1. 1 Using the Protein Ontology The view from the outside… Sirarat Sarntivijai 1, Yongqun He 2,3, Brian D. Athey 3, and Darrell R. Abernethy 1 1 Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, MD 20993, 2 Unit of Laboratory Animal Medicine, Department of Microbiology and Immunology, 3 Department of Computational Medicine and Bioinformatics, University of Michigan, MI 48109

2. This presentation reflects the views and perspectives of the authors and should not be construed to represent the FDA’s views or policies. 2

3. 3 Jane P.F. Bai and Darrell R. Abernethy Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization, Annu.Rev.Pharmacol. Toxicol. 2013, 53:22.1-22.23 Ontologies to assist communication and processing between layers of information

4. 03/28/14 Drug Safety Data Warehouse (DSDW) - Database - Method - Tool Data vendor -Clinical trials - Pharma-owned DBs -LORIS,… Hypothesis of Drug-AE Mechanism -DSDW -Mechanism Interaction Map - Ont-assisted Mapper, BIO2RDF? Drug-AE Validation - N/A (read results) - Manual curation - Human expert analysts Preclin./Clin. Data Analysis -NDAs, PharmGKB, PharmaData, - Integrative by tF honest broker - Multiple/ TBD Chem. Structure Analysis -SRS/ID, MOAD, TBD - QSAR,Integrative - SeaChange/ TBD Non-clin. Molec. Interaction Analysis -Multiple - NLP/Centrality, others TBD - Multiple/ TBD PK/PDPBPKPG Animal model Gene-Gene/Prot Interactions Proteomics Metabolomics Epigenetics/ Epigenomics Visualization tools Signal Detection -FAERS, EHR, - PRR, EBGM - MASE, Empirica Non-clin. Molec. Interaction Analysis -Multiple - NLP/Centrality, others TBD - Multiple/ TBD

5. Each type of data is described by a specific ontology. These ontologies are governed by the same upper-level guideline (OBO foundry) so they can be linked together via ontology mapping method 5 Jane P.F. Bai and Darrell R. Abernethy Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization, Annu.Rev.Pharmacol. Toxicol. 2013, 53:22.1-22.23 Drug Bank (CA) ChEBI PRO GO Pharm- GKB INO UBERON CL CLO OAE MedDRA HPO

6. 6 OAE-MedDRA term reorganization AEcountPRRCI PRR Diarrhoea28146.095.89 - 6.31 Nausea16441.951.86 - 2.04 Vomiting13422.52.37 - 2.63 Rash12423.673.48 - 3.88 Dehydration10715.955.60 - 6.31 Dyspnoea10301.811.70 - 1.92 Fatigue9871.881.76 - 1.99 Pyrexia9122.172.04 - 2.32 Death6340.980.91 - 1.06 Infusion related reaction62111.4610.58 - 12.42 Neutropenia5985.545.11 - 6 Asthenia5961.461.35 - 1.58 Hypotension5932.512.32 - 2.72 Abdominal pain5111.921.76 - 2.09 Pneumonia4851.781.63 - 1.94 Mucosal inflammation46116.2214.75 - 17.84 Febrile neutropenia4236.996.35 - 7.70 Anaemia4191.991.81 - 2.19 Malignant neoplasm progression 4135.985.43 - 6.59 Disease progression4123.983.61 - 4.38

7. 7 TKI-cardiotox study with OAE -TKI-cardiotox molecular mechanism is not known as there are many factors that affect the mechanism. -Understanding such mechanisms to predict cardiotoxicity requires knowledge derived from heterogeneous data that need to be linked together. -Building ontological infrastructure to lay down this integrative framework is essential.

8. Linking AEs to proteins of mechanism 8

9. 9 mitogens growth factor receptors** PI3K (PIK3CA) AKT (AKT1) mTOR PTEN NEU PIM1 GSK3 pro-apoptotic factors autophagy JAK/STAT signaling pathway cell cycle progression, cell proliferation cell death MAPK1 EGF EGFR* NRG1ERBB2* ERBB4*MIRN146A TLR4 ICAM1 PARP1HSPA1A JUNABL1* JAK* STAT IL-1 TNF P P Sarntivijai et al., unpublished **VEGFRs, PDGFRß PR_00000 0103 PR_00000 6933 PR_0000020 82 PR_00000 7160 PR_00000 1155 PR_00000 1467 PR_00000 1091 PR_00000 0033 PR_00001 2289 PR_00000 8871 PR_00002 8746 PR_00001 2719 PR_00002 9189 PR_00003 5899 PR_00001 2732 PR_00000 2082 PR_00002 5748 PR_00000 1933 PR_00000 1812 PR_00002 9649 PR_00000 3578 PR_00000 3041

10. Knowledge Integration with OAE - example of data infrastructure network from direct import and intermediate mapping arterial disorder AE arteriosclerosis coronary artery AE myocardial infarction AE cardiac disorder AE heart heart layer myocardium mesoderm-derived structure organ component layer cardiovascular disorder AE adverse event is_a located_in is_evidence_of part_of is_a located_in necrotic cell death relates_to* cell deathdeath single-organism process biological process is_a Ontology of Adverse Events Uber Anatomy Ontology Gene Ontology is_a single-organism cellular process is_a cellular process is_a Sarntivijai et al., unpublished

11. Discussion Gene-gene interaction = protein-protein interaction? –NO. Also, how do we validate the free data as genes or proteins? What are the associations between the two? What about post-translational modification? Can PR capture this information in data linking? –Also need post-transcriptional event information –Proteome over transcriptome How to make the connection from gene interaction level to protein interaction level – to understand both normal and disease states? –What information is missing? --- dynamic metabolome, PTM, what else? –A -> B -> C is not necessarily A -> C –Not all abnormalities -> disease Animal model != human Ontology development for clinical information –De factoVStop-down backward curation 11

12. Reactome annotation of a normal cell process Reactome annotation of a disease process Reactome annotation of an AE process in relation to underlying disease and *any* drugs taken by the patient. TIME is needed to understand the *progress*. –AEs are causally inconclusive. They may or may not have anything to do with the disease, the medicine(s) taken; or, they may have everything to do with the disease and/or the medicine(s). –The only attribute defining an AE is the temporal association to the drug(s) taken. Information of normal/disease protein activities can add clarity /OR/ confusion to the knowledge discovery process May (very likely) need to consider environmental factors to understand protein-disease-clinical phenotype activities –But, how? Human data are sparse. Interspecies knowledge is essential, especially in the domain of pharmacology. –EHRs may offer a lot of information, but lack of consensus to the drug- AE causal association makes it very challenging to use the data. 12

13. 13 Acknowledgement FDA Dr. Darrell Abernethy Dr. Keith Burkhart Dr. Jihong Shon Dr. Elizabeth Blair NIH/NCI Dr. Lori Minasian Bogazici University (Turkey) Dr. Arzucan Ozgur University of Michigan Dr. Brian Athey Dr. Gilbert Omenn Dr. Yongqun He Dr. Junguk Hur Allen Xiang Shelley Zhang Desikan Jagannathan

14. 14 Thank you