From Target to Product - Accelerating the Drug Lifecycle with Knowledge Graphs.pdf

Neo4j
Neo4jOpen Source NOSQL Graph Database à Neo4j
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
From Target to Product –
Accelerating the Drug Lifecycle
with Knowledge Graphs
1
Dr. Alexander Jarasch - on behalf of the whole Neo4j team
alexander.jarasch@neo4j.com
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
Audrey
Fredrik
Håkan
Kristof
Carlos Niels
Marius
Yuen Leung
Alex
Neo4j
Team
© 2023 Neo4j, Inc. All rights reserved.
Survey
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Survey
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Why Graphs?
5
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https://www.graphable.ai/blog
6
Motivation
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“Graphs are the main modality
of data we receive from
nature…”
7
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Simple Graph Example from Biology Textbook
RNA
Gene Protein
:CODES :CODES
:SYNONYM :SYNONYM :SYNONYM
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Simple Graph Example from Biology Textbook
RNA
Gene Protein
:CODES :CODES
:SYNONYM :SYNONYM :SYNONYM
Gene
Copy
Epi
genetics
RNA
silencing
CRISPR
Cas9
SNPs
RNA
splicing
Protein
glycosylation
Protein
phosphory
lation
Protein
ubiquitination
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Legacy
systems
can’t keep up
Relational Databases
don’t handle relationships well
Document Databases
don’t handle relationships at all
10
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11
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12
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Data, meet Graph.
Neo4j creates a more intuitive and connected view of data
relationships, unlocking deeper understanding and context
13
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14
How to manage complexity?
How to mitigate risk?
How to improve time to market?
14
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NEO4J: FOR APPLICATIONS AND ANALYTICS
15
Graph Transactions,
Storage & Querying
Graph Analytics, ML,
& Data Science
Intelligent Applications Better Predictions
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Graph Transactions,
Storage & Querying
Graph Analytics, ML,
& Data Science
Intelligent Applications Better Predictions
16
Find sub populations
-> Community Detections
Identify Targets
-> Centrality Algorithms
Find alternative Routes
-> K shortest paths
Classify Patients
-> Node Similarity
NEO4J: FOR APPLICATIONS AND ANALYTICS
© 2023 Neo4j, Inc. All rights reserved.
Graph Algorithms in Neo4j GDS
Pathfinding &
Search
• A* Shortest Path
• All Pairs Shortest Path
• Breadth & Depth First Search
• Delta-Stepping Single-Source
• Dijkstra Single-Source
• Dijkstra Source-Target
• K-Spanning Tree (MST)
• Minimum Weight Spanning Tree
• Random Walk
• Yen’s K Shortest Path
Centrality &
Importance
• ArticleRank
• Betweenness Centrality & Approx.
• Closeness Centrality
• Degree Centrality
• Eigenvector Centrality
• Harmonic Centrality
• Hyperlink Induced Topic Search (HITS)
• Influence Maximization (Greedy, CELF)
• PageRank
• Personalized PageRank
Community
Detection
• Conductance Metric
• K-1 Coloring
• K-Means Clustering
• Label Propagation
• Leiden Algorithm
• Local Clustering Coefficient
• Louvain Algorithm
• Max K-Cut
• Modularity Optimization
• Speaker Listener Label Propagation
• Strongly Connected Components
• Triangle Count
• Weakly Connected Components
Supervised
Machine Learning
• Link Prediction Pipelines
• Node Classification Pipelines
• Node Regression Pipelines
… and more!
Heuristic Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
Similarity
• K-Nearest Neighbors (KNN)
• Node Similarity
• Filtered KNN & Node Similarity
• Cosine & Pearson Similarity Functions
• Euclidean Distance Similarity Function
• Euclidean Similarity Function
• Jaccard & Overlap Similarity Functions
Graph
Embeddings
• Fast Random Projection (FastRP)
• GraphSAGE
• Node2Vec
• HashGNN
• Collapse Paths
• One Hot Encoding
• Pregel API (write your own algos)
• Property Scaling
• Split Relationships
• Synthetic Graph Generation
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
Use Cases R&D
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© 2022 Neo4j, Inc. All rights reserved.
19
Pains and Challenges
Connect internal and external data
Integration / Connection of Heterogeneous
datasets
Connect data to ontologies
Pain:
Connect data
Slow, tedious
manual work
Users:
Mainly Scientists
(Biochemists,
Bioinformatics,
Physicians)
Result:
Speed up research
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
20
Use Case R&D
HealthECCO / CovidGraph
Connecting Molecular Entities
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Volumen of (heterogenous) data is increasing
© 2023 Neo4j, Inc. All rights reserved.
Overview
• Usually Drug Discovery use
cases start with building a
Knowledge Graph
◦ Connect biomedical entities
◦ Connect Ontologies
◦ Infer / Validate Knowledge
• Applications
◦ Drug repurposing
◦ Target identification
Example of connecting biomedical entities (GitHub)
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
23
Use Case R&D
Novartis
Target and Drug Discovery / Drug
Repurposing
© 2023 Neo4j, Inc. All rights reserved.
24
Reduced Time to Market
Target Identification
Challenge
Slow process of identification of targets
across literature, experimental and clinical
trials data during drug discovery process
Solution
Knowledge graph of genes, diseases and
compounds created from internal historical
and imaging data and external PubMed data
and analyzed using powerful graph
algorithms.
Why Neo4j
Flexible, Contextual Data Model
½ B
Nodes
Quicker
and More
Accurate
Identification of
targets
Decreased
Human Effort &
Cost
© 2023 Neo4j, Inc. All rights reserved.
Novartis: Find “triangles” in Knowledge Graph
Disease
Gene
Drug
Clinical Knowledge
Ontologies
Comorbidities
Molecular Information
Pathways
Proteins
Clinical Trials
Side Effects
Targets
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
26
Use Case R&D
Novo Nordisk
Target identification / validation
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Knowledge Graph of Novo Nordisk
Presentation BioTechX,
Basel 2022
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© 2023 Neo4j, Inc. All rights reserved.
29
Use Case R&D
Qiagen
Target and Drug Discovery / Drug repurposing
Manually curated scientific data
© 2023 Neo4j, Inc. All rights reserved.
30
Qiagen’s Biomedical Knowledge Base (via Neo4j)
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31
Use Case
US Pharma Company
Drug Repurposing
© 2023 Neo4j, Inc. All rights reserved.
Knowledge Graph over Clinical Trial Data
Disease1
Drug1
Disease2
Drug2
Drug3
Drug7
Target2
Disease4
Drug4
Drug5
Drug6
Disease3
Phase 2
Phase 2
Phase 3
Phase 3
Phase 1
Target1
Phase 3
Disconti
nued
Phase 2
Disease2 Drug2
CD40
Disease1
(internal)
Drug1
(internal)
Opportunity for engineering?
“Is there an antibody (in-house) that
targets CD40 -> has CD40 targeted
by other drugs, have they
experimented with other indications?”
“In what targets and
diseases do
competitors invest?”
32
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
Use Cases Clinical Trials
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© 2023 Neo4j, Inc. All rights reserved.
34
Use Case
US Pharma Company
Clinical Trial PI Identification
© 2023 Neo4j, Inc. All rights reserved.
3000X
Faster
1
Tool
(Knowledge
Graph) created
for all PI-related
questions
More
Accurate
Identification of
experienced PIs
35
Faster time to market
Clinical Trials PI Identification
Semantic Search
Challenge
Identifying Principal Investigators (PIs)
with the right experience to ensure that
they do not churn within / after a single
trial
Solution
Knowledge graph of PI Name, Address,
Hospital, Therapeutic Area, Specialty,
Past Clinical Studies, etc to identify the
right PI for clinical trials.
Why Neo4j
Flexible, Contextual Data Model
Fortune
100
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© 2023 Neo4j, Inc. All rights reserved.
36
Use Case
Novo Nordisk
Clinical Trial Data Management
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Reducing complexity
Novo Nordisk’s Open Study Builder
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© 2023 Neo4j, Inc. All rights reserved.
38
Use Case
GSK
Clinical Reporting Workflows
© 2023 Neo4j, Inc. All rights reserved.
Contextualize Clinical Knowledge Graph
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
40
Use Case
German Center for Diabetes Research (DZD)
Cross-Species Mapping of Ontologies
Graph Queries
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© 2023 Neo4j, Inc. All rights reserved.
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43
Querying between Ontologies and Genes
43
Example: Equivalent HP-/MP-Term “Deafness”/”Hearing impairment” and ortholog Genes
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44
Use Case
Astra Zeneca
Patient Journey
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45
Reducing risk and time to market
Patient Recommendations
Bringing new drugs to market can be
hard: you need to find the right patients
and educate their providers
Results:
● 3 years of health records with 4B+
data points ingested
● Unique archetype of successful
patients ID’d via graph embeddings
● Key opinion leaders identified based
on network structure
“We used graph algorithms to find
patients that had specific journey
types and patterns and then find
others that are close and similar.”
Joseph Roemer
Global Commercial IT Insight & Analytics Sr. Director
AstraZeneca
© 2023 Neo4j, Inc. All rights reserved.
46
Patient Journey with Neo4j
46
Synthetic Patient Journey Data
• Patient connected to all its
Encounters
• Each Encounter eventually has a
Clinical Condition and/or Drug
prescription
Part of the data model
One journey example
Zoom
24 Encounters
3 drugs prescribed
Patient
5 conditions
diagnosed
© 2023 Neo4j, Inc. All rights reserved.
47
Patient Journey Dashboard with NeoDash
47
Patient location
(geospatial data)
Population size
Deceased
subpopulation
Which clinical conditions
end up in the same drug
prescription
Month between
pre-diabetes (reversible)
and type 2 diabetes
(irreversible)
Encounters connected
to diabetes
Gender distribution
© 2023 Neo4j, Inc. All rights reserved.
Neo4j, Inc. All rights reserved 2022
Use Cases in Pharma Supply Chain
Supply chain network
Agility and resilience in an age of uncertainty
© 2023 Neo4j, Inc. All rights reserved.
Digital Supply Chain Twins
Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits
Benno Gerlach, Simon Zarnitz, Benjamin Nitsche and Frank Straube
Logistics 2021, 5, 86. https://doi.org/10.3390/logistics5040086
© 2023 Neo4j, Inc. All rights reserved.
Supply Chain Applications using Knowledge Graphs
Supply Chain
Optimization
Logistics
BoM
Management
Track & Trace
• Route optimization
• Risk identification and
diversity planning
• Predictive fulfillment
• Quality recommendation
• Supply chain driven
product design
© 2023 Neo4j, Inc. All rights reserved.
Best alternative Route
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Issues & Routes Affected
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Calculating costs on alternative routes
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Graph Algorithms in Supply Chains
Graph algorithms enable
reasoning about network
structure
K-Shortest Paths to
identify the best
alternative routes
© 2023 Neo4j, Inc. All rights reserved.
Graph Algorithms in Supply Chain
Graph algorithms enable reasoning about network
structure
K-Shortest Paths to identify the best alternative
routes
Betweenness Centrality to find critical bottlenecks or
risk points
Degree Centrality to see distribution centers with
high use
Similarity to find providers that can step in during a
disruption
© 2023 Neo4j, Inc. All rights reserved.
Graph Algorithms in Supply Chain
Graph algorithms enable reasoning about network
structure
K-Shortest Paths to identify the best alternative
routes
Betweenness Centrality to find critical bottlenecks or
risk points
Degree Centrality to see distribution centers with
high use
Similarity to find providers that can step in during a
disruption
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
57
Use Case
US Pharma Company
Track, Trace & Reconcile Billions of
Pharma Product Serial Numbers
© 2023 Neo4j, Inc. All rights reserved.
Track and Trace Solution
• Scalable solution for vast volumes of serial
numbers
“Continuing to use SQL Server DB was
unsustainable”
• Graph has the ability to record complex data
interdependencies
• Query speed ~100x faster than SQL Server
“Retrieval times for 1,000 serial numbers from two
billion records … We were seeing responses to
single queries of under a second, up to very few
seconds, compared to what could have been
several minutes using SQL Server – performance
which just isn’t acceptable in our business”
• regulated life sciences industry
• Identify at any given time where
individual medicine items are
• FEB 2019,
the EU’s Falsified Medicines
Directive (FMD) specifies
◦ any medicinal products for
human use must carry a unique
product identifier code
◦ manufacturers and distributors
can demonstrate detailed
recordkeeping
• Processes across multiple sites,
markets, keeping track of every
stock
Challenge
© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
59
Use Case
US Pharma Company
Supply Chain / Digital Twin
© 2023 Neo4j, Inc. All rights reserved.
An Example for Drug Supply Network
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Use Case FAERS
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FDA Adverse Event Reporting System Data with Neo4j
+ Internal Adverse Event Data
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FAERS - Potential Business Questions
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67
Graph Use Cases for the entire Drug LifeCycle
67
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© 2023 Neo4j, Inc. All rights reserved.
…there’s one more THING
https://neo4j.com/labs/apoc/5/ml/openai/
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69
Setting the context - From a user point of view
© 2023 Neo4j, Inc. All rights reserved.
Three potential scenarios
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72
Let’s rock some graphs!
alexander.jarasch@neo4j.com
© 2023 Neo4j, Inc. All rights reserved.
Survey
© 2023 Neo4j, Inc. All rights reserved.
Survey - Interactive sessions
© 2023 Neo4j, Inc. All rights reserved.
Visual Graph
Explorer
Interactive
Query Tools
API
Libraries
Application
Frameworks
Graph Data
Science
Language
Interfaces
75
Neo4j Graph Data Platform
BUSINESS
USERS
DEVELOPERS
DATA
SCIENTISTS
DATA
ANALYSTS
Data Sources
Graph
Transactions
Graph
Analytics
Native Graph Database
The foundation of the Neo4j platform; delivers enterprise-scale
and performance, security, and data integrity for transaction and
analytical workloads.
Data Science and Analytics
Explorative tools, rich algorithm library, and Integrated
supervised Machine Learning framework.
Development Tools & Frameworks
Tooling, APIs, query builder, multi-language support for
development, admin, modeling, and rapid prototyping needs.
Discovery & Visualisation
Code-free querying, data modeling and exploration tools for data
scientists, developers, and analysts.
Graph Query Language Support
Cypher & openCypher; Ongoing leadership and standards work
(GQL) to establish lingua franca for graphs.
Ecosystem & Integrations
Rich ecosystem of tech and integration partners. Ingestion tools
(JDBC, Kafka, Spark, BI Tools, etc.) for bulk and streaming needs.
Runs Anywhere
Deploy as-a-Service (AuraDB) or self-hosted within your cloud of
choice (AWS, GCP, Azure) via their marketplace, or on-premises.
Graph Database
Data Connectors
1 sur 75

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From Target to Product - Accelerating the Drug Lifecycle with Knowledge Graphs.pdf

  • 1. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. From Target to Product – Accelerating the Drug Lifecycle with Knowledge Graphs 1 Dr. Alexander Jarasch - on behalf of the whole Neo4j team alexander.jarasch@neo4j.com
  • 2. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. Audrey Fredrik Håkan Kristof Carlos Niels Marius Yuen Leung Alex Neo4j Team
  • 3. © 2023 Neo4j, Inc. All rights reserved. Survey
  • 4. © 2023 Neo4j, Inc. All rights reserved. Survey
  • 5. © 2023 Neo4j, Inc. All rights reserved. Why Graphs? 5
  • 6. © 2023 Neo4j, Inc. All rights reserved. https://www.graphable.ai/blog 6 Motivation
  • 7. © 2023 Neo4j, Inc. All rights reserved. “Graphs are the main modality of data we receive from nature…” 7
  • 8. © 2023 Neo4j, Inc. All rights reserved. Simple Graph Example from Biology Textbook RNA Gene Protein :CODES :CODES :SYNONYM :SYNONYM :SYNONYM
  • 9. © 2023 Neo4j, Inc. All rights reserved. Simple Graph Example from Biology Textbook RNA Gene Protein :CODES :CODES :SYNONYM :SYNONYM :SYNONYM Gene Copy Epi genetics RNA silencing CRISPR Cas9 SNPs RNA splicing Protein glycosylation Protein phosphory lation Protein ubiquitination
  • 10. © 2023 Neo4j, Inc. All rights reserved. Legacy systems can’t keep up Relational Databases don’t handle relationships well Document Databases don’t handle relationships at all 10
  • 11. © 2023 Neo4j, Inc. All rights reserved. 11
  • 12. © 2023 Neo4j, Inc. All rights reserved. 12
  • 13. © 2023 Neo4j, Inc. All rights reserved. Data, meet Graph. Neo4j creates a more intuitive and connected view of data relationships, unlocking deeper understanding and context 13
  • 14. © 2023 Neo4j, Inc. All rights reserved. 14 How to manage complexity? How to mitigate risk? How to improve time to market? 14
  • 15. © 2023 Neo4j, Inc. All rights reserved. NEO4J: FOR APPLICATIONS AND ANALYTICS 15 Graph Transactions, Storage & Querying Graph Analytics, ML, & Data Science Intelligent Applications Better Predictions
  • 16. © 2023 Neo4j, Inc. All rights reserved. Graph Transactions, Storage & Querying Graph Analytics, ML, & Data Science Intelligent Applications Better Predictions 16 Find sub populations -> Community Detections Identify Targets -> Centrality Algorithms Find alternative Routes -> K shortest paths Classify Patients -> Node Similarity NEO4J: FOR APPLICATIONS AND ANALYTICS
  • 17. © 2023 Neo4j, Inc. All rights reserved. Graph Algorithms in Neo4j GDS Pathfinding & Search • A* Shortest Path • All Pairs Shortest Path • Breadth & Depth First Search • Delta-Stepping Single-Source • Dijkstra Single-Source • Dijkstra Source-Target • K-Spanning Tree (MST) • Minimum Weight Spanning Tree • Random Walk • Yen’s K Shortest Path Centrality & Importance • ArticleRank • Betweenness Centrality & Approx. • Closeness Centrality • Degree Centrality • Eigenvector Centrality • Harmonic Centrality • Hyperlink Induced Topic Search (HITS) • Influence Maximization (Greedy, CELF) • PageRank • Personalized PageRank Community Detection • Conductance Metric • K-1 Coloring • K-Means Clustering • Label Propagation • Leiden Algorithm • Local Clustering Coefficient • Louvain Algorithm • Max K-Cut • Modularity Optimization • Speaker Listener Label Propagation • Strongly Connected Components • Triangle Count • Weakly Connected Components Supervised Machine Learning • Link Prediction Pipelines • Node Classification Pipelines • Node Regression Pipelines … and more! Heuristic Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors Similarity • K-Nearest Neighbors (KNN) • Node Similarity • Filtered KNN & Node Similarity • Cosine & Pearson Similarity Functions • Euclidean Distance Similarity Function • Euclidean Similarity Function • Jaccard & Overlap Similarity Functions Graph Embeddings • Fast Random Projection (FastRP) • GraphSAGE • Node2Vec • HashGNN • Collapse Paths • One Hot Encoding • Pregel API (write your own algos) • Property Scaling • Split Relationships • Synthetic Graph Generation
  • 18. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. Use Cases R&D
  • 19. © 2023 Neo4j, Inc. All rights reserved. © 2022 Neo4j, Inc. All rights reserved. 19 Pains and Challenges Connect internal and external data Integration / Connection of Heterogeneous datasets Connect data to ontologies Pain: Connect data Slow, tedious manual work Users: Mainly Scientists (Biochemists, Bioinformatics, Physicians) Result: Speed up research
  • 20. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 20 Use Case R&D HealthECCO / CovidGraph Connecting Molecular Entities
  • 21. © 2023 Neo4j, Inc. All rights reserved. Volumen of (heterogenous) data is increasing
  • 22. © 2023 Neo4j, Inc. All rights reserved. Overview • Usually Drug Discovery use cases start with building a Knowledge Graph ◦ Connect biomedical entities ◦ Connect Ontologies ◦ Infer / Validate Knowledge • Applications ◦ Drug repurposing ◦ Target identification Example of connecting biomedical entities (GitHub)
  • 23. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 23 Use Case R&D Novartis Target and Drug Discovery / Drug Repurposing
  • 24. © 2023 Neo4j, Inc. All rights reserved. 24 Reduced Time to Market Target Identification Challenge Slow process of identification of targets across literature, experimental and clinical trials data during drug discovery process Solution Knowledge graph of genes, diseases and compounds created from internal historical and imaging data and external PubMed data and analyzed using powerful graph algorithms. Why Neo4j Flexible, Contextual Data Model ½ B Nodes Quicker and More Accurate Identification of targets Decreased Human Effort & Cost
  • 25. © 2023 Neo4j, Inc. All rights reserved. Novartis: Find “triangles” in Knowledge Graph Disease Gene Drug Clinical Knowledge Ontologies Comorbidities Molecular Information Pathways Proteins Clinical Trials Side Effects Targets
  • 26. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 26 Use Case R&D Novo Nordisk Target identification / validation
  • 27. © 2023 Neo4j, Inc. All rights reserved. Knowledge Graph of Novo Nordisk Presentation BioTechX, Basel 2022
  • 28. © 2023 Neo4j, Inc. All rights reserved.
  • 29. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 29 Use Case R&D Qiagen Target and Drug Discovery / Drug repurposing Manually curated scientific data
  • 30. © 2023 Neo4j, Inc. All rights reserved. 30 Qiagen’s Biomedical Knowledge Base (via Neo4j)
  • 31. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 31 Use Case US Pharma Company Drug Repurposing
  • 32. © 2023 Neo4j, Inc. All rights reserved. Knowledge Graph over Clinical Trial Data Disease1 Drug1 Disease2 Drug2 Drug3 Drug7 Target2 Disease4 Drug4 Drug5 Drug6 Disease3 Phase 2 Phase 2 Phase 3 Phase 3 Phase 1 Target1 Phase 3 Disconti nued Phase 2 Disease2 Drug2 CD40 Disease1 (internal) Drug1 (internal) Opportunity for engineering? “Is there an antibody (in-house) that targets CD40 -> has CD40 targeted by other drugs, have they experimented with other indications?” “In what targets and diseases do competitors invest?” 32
  • 33. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. Use Cases Clinical Trials
  • 34. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 34 Use Case US Pharma Company Clinical Trial PI Identification
  • 35. © 2023 Neo4j, Inc. All rights reserved. 3000X Faster 1 Tool (Knowledge Graph) created for all PI-related questions More Accurate Identification of experienced PIs 35 Faster time to market Clinical Trials PI Identification Semantic Search Challenge Identifying Principal Investigators (PIs) with the right experience to ensure that they do not churn within / after a single trial Solution Knowledge graph of PI Name, Address, Hospital, Therapeutic Area, Specialty, Past Clinical Studies, etc to identify the right PI for clinical trials. Why Neo4j Flexible, Contextual Data Model Fortune 100
  • 36. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 36 Use Case Novo Nordisk Clinical Trial Data Management
  • 37. © 2023 Neo4j, Inc. All rights reserved. Reducing complexity Novo Nordisk’s Open Study Builder
  • 38. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 38 Use Case GSK Clinical Reporting Workflows
  • 39. © 2023 Neo4j, Inc. All rights reserved. Contextualize Clinical Knowledge Graph
  • 40. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 40 Use Case German Center for Diabetes Research (DZD) Cross-Species Mapping of Ontologies Graph Queries
  • 41. © 2023 Neo4j, Inc. All rights reserved.
  • 42. © 2023 Neo4j, Inc. All rights reserved.
  • 43. © 2023 Neo4j, Inc. All rights reserved. 43 Querying between Ontologies and Genes 43 Example: Equivalent HP-/MP-Term “Deafness”/”Hearing impairment” and ortholog Genes
  • 44. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 44 Use Case Astra Zeneca Patient Journey
  • 45. © 2023 Neo4j, Inc. All rights reserved. 45 Reducing risk and time to market Patient Recommendations Bringing new drugs to market can be hard: you need to find the right patients and educate their providers Results: ● 3 years of health records with 4B+ data points ingested ● Unique archetype of successful patients ID’d via graph embeddings ● Key opinion leaders identified based on network structure “We used graph algorithms to find patients that had specific journey types and patterns and then find others that are close and similar.” Joseph Roemer Global Commercial IT Insight & Analytics Sr. Director AstraZeneca
  • 46. © 2023 Neo4j, Inc. All rights reserved. 46 Patient Journey with Neo4j 46 Synthetic Patient Journey Data • Patient connected to all its Encounters • Each Encounter eventually has a Clinical Condition and/or Drug prescription Part of the data model One journey example Zoom 24 Encounters 3 drugs prescribed Patient 5 conditions diagnosed
  • 47. © 2023 Neo4j, Inc. All rights reserved. 47 Patient Journey Dashboard with NeoDash 47 Patient location (geospatial data) Population size Deceased subpopulation Which clinical conditions end up in the same drug prescription Month between pre-diabetes (reversible) and type 2 diabetes (irreversible) Encounters connected to diabetes Gender distribution
  • 48. © 2023 Neo4j, Inc. All rights reserved. Neo4j, Inc. All rights reserved 2022 Use Cases in Pharma Supply Chain Supply chain network Agility and resilience in an age of uncertainty
  • 49. © 2023 Neo4j, Inc. All rights reserved. Digital Supply Chain Twins Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits Benno Gerlach, Simon Zarnitz, Benjamin Nitsche and Frank Straube Logistics 2021, 5, 86. https://doi.org/10.3390/logistics5040086
  • 50. © 2023 Neo4j, Inc. All rights reserved. Supply Chain Applications using Knowledge Graphs Supply Chain Optimization Logistics BoM Management Track & Trace • Route optimization • Risk identification and diversity planning • Predictive fulfillment • Quality recommendation • Supply chain driven product design
  • 51. © 2023 Neo4j, Inc. All rights reserved. Best alternative Route
  • 52. © 2023 Neo4j, Inc. All rights reserved. Issues & Routes Affected
  • 53. © 2023 Neo4j, Inc. All rights reserved. Calculating costs on alternative routes
  • 54. © 2023 Neo4j, Inc. All rights reserved. Graph Algorithms in Supply Chains Graph algorithms enable reasoning about network structure K-Shortest Paths to identify the best alternative routes
  • 55. © 2023 Neo4j, Inc. All rights reserved. Graph Algorithms in Supply Chain Graph algorithms enable reasoning about network structure K-Shortest Paths to identify the best alternative routes Betweenness Centrality to find critical bottlenecks or risk points Degree Centrality to see distribution centers with high use Similarity to find providers that can step in during a disruption
  • 56. © 2023 Neo4j, Inc. All rights reserved. Graph Algorithms in Supply Chain Graph algorithms enable reasoning about network structure K-Shortest Paths to identify the best alternative routes Betweenness Centrality to find critical bottlenecks or risk points Degree Centrality to see distribution centers with high use Similarity to find providers that can step in during a disruption
  • 57. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 57 Use Case US Pharma Company Track, Trace & Reconcile Billions of Pharma Product Serial Numbers
  • 58. © 2023 Neo4j, Inc. All rights reserved. Track and Trace Solution • Scalable solution for vast volumes of serial numbers “Continuing to use SQL Server DB was unsustainable” • Graph has the ability to record complex data interdependencies • Query speed ~100x faster than SQL Server “Retrieval times for 1,000 serial numbers from two billion records … We were seeing responses to single queries of under a second, up to very few seconds, compared to what could have been several minutes using SQL Server – performance which just isn’t acceptable in our business” • regulated life sciences industry • Identify at any given time where individual medicine items are • FEB 2019, the EU’s Falsified Medicines Directive (FMD) specifies ◦ any medicinal products for human use must carry a unique product identifier code ◦ manufacturers and distributors can demonstrate detailed recordkeeping • Processes across multiple sites, markets, keeping track of every stock Challenge
  • 59. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 59 Use Case US Pharma Company Supply Chain / Digital Twin
  • 60. © 2023 Neo4j, Inc. All rights reserved. An Example for Drug Supply Network
  • 61. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. Use Case FAERS
  • 62. © 2023 Neo4j, Inc. All rights reserved. FDA Adverse Event Reporting System Data with Neo4j + Internal Adverse Event Data
  • 63. © 2023 Neo4j, Inc. All rights reserved. FAERS - Potential Business Questions
  • 64. © 2023 Neo4j, Inc. All rights reserved.
  • 65. © 2023 Neo4j, Inc. All rights reserved.
  • 66. © 2023 Neo4j, Inc. All rights reserved.
  • 67. 67 Graph Use Cases for the entire Drug LifeCycle 67
  • 68. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. …there’s one more THING https://neo4j.com/labs/apoc/5/ml/openai/
  • 69. © 2023 Neo4j, Inc. All rights reserved. 69 Setting the context - From a user point of view
  • 70. © 2023 Neo4j, Inc. All rights reserved. Three potential scenarios
  • 71. © 2023 Neo4j, Inc. All rights reserved.
  • 72. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. 72 Let’s rock some graphs! alexander.jarasch@neo4j.com
  • 73. © 2023 Neo4j, Inc. All rights reserved. Survey
  • 74. © 2023 Neo4j, Inc. All rights reserved. Survey - Interactive sessions
  • 75. © 2023 Neo4j, Inc. All rights reserved. Visual Graph Explorer Interactive Query Tools API Libraries Application Frameworks Graph Data Science Language Interfaces 75 Neo4j Graph Data Platform BUSINESS USERS DEVELOPERS DATA SCIENTISTS DATA ANALYSTS Data Sources Graph Transactions Graph Analytics Native Graph Database The foundation of the Neo4j platform; delivers enterprise-scale and performance, security, and data integrity for transaction and analytical workloads. Data Science and Analytics Explorative tools, rich algorithm library, and Integrated supervised Machine Learning framework. Development Tools & Frameworks Tooling, APIs, query builder, multi-language support for development, admin, modeling, and rapid prototyping needs. Discovery & Visualisation Code-free querying, data modeling and exploration tools for data scientists, developers, and analysts. Graph Query Language Support Cypher & openCypher; Ongoing leadership and standards work (GQL) to establish lingua franca for graphs. Ecosystem & Integrations Rich ecosystem of tech and integration partners. Ingestion tools (JDBC, Kafka, Spark, BI Tools, etc.) for bulk and streaming needs. Runs Anywhere Deploy as-a-Service (AuraDB) or self-hosted within your cloud of choice (AWS, GCP, Azure) via their marketplace, or on-premises. Graph Database Data Connectors