Identification of novel potential anti-cancer agents using
network pharmacology based computational modelling
Name: Ben Allen
Organisation: E-Therapeutics PLC
e-Therapeutics plc
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?NodeEdge
o Network Propertieso Node Propertieso 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 NetworksPerturbation of a protein-protein interaction network
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Application to Biological NetworksInterventions need to be both multiple and specific
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Application to Biological NetworksInterventions need to be both multiple and specific
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And nothing much happens….
Application to Biological NetworksMake 5 random interventions
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And big things can happen…And nothing much happens….
Application to Biological NetworksMake 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 multipleDisease 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
substrates
genes
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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ResultsNumber of Cell
LinesActive at 100µM
Active at 15µM
0 33 711 12 92 13 33 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
indexComparison 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!