network-based Target Ranking for Polypharmacological Therapies

Francesca Vitali, Francesca Mulas, Pietro Marini and Riccardo Bellazzi

NETWORK-BASED TARGET RANKING FOR POLYPHARMACOLOGICAL THERAPIES

University of Pavia, Italy

Angelo Nuzzo IIT@SEMM, Milan, 2011

Drug Discovery


Drug Discovery Today

Decline in:

• Clinical development

• Productivity

Standard approach MAGIC BULLET

SINGLE TARGET DRUGS

Emerging approach POLYPHARMACOLOGY


Polypharmacology

MULTI TARGET DRUGS

• Collective weak interactions vs single strong interaction

• Improve therapeutic efficacy

- Multi-factorial diseases (e.g. cancer)

• Avoid

- Adaptive drug resistance

Drug

Protein


Network-based Polypharmacology

Prior knowledge on complex diseases

DATA SOURCES +

PROTEIN INTERACTION NETWORKS

Protein

Interaction


Method

Disease of interest

Network design

Network targeting

Scoring of target combinations

Ad hoc analysis of drug agents related to the selected targets


Method

Disease of interest

Network design

Network targeting



Type 2 Diabetes Mellitus

(T2DM)


Method

Disease of interest

Network design

Network targeting




Network design

NODES Proteins

EDGES Interactions

Human proteins involved T2DM

pathways

Network

Protein – Protein Interaction Repository


Biological Network Features

• Interactions

• Experimental evidence

- Not considering:

Literature mining, co-occurence, ecc.

• Confidence Score › 0.7 (high confidence)

NODES 587

EDGES 3683


Disease Network

Network genes

87 disease proteins

Up- and down-regulated genes

Diabetes vs control


Method

Disease of interest

Network design

Network targeting




Network Targeting

• Node constraints:

• Druggable

• Not Hub

• High Bridging Centrality


Druggability

LOCALIZATION

DRUG INTERACTIONS

• Cell membrane

• Extracellular space

DRUGGABLE PROTEINS

206 + 108

Chemical – Protein Interaction Repository


Hub

• Hub

1. High # neighbours

2. Located between highly connected regions

Traditional pharmacology

DISCARDED AS POTENTIAL POLYPHARMACOLOGICAL TARGET

• Hub discovery method:

DISCARDED HUB 14

Vallabhajosyula et al. Identifying hubs in protein interaction networks.PLoS ONE, 4(4):e5344, 2009.


Betweenness centrality

• Global bridging properties

• Influence of a node in the spread of information through the network

Bridging Coefficient

• Local bridging properties

• Penalizes hub

Bridging Centrality

Bridging centrality

BC = Bridging Coefficient

RWB = Random-walk Betweenness


Bridging Centrality

Bridging centrality

BC = Bridging Coefficient

RWB = Random-walk Betweenness

Bridge node first 25% of the nodes ordered by bridging centrality

BRIDGING PROTEINS 147

Hwang et al. Bridging centrality: Identifying bridging nodes in scale-free networks.Knowledge Discovery and Data mining, 2006


Target selection

47 target proteins

“Sources ” of pharmacological actions

Druggable

Non hub

Bridge nodes


Method

Disease of interest

Network design

Network targeting




Drug Synergy

• Reachability of a single Disease Protein

Wi,ji j

• Reachability of all DPs

Sh = shortest path

w i,j = edge weight between i and j

(i,j) = node pair

Np = # disease proteins

T


Drug Synergy

TRIPLETS

• Low computational cost

• Upper limit to the number of drugs to be jointly delivered


TSDS

• TSDS (Topological Score of Drug Synergy) of a triplet

TSDS

• Null distribution:

TSDS for 50 000 triplets ofproteins randomly selectedfrom the network

Combinationsof 18 targets

88 triplets with

P-Value < 0.01


Method

Disease of interest

Network design

Network targeting




Target AnalysisFrequency


Target Analysis

Insulin-like

growth factor

family

Frequency



• EXPERIMENTAL EVIDENCE

• Negative regulator of insulin signalling TO BE INHIBITED

• Extract of Larrea Tridente

1. Decrease

• plasma glucose

• triglyceride concentrations

2. Inhibits IGF1R activity


Target Analysis

Alzheimer

Frequency


• Strongly altered expression of

IGF1R/IGF

Target Analysis

Alzheimer

• Recent Studies

T2DM Alzheimer

• Strongly altered expression of IGF1R/IGF

Frequency

• Patients with T2DM have a 2- to 3-fold increased risk for AD.



Only one standard target


Conclusion

• Network-based method for feasible identification of multi-component synergy

• Data integration

• Topological feature analysis

• Scoring system

Results:

• Disease-causative pathways

• Potential multi-target nodes

• To discover new therapies

• To support the standard therapies

Future steps:

• Testing on other complex diseases

• Application to directed network

Thanks.

NETWORK-BASED TARGET RANKING FOR POLYPHARMACOLOGICAL THERAPIES

Documents

network-based Target Ranking for Polypharmacological Therapies