OpenPHACTS: Maximizing Impact for Pharmaceutical Applications Watch the full webinar video at https://www.youtube.com/watch?v=y1docvn0Jjg

1. DATA2DISCOVERY
OpenPHACTS:
Maximizing Impact for
Pharmaceutical Applications
DAVID WILD, PhD.
CEO Data2Discovery Inc
Associate Professor and Director of Data Science, Indiana University
http://d2discovery.com - http://djwild.info - david@d2discovery.com
DATA SCIENCE DATA2DISCOVERY
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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
• http://d2discovery.com
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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
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IT Infrastructure Re-Imagined
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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?

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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
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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
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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
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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
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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)
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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)
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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
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Example: Troglitazone and PPARG
Association score: 2385.9
Association significance: 9.06 x 10-6 =>
missing link predicted
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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)
1https://ncats.nih.gov/expertise/preclinical/pd2
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Open Phenotypic Drug Discovery Resource
Eli Lilly IT
https://ncats.nih.gov/expertise/preclinical/pd2
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Current P3 Linked Data Network
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P3 Public Data Example
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Drug Discovery Development & Clinical
Trials
Post Market /Healthcare
Use
Product
Example
Data
Open
PHACTS
OPDDR
ToxCast
Internal
Cpd/Assay
Internal
Trials
Data
ClinicalTrials.gov
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
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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, david@d2discovery.com
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