Making semantics work in drug discovery

Transcription

1 Indiana University School of Informatics and Computing Making semantics work in drug discovery Information is cheap. Understanding is expensive (Karl Fast) David Wild, Assistant Professor and Director, Cheminformatics & Chemogenomics Research Group (CCRG) Indiana University School of Informatics and Computing

2 Stack of applying semantics in drug discovery & healthcare New biomedical insights Integrated knowledge discovery processes Integrative tools and algorithms Accessible networks of semantically integrated data Only now going mainstream Wild, D.J., Ding, Y., Sheth, A.P., Harland, L., Gifford, E.M., Lajiness, M.S. Systems Chemical Biology and the Semantic Web: what they mean for the future of drug discovery research, Drug Discovery Today, 2012, 17,

3 Chem2Bio2RDF.org semantically integrated data Chen, B., Dong. X., Jiao, D., Wang, H., Zhu, Q., Ding, Y., Wild, D.J. Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinformatics 2010, 11, 255.

4 We can answer many questions with SPARQL What pathways will troglitazone affect? PREFIX c2b2r: < chem2bio2rdf.owl#> PREFIX bp: < PREFIX rdfs: < PREFIX rdf: < select distinct?pathwayname?datasource from < where {?chemical rdfs:label "Troglitazone"^^xsd:string; c2b2r:hasinteraction?interaction.?interaction c2b2r:hastarget?target ; c2b2r:biologicalinterest true.?pathway c2b2r:ispathwayof?target ; bp:name?pathwayname ; bp:xref [c2b2r:identifiertype?datasource]. } Drug Gene Pathway

5 We can answer many questions with SPARQL What are possible multi-target MAPK Inhibitors? PREFIX pubchem: < resource/> PREFIX kegg: < PREFIX uniprot: < resource/> SELECT?compound_cid (count(?compound_cid) as? active_assays) FROM < FROM < FROM < Compound Bio- Assay WHERE {?bioassay pubchem:cid?compound_cid.?bioassay pubchem:outcome?activity. FILTER (? activity=2).?bioassay pubchem:score?score. FILTER (?score>50).?bioassay pubchem:gi?gi.?uniprot uniprot:gi?gi.?pathway kegg:protein?uniprot.?pathway kegg:pathway_name?pathway_name. FILTER regex(?pathway_name,"mapk signaling pathway","i"). Gene Gene } GROUP BY?compound_cid HAVING (count(*)>1) Pathway

6 Variety of expert GUI tools for searching

7 Stack of applying semantics in drug discovery & healthcare New biomedical insights Integrated knowledge discovery processes Integrative tools & algorithms Very little work done in this area Accessible networks of semantically integrated data Wild, D.J., Ding, Y., Sheth, A.P., Harland, L., Gifford, E.M., Lajiness, M.S. Systems Chemical Biology and the Semantic Web: what they mean for the future of drug discovery research, Drug Discovery Today, 2012, 17,

8 ChemBioSpace Association Search He, B., Tang, J., Ding, Y., Wang, H., Sun, Y., Shin, J.H., Chen, B., Moorthy, G., Qiu, J., Desai, P., Wild, D.J., Mining relational paths in biomedical data. PloS One, 2011, 6(12), e27506.

9 Semantic Linked Association Prediction Association score: Association significance: 9.06 x 10-6 => missing link predicted

10 SLAP significant subgraph Chen, B., Ding, Y., Wild, D.J. Assessing Drug Target Association using Semantic Linked Data. PLoS Computational Biology, 2012, 8(7), e

11 Compound/Target SLAP Virtual Screen - Troglitazone (C) 2012 DATA2DISCOVERY INC 11

12 SLAP Drug-Target Prediction Matrix

13 Bipartite repurposing graph created with Sci2

14 Assessing drug similarity from biological function Took 157 drugs with 10 known therapeutic indications, and created SLAP profils against 1,683 human targets Pearson correlation between profiles > 0.9 was used to create associations between drugs Drugs with the same therapeutic indication unsurprisingly cluster together also subcluster by MOA Some drugs with similar profile have different indications potential for use in drug repurposing?

15 Large-scale repurposing networks (C) 2012 DATA2DISCOVERY INC 15

16 Repurposing example anticonvulsant antiarrhythmic anticonvulsant antidepressive H1 antihistamine alpha / beta blocker used for CHF (C) 2012 DATA2DISCOVERY INC 16

17 Stack of applying semantics in drug discovery & healthcare New biomedical insights Integrated knowledge discovery processes What is the added value? Integrative tools & algorithms Accessible networks of semantically integrated data Wild, D.J., Ding, Y., Sheth, A.P., Harland, L., Gifford, E.M., Lajiness, M.S. Systems Chemical Biology and the Semantic Web: what they mean for the future of drug discovery research, Drug Discovery Today, 2012, 17,

18 Integrative virtual screening Ligand-based screening: QSAR, similarity, pharmacophore Structure-based screening: Molecular docking Semantic screening: Semantic association with targets and/or known ligands Look at top hits using each method, and fused hits using harmonic data fusion of ranked lists Currently being validated in PXR (Univ. Cincinnati) and Mtb (OSDD) projects) Pharmacophore Forest Random ROCS SLAP Fusion Pharmacophore Random Forest ROCS SLAP See Bioorg Med Chem Lett.2012 May 1;22(9): Fusion 1

19 MOA: Identifying cardiac side effects of Rosiglitazone Gene/Drug SAA2 APOE ADIPOQ CYP2C8 Strong Discussed PharmGKB Strong Discussed PharmGKB + Matador Strong Positive PharmGKB Strong Changes metabolism (CTD) Rosiglitazone Troglitazone Pioglitazone V. weak V. weak V. weak V. weak V. weak Strong Positive PharmGKB V. Weak Strong Changes metabolism (CTD)

20 MOA: Identifying differential LDL-lowering effect of Troglitazone

21 Parkinson Disease-Inflammation Network

22 Integrating phenotypic assays Wnt pathway Associated Assay Phenotypic Assay Resveratrol Known assay data Associated targets 22 (C) 2012 DATA2DISCOVERY INC

23 Tool prototypes: djwild.info / d2discovery.com

24 Semantic Technologies in Drug Discovery Most commercial organizations are still in the early adopter phase: with a big data integration problem and realizing semantic technologies are a better solution to this than relational databases Some companies are in the bowling alley and are moviong out of this phase. No-one is in the tornado Research (OpenPHACTS, etc) is well on the way to solving the data integration problem and is moving on to advanced searching, data mining and prediction

25 Google Knowledge Graph

26 Lessons & thoughts: adoption of semantics Semantic search is going mainstream Google Knowledge Graph, Facebook Graph Search, Linked Open Data (LOD), OpenPHACTS Now identified as top technology trend for 2013 (gartner.com/newsroom/id/ ) Everyone has a big data integration problem. Semantics now work well for this. Many pharma companies have semantic pilot programs but no-one has gone mainstream with semantics. However this is probably not far off. Switching from relational to semantic models seems revolutionary but can be done in an evolutionary fashion (D2R, etc), although there are some capacity issues and limitations with this approach. We need support for horizontal research in semantic prediction and data mining Based on huge hetergeneous graphs application of existing and new graph algorithms Very little work has been done so far on semantic prediction using heterogeneous, semantic graphs most work is siloed in graph theory, data mining, communities We need support for vertical research in big data / networks / semantics for translational medicine and drug discovery Semantic prediction, using all available data, shows strong promise for utility in areas such as drug-target prediction, off-target profiling, and drug repurposing. Semantics might have rapid adoption in healthcare (EMR s, PHR), and it should be important to be able to integrate from the molecular to patient level. Need to keep good alignment between disciplines Academic-Industry cross-silo colloabration essential: EU OpenPHACTS good example of success