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

© 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

Audrey
Fredrik
Håkan
Kristof
Carlos Niels
Marius
Yuen Leung
Alex
Neo4j
Team

Survey

Why Graphs?
5

https://www.graphable.ai/blog
6
Motivation

“Graphs are the main modality
of data we receive from
nature…”
7

Simple Graph Example from Biology Textbook
RNA
Gene Protein
:CODES :CODES
:SYNONYM :SYNONYM :SYNONYM

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

Legacy
systems
can’t keep up
Relational Databases
don’t handle relationships well
Document Databases
don’t handle relationships at all
10

11

12

Data, meet Graph.
Neo4j creates a more intuitive and connected view of data
relationships, unlocking deeper understanding and context
13

14
How to manage complexity?
How to mitigate risk?
How to improve time to market?
14

NEO4J: FOR APPLICATIONS AND ANALYTICS
15
Graph Transactions,
Storage & Querying
Graph Analytics, ML,
& Data Science
Intelligent Applications Better Predictions

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

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

Use Cases R&D

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
Use Case R&D
HealthECCO / CovidGraph
Connecting Molecular Entities

Volumen of (heterogenous) data is increasing

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
Use Case R&D
Novartis
Target and Drug Discovery / Drug
Repurposing

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

Novartis: Find “triangles” in Knowledge Graph
Disease
Gene
Drug
Clinical Knowledge
Ontologies
Comorbidities
Molecular Information
Pathways
Proteins
Clinical Trials
Side Effects
Targets

26
Use Case R&D
Novo Nordisk
Target identiﬁcation / validation

Knowledge Graph of Novo Nordisk
Presentation BioTechX,
Basel 2022

29
Use Case R&D
Qiagen
Target and Drug Discovery / Drug repurposing
Manually curated scientiﬁc data

30
Qiagen’s Biomedical Knowledge Base (via Neo4j)

31
Use Case
US Pharma Company
Drug Repurposing

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

Use Cases Clinical Trials

34
Use Case
US Pharma Company
Clinical Trial PI Identiﬁcation

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
Use Case
Novo Nordisk
Clinical Trial Data Management

Reducing complexity
Novo Nordisk’s Open Study Builder

38
Use Case
GSK
Clinical Reporting Workﬂows

Contextualize Clinical Knowledge Graph

40
Use Case
German Center for Diabetes Research (DZD)
Cross-Species Mapping of Ontologies
Graph Queries

43
Querying between Ontologies and Genes
43
Example: Equivalent HP-/MP-Term “Deafness”/”Hearing impairment” and ortholog Genes

44
Use Case
Astra Zeneca
Patient Journey

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
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
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

Neo4j, Inc. All rights reserved 2022
Use Cases in Pharma Supply Chain
Supply chain network
Agility and resilience in an age of uncertainty

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

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

Best alternative Route

Issues & Routes Affected

Calculating costs on alternative routes

Graph Algorithms in Supply Chains
Graph algorithms enable
reasoning about network
structure
K-Shortest Paths to
identify the best
alternative routes

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
Use Case
US Pharma Company
Track, Trace & Reconcile Billions of
Pharma Product Serial Numbers

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
Use Case
US Pharma Company
Supply Chain / Digital Twin

An Example for Drug Supply Network

Use Case FAERS

FDA Adverse Event Reporting System Data with Neo4j
+ Internal Adverse Event Data

FAERS - Potential Business Questions

67
Graph Use Cases for the entire Drug LifeCycle
67

…there’s one more THING
https://neo4j.com/labs/apoc/5/ml/openai/

69
Setting the context - From a user point of view

Three potential scenarios

72
Let’s rock some graphs!
alexander.jarasch@neo4j.com

Survey - Interactive sessions

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

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