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Using the Protein OntologyThe view from the outside…

Sirarat Sarntivijai1, Yongqun He2,3,Brian D. Athey3, and Darrell R. Abernethy1

1Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, MD 20993, 2Unit of Laboratory Animal Medicine, Department

of Microbiology and Immunology, 3Department of Computational Medicine and Bioinformatics, University of Michigan, MI 48109

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

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3Jane P.F. Bai and Darrell R. AbernethySystems 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

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. StructureAnalysis

-SRS/ID, MOAD, TBD- QSAR ,Integrative

- SeaChange/ TBD

Non-clin. Molec.Interaction Analysis

-Multiple

- NLP/Centrality, others TBD- Multiple/ TBD

PK/PD PBPK PG

Animal model

Gene-Gene/ProtInteractions

Proteomics

MetabolomicsEpigenetics/Epigenomics

Visualizationtools

Signal Detection

-FAERS, EHR, - PRR, EBGM

- MASE, Empirica

Non-clin. Molec.Interaction Analysis

-Multiple

- NLP/Centrality, others TBD- Multiple/ TBD

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

5Jane P.F. Bai and Darrell R. AbernethySystems 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

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OAE-MedDRA term reorganization

AE count PRR CI PRR

Diarrhoea 2814 6.09 5.89 - 6.31

Nausea 1644 1.95 1.86 - 2.04

Vomiting 1342 2.5 2.37 - 2.63

Rash 1242 3.67 3.48 - 3.88

Dehydration 1071 5.95 5.60 - 6.31

Dyspnoea 1030 1.81 1.70 - 1.92

Fatigue 987 1.88 1.76 - 1.99

Pyrexia 912 2.17 2.04 - 2.32

Death 634 0.98 0.91 - 1.06

Infusion related reaction 621 11.46 10.58 - 12.42

Neutropenia 598 5.54 5.11 - 6

Asthenia 596 1.46 1.35 - 1.58

Hypotension 593 2.51 2.32 - 2.72

Abdominal pain 511 1.92 1.76 - 2.09

Pneumonia 485 1.78 1.63 - 1.94

Mucosal inflammation 461 16.22 14.75 - 17.84

Febrile neutropenia 423 6.99 6.35 - 7.70

Anaemia 419 1.99 1.81 - 2.19

Malignant neoplasm progression 413 5.98 5.43 - 6.59

Disease progression 412 3.98 3.61 - 4.38

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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.

Linking AEs to proteins of mechanism

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mitogensgrowth factorreceptors**

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*

NRG1 ERBB2*

ERBB4* MIRN146A

TLR4

ICAM1

PARP1 HSPA1A

JUN ABL1*

JAK*

STAT

IL-1

TNF

P

P

Sarntivijai et al., unpublished**VEGFRs, PDGFRß

PR_000000103

PR_000006933

PR_000002082

PR_000007160

PR_000001155

PR_000001467

PR_000001091

PR_000000033

PR_000012289

PR_000008871

PR_000028746

PR_000012719

PR_000029189

PR_000035899

PR_000012732

PR_000002082

PR_000025748

PR_000001933

PR_000001812

PR_000029649

PR_000003578

PR_000003041

Knowledge Integration with OAE - example of data infrastructure network from direct import and intermediate mapping

arterial disorder AE

arteriosclerosiscoronary artery AE

myocardialinfarction AE

cardiac disorder AE

heart

heart layer

myocardium

mesoderm-derivedstructure

organ componentlayer

cardiovasculardisorder AE

adverse event

is_a

is_ais_a

is_a

is_a

located_in

is_evidence_of

part_of

part_of is_a

is_a

located_in

necrotic cell death

relates_to*

cell death death

single-organism process

biological process

is_a is_a

is_a

is_a

Ontology ofAdverse EventsUber Anatomy

Ontology

Gene Ontology

is_a

single-organism cellularprocess

is_a

cellular process

is_ais_a

is_a

Sarntivijai et al., unpublished

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 facto VS top-down backward curation

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• 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.

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AcknowledgementFDA• 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

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Thank you