Automatic Generation of Drug Metabolic Pathways from ADME Ontology on OWL-DL

1. 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

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

3. Issues • Detection of Personal Genome Variation • Inference of Drug-Drug Interactions on Multiple Dose • Quantitative Analysis by Drug Metabolic Pathway Simulation

4. What is a pathway?

5. Pathway KEGG

6. Metabolic Pathway http://www.expasy.org/cgi-bin/show_thumbnails.pl

7. Typical Pathway Analogy

8. Static Approach Dynamic Approach Generated from primitive reactions depending on “Trigger” Trigger Approaches for Metabolic Pathway Models A Priori Defined Body/Cell Primitives KEGG http://www.genome.jp/kegg/

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

10. How many colors can you see in rainbow?

11. Real Rainbow Color All the colors you can see with your own eyes! to 760 nm～830 nm From 360 nm～400 nm

12. Explicit Knowledge of Colors Purple Indigo Blue Green Yellow Orange Red

13. Ontology for Rainbow Colors RGB Value Purple #800080 Indigo #000080 Blue #0000FF Green #008000 Yellow #FFFF00 Orange #FF8000 Red #FF0000

14. Which are Purple? #800050 #700080 #600080 #800080 #800060 #800070 #500080

15. Color Representation by Primitives RGB Representation • R: 700nm, • G: 546.1nm, • B: 435.8nm. Purple Red ? ??? 360nm 830nm

16. Ontology Schema

17. 基本要素インスタンスによる静的クラス（continuants)と動的クラス(process)の統合基本要素インスタンスによる静的クラス（continuants)と動的クラス(process)の統合 「プロセス」を定義するために必要十分な「物」の関係 Trigger (SN-38@lever) Situated (Carboxyl esterase@lever) Process (Irinotecan-SN38 Metabolism@lever) Resultant (SN-38@lever)

18. 基本要素インスタンスの重合による現実世界の記述基本要素インスタンスの重合による現実世界の記述 薬相互作用オントロジーでの例（吉川、有熊、小長谷、２００６）

19. Prototype System

20. Controlled Vocabulary

21. Generated Pathway

22. Detected Drug Interactions

23. Ontology-driven Hypothetical Assertion

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.

25. Future Works • Expansion of Drug Interaction Ontology • Automatic Generation of ADME models • Integration of Drug Interaction Ontology and ADME simulation

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. )

27. ご静聴ありがとうございました。 Thank You for Listening