This document discusses using network pharmacology and computational modeling to identify novel potential anti-cancer agents. It describes how E-Therapeutics constructs disease networks and then uses its proprietary chemoinformatics tools to identify compounds that could impact those networks. One lead anti-cancer candidate, Dexanabinol, is highlighted which has passed Phase 1 trials. Experimental validation of compounds predicted to impact glioma networks identified over 50% as weakly active potential leads and 14 as highly active candidates, demonstrating the potential of this network pharmacology approach.

1. Identification of novel potential
anti-cancer agents using
network pharmacology based
computational modelling
Name: Ben Allen
Organisation: E-Therapeutics PLC

2. e-Therapeutics
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 What is Network Pharmacology
 Network Science
 Application to Biological Networks
 Drug Discovery using Networks
 Bioinformatics
 Network Construction
 Proprietary Chemoinformatics
 Anti-cancer Compounds
 Dexanabinol
 Validation in Cytotoxicity Assays

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Network Pharmacology

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Network Science
 What is a network?
Node
Edge
o Network Properties
o Node Properties
o Community Structure

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Network Properties
• Distance
• Length of a shortest path between two vertices
• Distance = number of hops between nodes
• Edges can be weighted
• Distance depends on sum of weights along a path
Distance = 4 hops Distance = 0.85

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Network Properties
• Network diameter = max(distance)
• Useful indicator of perturbation effect: increase in diameter implies a
decrease in connectedness
Diameter = 4 hops Diameter = 5 hops

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Node Properties
• Centrality – measure of how important is a vertex
• Degree centrality
• How many other nodes does a node connect to
• Measure of local importance
Leaf nodes
Hub nodes

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Node Properties
• Betweeness centrality
• How often a node is present on shortest paths through a network
• Measure of bottlenecks in network communication
• More global measure of importance
Hub and bottleneck
Hub and not a bottleneck

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Network Science
• Community structure (modules, cliques, clustering)
• Collection of vertices more connected to each other than to the rest of the
network
• Communities: functional organization of complex networks

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 Random network
 Gaussian degree
distribution
 As vulnerable to
random failure as to
targeted
 Vulnerability
depends on number
of connections
Network Science

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Network Science
 Biological network
 Power-law degree
distribution
 No inherent ‘scale’
 Structure at all levels
 Robustness
 Resists random node
deletion
 Brittle
 Vulnerable to targeted
node deletion

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Application to Biological Networks
Perturbation of a protein-protein interaction network

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Application to Biological Networks
Interventions need to be both multiple and specific

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Application to Biological Networks
Interventions need to be both multiple and specific

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And nothing much happens….
Application to Biological Networks
Make 5 random interventions

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And big things can happen…And nothing much happens….
Application to Biological Networks
Make 5 targeted interventions

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Drug Discovery using Networks
 Bioinformatics
 Cellular networks
• Protein–protein interaction networks
• Signal transduction and gene regulation networks
• Metabolic networks
 Distinction reflects experimental techniques
 Real cellular network is integration of all three
 Compound-Protein Interaction Database

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Network Construction
 Requires detailed biological insight
 Literature searching
 Pathway analysis
 Single network v’s multiple
 Disease network compared to normal
 Network validation
 Node score for key proteins

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Kinase GPCR
Second messengers e.g. cGMP,
cAMP
Other receptor types enzyme
Basal impact signature of a drug can be very large and a large signature appears to be critical for efficacy
Drug and metabolite promiscuity Multiple drug
metabolites
Pleiotropy
substrate
s
gene
s
Compounds are Promiscuous Binders and Pleiotropic in Action

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 E-Therapeutics
In-house Toolset
 Currently being
prepared for
patenting
 Allows identification of optimal known
compounds to impact a network of interest
 Usually generates structurally diverse hits
Proprietary Chemoinformatics

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 Lead anti-cancer candidate
 Passed Phase 1 trials
 Entering Phase 1b
 Target template from:
 Experimental binding footprint
 Literature Glioma network
 Combined to generate multiple
target networks
Dexanabinol

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Dexanabinol Binding Footprint
 CEREP studies of Dexanabinol identified:
 66 proteins with measureable interaction with Dex

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 Application of proprietary chemoinformatics to target
networks generates a ranked list of candidate
compounds
 Additional filtering based on IP and ADME/Tox
 Final list of 100 selected for testing
 Cytotoxicity assay against three cancer cell lines
 U-87 MG, Hs578.T and OE21.
 85 compounds sourced
 Screening performed by Biofocus
Experimental Methods

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Results
Number of Cell
Lines
Active at
100µM
Active at
15µM
0 33 71
1 12 9
2 13 3
3 27 2
 Over 50% weakly active potential leads
 14 highly active candidates
 Structurally highly diverse set

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Conclusions
 Network Pharmacology be used to
describe and model disease systems.
 E-Therapeutics can identify compounds
that impact the model system.

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Further Work
 Larger scale test of 200 additional
compounds
 Non-cancer cell line to assess therapeutic
index
 Comparison test of 200 compounds
generated using structural similarity
 Using Cresset Blaze screening software

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Thanks
 The E-Therapeutics Discovery Team
 Jonny Wray
 Brendan Jackson
 Victoria Flores
 Marie Weston
 Andreas Gessner
 Everyone at Cresset!