Kisun Pokharel19.05.11

Introduction

• Current platforms in understanding relationships between molecular structure and biological effects are structure-centeredo Dogma- selectivity imparts efficacy and safety is not supported

• The reality: target-based drug-discovery platforms are not able to predict drug-efficacy and the full spectrum of drugs in organismso Complexity after molecular interaction is extra-ordinarily complex

• Drug action: a coordinated response to multiple perturbations of cellular networks

• Thus, there is a need of shift from structure/target-based platform to system/network-based platform

• Challenging?o Fundamental rethinking of tranditional structure-function modelo Biological effects are not defined by intended effects of a medicine or primary

effects of disease

Network-based cause-effect relationships

Information flow in analysis of cause-effect relationships

• Information on heterogenous molecular interactions exhibit complex regulatory scheme

• Unlike reductionist approach, system-based strategies involve information flow within cellular and organism network systems

• System-based cause-effect analysis employ network topology models

• Disruption and production of macromolecular interactions: root of many diseases

Components of biomolecular networks

Overview of biological networks and network targets

• Nodes (discrete molecules) and edges (functional connections)

• Hubs – Nodes with higher no. of functional connections

• Network properties – Scale free, robust, correlated (between network distance and functional distance)

• Disease targets – bridging nodes

• Medicine effects

General steps in Network-based cause-effect analysis

( Construction of PPI network models)

• Identification of network-reachable proteinso Network reachability – term used to identify proteins involved with discrete

evento Strategy : identify known disease genes and map to proteins (on average a

disease is associated with 12 genes)o Alternatively by the use of Analytical techniques (Quantitative MS, Phospho-

peptide enrichment, aptamer technology, micro-western arrays, literature text mining)

• Identification of cellular components capable of interacting with network-reachable proteinso Curated protein interaction databaseso Problem: data variabilityo Solution: Integration of orthogonal molecular information and computational

models based on genomic and structural information

• Assessing viable routes for information flow between network-reachable proteinso Protein associations can be obtained by sorting protein-reachability profiles of

drugso Functional couplingo Through curated protein databases

Copyright 2011 discovery medicine

PPI disease networks

• Most diseases are multifactorial• Use of PPI models for considering cause-effect associations is

complicated• PPI networks models are refined whenever possible• Examples

o Huntington’s disease (Huntington gene + GPTase)o Ataxia

• Properties of disease networkso Diseased genes are not randomly positioned in a networko Disease states – resistant to perturbationso Protein hubs – highly conservedo Many diseases are results of small defects in many genes than large defects in

few

An example: Etiology of ataxia (lim et al., 2006)

PPI ataxia sub-network

Uses for disease network topology

• Identifying drug targetso Betweenness centrality and degree centrality - pharmacologically more

significanto Less connected nodes affecting pathways -> more attractive drug targetso Targeting bridging nodes to identify potential drug targets

• Multiple network targetso Attractive strategy for modifying phenotypeso Eg: non steroidal antiinflammatory drugs, antidepressants, anticancer drugso Challenge: link desired and undesired effects of such medicine

• Drug combinationso To target multiple sites within the same protein or multiple nodes within a

molecular networko Well known examples:

• Three drug combinations of reverse-transcriptase and protease inhibitors to treat HIV-infection

• Four drug combinations to treat non-Hodgkin’s lymphoma

• Drug repurposingo Imatinib: originally for chronic myelogenous leukemia; also to other cancerso Finasteride: originally for treating enlargement of prostrate gland but also for

treating male baldness

Use of disease network topologies to understand pharmacology of medicines

Copyright 2011 discovery medicine

Capturing information on drug effects

• Computer readable side-effect resource (SIDER)

• Text mining of mendilian database

• Sorting medicine-effect profiles

A PPI network view of medicine effects

Figure: Aligning protein interaction and drug-effect topologies of medicines

Caevets and Outlook

• Current models are unable to capture the dynamics of cellular signaling

• Long-term strategies – personalised medicine

Final Thoughts

• Broad range of heterogenous protein interaction and drug-effect data in IFA provides a new avenue for investigating cause-effect relationships in drug discovery

• Use of cause effect analysis in combination with predictive modeling experiments provides roadmap for identifying circuits regulating transitions between various protein network topologies involved in pharmacological outcomes and disease progressionUnable to capture the dynamics of cellular signaling

• The more detail and accurate the network topoloties are, the more we understand our biology, i.e higher success rate of new medicines, easy targets

Thanks for your attention!!