Project Director Advanced Genome.

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    13-Dec-2015

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Automatic Generation of Drug Metabolic Pathways from ADME Ontology on OWL-DL Konagaya Akihiko RIKEN Genomic Sciences Center Project Director Advanced Genome Information Technology Research Group Slide 2 Motivation Coming of Personalized Genome Era Polymorphism in Drug Response Genes Detection of Drug-Drug Interaction In silico prediction of individual differences in drug response and drug-drug interactions on multiple dose Slide 3 Issues Detection of Personal Genome Variation Inference of Drug-Drug Interactions on Multiple Dose Quantitative Analysis by Drug Metabolic Pathway Simulation Slide 4 What is a pathway? Slide 5 Pathway KEGG Slide 6 Metabolic Pathway http://www.expasy.org/cgi-bin/show_thumbnails.pl Slide 7 Typical Pathway Analogy Slide 8 Primitives Body/Cell Approaches for Metabolic Pathway Models Static Approach Dynamic Approach Generated from primitive reactions depending on Trigger Trigger KEGG http://www.genome.jp/kegg/ A Priori Defined Slide 9 Why Dynamic Approach? Combinatorial Explosion of Molecular Pathways Integration of Continuants and Processes on Primitive Molecular Interactions Representation of Pathways as Aggregation of Primitive Molecular Events Slide 10 How many colors can you see in rainbow? Slide 11 Real Rainbow Color All the colors you can see with your own eyes! From 360 nm 400 nm to 760 nm 830 nm Slide 12 Explicit Knowledge of Colors Red Yellow Green Blue Indigo Purple Orange Slide 13 Ontology for Rainbow Colors #800080 RGB Value #000080 #0000FF #008000 #FFFF00 #FF8000 #FF0000 Red Yellow Green Blue Indigo Purple Orange Slide 14 Which are Purple? #800070 #800060 #500080 #800050#700080 #600080 #800080 Slide 15 Color Representation by Primitives R: 700nm, G: 546.1nm, B: 435.8nm. RGB Representation ? Purple Red 360nm830nm ??? Slide 16 Ontology Schema Slide 17 continuants) (process) Trigger (SN-38@lever) Situated (Carboxyl esterase@lever) Resultant (SN-38@lever) Process (Irinotecan-SN38 Metabolism@lever) Slide 18 Slide 19 Prototype System Slide 20 Controlled Vocabulary Slide 21 Generated Pathway Slide 22 Detected Drug Interactions Slide 23 Ontology-driven Hypothetical Assertion Slide 24 Conclusion Drug Interaction Ontology can be represented by OWL-DL in terms of processes, continuants and events. Drug metabolic pathways can be dynamically generated by the aggregation of primitive molecular events with OWL-DL and Prolog. Drug interaction can be detected by logical inference and mapped onto drug interaction ontology. Slide 25 Future Works Expansion of Drug Interaction Ontology Automatic Generation of ADME models Integration of Drug Interaction Ontology and ADME simulation Slide 26 Acknowledgement Sumi Yoshikawa RIKEN GSC Ryuzo Azuma RIKEN GSC Takeo Arikuma Tokyo Institute of Technology Kentaro Watanabe Tokyo Institute of Technology (Hitachi Ltd., Japan. ) Kazumi Matsumura RIKEN GSC (DAIICHI PURE CHEMICALS CO., LTD., Japan. ) Slide 27 Thank You for Listening

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