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Open PHACTS webinar June 2016 - Data2Discovery


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OpenPHACTS: Maximizing Impact for Pharmaceutical Applications

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Open PHACTS webinar June 2016 - Data2Discovery

  1. 1. DATA2DISCOVERY OpenPHACTS: Maximizing Impact for Pharmaceutical Applications DAVID WILD, PhD. CEO Data2Discovery Inc Associate Professor and Director of Data Science, Indiana University - - DATA SCIENCE DATA2DISCOVERY 1
  2. 2. DATA2DISCOVERY Data2Discovery Inc •  Finding insights from proprietary, public and commercial data together in ways never tried before •  Linking data from molecular to human •  Key expertise in using OpenPHACTS data •  Partnering with customers to implement this at scale •  Phenotypic drug discovery is a beachhead application • 2
  3. 3. DATA2DISCOVERY Data2Discovery Application Areas Pharmaceutical Research Healthcare Delivery Public Health Precision Medicine Population Health Real World Evidence Combination therapy Outcome Analytics Nudge Economics Quantified Self Performance-based Pricing Risk Adjustment Phenotypic Drug Discovery Adverse Event Prediction 3
  4. 4. DATA2DISCOVERY IT Infrastructure Re-Imagined 4 What effect will inhibiting PPAR-γ have on HER+ breast cancer tissue? Interaction Infrastructure (II) Custom interfaces and applications built using reusable components Computation Infrastructure (CI) Fast, in memory computation modules that work on heterogeneous data sub-graphs Linked Data Ecosystem (LDE) Data APIs, MicroTranslators, Live Triple Stores, Ontologies & Standards What adverse effects could Cymbalta cause in patients with Chron’s?
  5. 5. DATA2DISCOVERY Case study – Eli Lilly •  Linked data ecosystem: Developed live Data Translator that maps key internal assay data into OpenPHACTS triple store, annotated by public ontologies (e.g. BAO) •  Computation/Interaction infrastructure: Contextual inquiry in high impact end-user applications across the organization 5
  6. 6. DATA2DISCOVERY Method Target UniProt ChEMBL Assay Internal Assay Data ChEMBL Compd Semantic Schema Assay Result Type ChEMBL Cell Line Gene Cell Line Mapping Internal and External Assay Data 6
  7. 7. DATA2DISCOVERY Phenotypic Drug Discovery •  “Beachhead” application •  Subject of strong Pharma interest (OpenPHACTS researchathon Spring 2015) •  Crosses the big two domains –  Chemistry & biology (molecular) –  Study of people (patient) •  Need to find links across many datasets –  Preclinical, toxicology, clinical trials, post-market 7
  8. 8. DATA2DISCOVERY P3 – Predictive Phenotypic Profiler •  P3 is a product in development by Data2Discovery that is designed to address current gaps in maximizing impact of phenotypic assay data. Intended applications include: –  Target deconvolution –  Target-based mechanism of action discovery –  Identifying similarities between phenotypic assays •  These use cases were identified in OpenPHACTS Phenotypic Screening Workshop in February 2015 •  Identifies associations using SEMAPTM association finding technology •  Links OpenPHACTS data into other key data resources 8
  9. 9. DATA2DISCOVERY P3 Architecture •  Componentized architecture –  Permits plug-and-play of datasets –  Allows new applications to be quickly developed •  Highly scalable –  Uses cloud technology, fast graph databases and Apache SPARK •  Secure deployments where needed –  Can be used as external software-as service –  Can be deployed behind firewalls (Docker images) 9
  10. 10. DATA2DISCOVERY What types of data can P3 use? •  P3 can link public and proprietary data sources and cross pre- clinical and clinical data. Example types of data include: –  Enzymatic assay (compound-gene/target) –  Phenotypic assay (compound-phenotype) –  Cellular assay (compound-cell line) –  Gene expression (e.g. LINCS, L1000) –  Pathway (pathway-gene) –  Molecular-Phenotype links (compound, gene, pathway, cell line to disease state, genotype, patient phenotype, adverse event, electronic medical record, real world evidence etc) 10
  11. 11. DATA2DISCOVERY SEMAP™ - Semantic Association Prediction •  Based on research at Indiana University •  Predicts association based on data subnetworks between points of interest •  Can predict drug-target interaction across thousands of genes •  No scope or bias problems •  Extensive external validation including comparison with SEA and use for predicting gene expression profiling (see Chen, B. et al., PLoS Computational Biology, 2012, 8(7), e1002574) •  Demonstrated applications in drug repositioning, MOA discovery •  Now being used in phenotypic target deconvolution and MOA discovery 11
  12. 12. DATA2DISCOVERY Example: Troglitazone and PPARG Association score: 2385.9 Association significance: 9.06 x 10-6 => missing link predicted 12
  13. 13. DATA2DISCOVERY P3 Public Data Demonstration •  Links several public datasets using TB as a sample TA: •  ChEMBL/OpenPHACTS (enzymatic, cellular assays) •  NCATS Phenotypic Drug Discovery Resource1 (phenotypic assays) •  Manual phenotypic assay – pathway/gene annotations •  TB drug – gene (DrugBank, NCATS) 1 13
  14. 14. DATA2DISCOVERY Open Phenotypic Drug Discovery Resource Eli Lilly IT 14
  15. 15. DATA2DISCOVERY Current P3 Linked Data Network 15
  16. 16. DATA2DISCOVERY P3 Public Data Example 16
  17. 17. DATA2DISCOVERY Drug Discovery Development & Clinical Trials Post Market /Healthcare Use Product Example Data Open PHACTS OPDDR ToxCast Internal Cpd/Assay Internal Trials Data Quantified Self / IOT Medicare CMS EMR Data RWE Data P3-Discovery P3-Clinical P3-PopHealth Target ID Poly- Pharmacology MOA Discovery ADME/ Tox Adverse Event Prediction Candidate Repositioning Drug Repositioning Population Analysis Drug Combinations Personalized Medicine FAERS Value/Performance based Pricing P3-Development Plan 17
  18. 18. DATA2DISCOVERY What can Data2Discovery offer? •  Agile development of high impact applications that use semantic linked data •  Implementation of linked data ecosystem, computation infrastructure and user components •  Key expertise in using OpenPHACTS data •  Contact David Wild, 18