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!