Daniel Himmelstein, PhD

Data-Driven Drug Repurposing Workshop
2023-10-03

Integrating All Biomedical Knowledge to Systematically Find the Best Opportunities:

From Hetionet to Related Sciences

slides.com/dhimmel/related-sciences

Workshop Information

Data-Driven Drug Repurposing Workshop: Unlocking disease biology and advancing systematic approaches

  • CZI Headquarters
  • Hosted by CZI & Every Cure
  • Redwood City, CA
  • 2023-10-03 at 1:30 PM

Session 2: Curating and Representing Biomedical Knowledge in Network-Based Approaches
Session Abstract: Biomedical knowledge graphs (KGs) represent rich relationships and semantics between drugs, targets and diseases, which support novel methods for inferring biological pathways and predicting drug-target links across a network of interacting genes and proteins. In this session, panelists will share efforts to build and apply KGs for drug repurposing, including advances in optimizing predictions as well as challenges in extracting biomedical knowledge and overcoming inconsistencies, contradictions and other limitations and complexities.

Sandler Neurosciences Center

Sergio

Greene Lab

http://www.greenelab.com/

Postdoc at the University of Pennsylvania

  • knowledge graph for drug repurposing
     
  • integrates 29 public resources
    knowledge from millions of studies
     
  • ~50 thousand nodes
    11 types (labels)
     
  • ~2.25 million relationships
    24 types

Hetionet v1.0

Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
eLife (2017) https://doi.org/cdfk

Hetionet metagraph (schema)

https://neo4j.het.io

github.com/hetio/hetionet

observations =

compound–disease pairs

features = types of paths

Project Rephetio

Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
eLife (2017) https://doi.org/cdfk

predicted probability of treatment for 209,168 compound–disease pairs
https://het.io/repurpose/

1,538 connected

138 connected

disease modifying treatments
+755, −208,413
AUROC = 97.4%

treatments with clinical trials
+5,594, −202,186
AUROC = 70.0%

Project Rephetio: Does bupropion treat nicotine dependence?

  • Bupropion was first approved for depression in 1985
     
  • In 1997, bupropion was approved for smoking cessation
     
  • Can we predict this repurposing from Hetionet? The prediction was:

Compound–causes–Side Effect–causes–Compound–treats–Disease

Compound–binds–Gene–associates–Disease

Compound–binds–Gene–participates–Pathway–participates–Disease

connectivity search

how are two nodes connected?

sans supervision

Hetnet connectivity search provides rapid insights into how biomedical entities are related
Daniel Himmelstein, Michael Zietz, Vincent Rubinetti, Kyle Kloster, Benjamin Heil, Faisal Alquaddoomi, Dongbo Hu, David Nicholson, Yun Hao, Blair Sullivan, Michael Nagle, Casey Greene

GigaScience (2023) https://doi.org/gsd85n

https://het.io/search/

enriched metapaths

enriched paths

visualizing subgraphs

  • What we learned?
    • unsupervised is hard
       
  • Hetionet wishlist
    • time resolution
    • automated updates
    • greater disease coverage
    • data on real world entities

https://related.vc

continuing the journey at

SUPPLY

of great new drug targets

evidence from 3 million global researchers

DEMAND

to acquire new drugs

350+ large
biopharma acquirers

https://related.vc/team

https://related.vc

how to build biotechs that fail less often?

an efficient R&D operating
model

a new data science platform

  • new scientific staffing model
  • hub-and-spoke R&D partnerships
  • rank 250 million target-disease pairs
  • leverage ML to predict outcomes over time

RS Facets

AI/ML Opportunity Ranking Platform

RS Facets

  1. Time-Resolved Biomedical Atlas
    70+ public and private data sets
  2. Feature Design
    100s of metrics designed to quantitatively assess risk and reward for 250 million target-disease pairs
  3. Predictive ML Models
    Outcomes like probability of clinical success, commercial interest, or economic value creation
  4. Validation and Back-testing
    Enable objective comparative performance benchmarking and iterative improvement

RS Facets™ ingests all activities in global biomedicine to systematically predict the best new drug discovery opportunities.

Identifying 1,000s of the very best opportunities from 250 million

Investing in things most likely to work.

Back-testing performance covering all indications except infectious diseases and oncology

RS Facets back-testing and validation data for its Q1 2023 clinical prediction model build; comparisons reflect the performance the Facets system’s top predictions on a bet- and time-matched basis would have had in a given historical year, based only on what was known in that year.

Likelihood of FDA Approval from Phase I

  • diving into one data team project at Related Sciences
  • EFO OTAR Slim
  • cause of the tangle
    • diagnosable diseases
    • grouping terms
  • How many diseases are there?

the hairball of disease

github.com/related-sciences

classifying EFO nodes

Audience poll from the MONDO Outreach Workshop
https://slides.com/dhimmel/efo-disease-precision

predicted EFO classifications

Node outline shows precision

  • low = dotted
    disease groupings
  • medium = dashed
    diseases
  • high = solid
    disease subtypes

nxontology software suite

  • nxontology

    NetworkX-based Python library for representing ontologies.

  • nxontology-ml
    Machine learning to classify ontology nodes.
  • nxontology-data
    Making ontologies accessible as simple JSON files.
    • EFO
    • MeSH
    • HGNC Gene Groups
    • PubChem Classifications

Figure from the obo-community slack by Philip Strömert generated with Midjourney prompt:

We cannot interpret our research data anymore because we did not annotate it with ontologies

github.com/related-sciences

feature group importance

poll

Which feature group has the greatest influence on the outcome?

 

Slido: 1342 945

  • topology
  • cross-references
  • prefixes
  • subsets
  • descriptions
  • gpt tags
  • gwas

Looking forward to discussing!

Say hi to Adam Kolom and me.

Integrating All Biomedical Knowledge to Systematically Find the Best Opportunities: From Hetionet to Related Sciences

By Daniel Himmelstein

Integrating All Biomedical Knowledge to Systematically Find the Best Opportunities: From Hetionet to Related Sciences

Presented on 2023-10-03 to the Data-Driven Drug Repurposing Workshop at CZI headquarters in Redwood City, CA. Hosted by EveryCure and the Chan Zuckerberg Initiative.

  • 187