targeting egfr signal transduction pathway by anticancer drugs l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Targeting EGFR Signal Transduction Pathway by Anticancer Drugs PowerPoint Presentation
Download Presentation
Targeting EGFR Signal Transduction Pathway by Anticancer Drugs

Loading in 2 Seconds...

play fullscreen
1 / 27
dareh

Targeting EGFR Signal Transduction Pathway by Anticancer Drugs - PowerPoint PPT Presentation

441 Views
Download Presentation
Targeting EGFR Signal Transduction Pathway by Anticancer Drugs
An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Targeting EGFR Signal Transduction Pathway by Anticancer Drugs First Infobiomed Training Challenge, Barcelona/Villadrau Ignasi Belda Marilena Garefalaki Mark McAuley Eva van Soest Karin Pike-Overzet

  2. Group members • Ignasi, Spain: Drug design • Marilena, Greece: Biology • Mark, Ireland: Mathematical modeling • Eva, the Netherlands: Epidemiology • Karin, the Netherlands: Immunology

  3. Why targeting EGFR signaling pathway? • Implicated in multiple human cancers • Can promote multiple properties of neoplastic cells- proliferation, migration, angiogenesis, stromal invasion, resistance to apoptosis • Hyperactivation mechanisms: • Autocrine secretory loop (ligand overproduction) • Paracrine growth –ligand produced by adjacent cells • Constitutive receptor activation

  4. Initial contributions • Biologist: general introduction pathway • Epidemiologist: overview clinical drug testing • Immunologist: Micro-array data analysis • Mathematical modeler: overview basic modeling • Drug designer: drug designing strategies

  5. Process of finding the target • 4 segments of the pathway to target: • Ligands • Receptor • Intracellular cascade • Tyrosine kinase inhibitor • Other inhibitors • Transciption factors • What has been done? What worked? What didn’t work? Why not?

  6. Sources of information • Group-member knowledge • Pubmed for literature • Microarray Databases • In-house expertise • External contacts: • Colleagues • Author of interesting papers • Modeling Software: Systems Biology Markup Language (MathSBML)

  7. Ligands Advantages: • Accessible target • Not published yet (structure and mechanisms recently solved) • Structural similarities • Immunoresponse-derived side-effect not expected • Disadvantages: • Expected side-effects (systemic presence)

  8. Receptor/Antibody therapy Advantages: • Clinical data available • Overexpression in cancer • Extracellular • Disadvantages: • With use of antibodies: side-effects • Low response rate • Not novel

  9. Intracellular cascade Tyrosine kinase inhibitors/others Advantages: • Clinical information available • Promising in in vitro experiments • Expression data available • Disadvantages: • Low response rate in clinical trials • One membrane to cross • Alternative pathway resistance (temporary response)

  10. Transcription factors Advantages: • Novel • Disadvantages: • Two membranes to cross • No data to proceed

  11. Focusing on a novel approach • Ligands (target known) • Expertise: epidemiologist, biologist, drug designer • Signaling cascade (target unknown) • Expertise: immunologist, mathematical modeler Brainstorm and feedback regularly together

  12. Target 1: ligand Marmor et al., 2004

  13. Mathematical modeling • Rationale: • Representation of dynamics of ligand binding, internalization and degradation • Establishing of links between physiological responses and levels of receptor activation • Tool for decision making: • Optimal set of ligands to target: balance • As little blocking as possible • As much effect as possible

  14. Receptors RL complexes Dimers Ligands

  15. Biological approach • Certain ligand expression is tissue dependent • Overexpression of EGFR1, EGFR2, EGF-like ligands in solid tumors

  16. Drug design approach • Design strategies: • Structure-based design • De novo virtual drug design • Combinatorial chemistry – Chemical Graph Identifier – QSAR • High-throughput screening of natural products

  17. In vivo model and test Knock-in models Induced cancer Toxicity – immune response studies In vitro model and test Binding assays: Micro-calorimetry Surface plasmon resonance NMR (chemical shifts) Cellular experiments: Inhibition experiments Toxicity Degradation experiments Drug design approach

  18. Drug delivery perspective • Organ specific delivery to minimize side-effects, e.g.: • Lung: • Inhaling delivery techniques • Organ specific proteases to activate pro-drugs (e.g. Human L-Capsine) • Brain: • Blood-brain barrier crossing drugs

  19. Summary Cell Biology Mathematical model Target selection Drug design (Pre)clinical testing

  20. Target 2: EGFR signal transduction pathway A comprehensive pathway map of epidermal growth factor receptor signaling Kanae Oda et al. Molecular Systems Biology doi: 10.1038/msb4100014 published online: 25 May 2005

  21. Micro array technology

  22. Cancer expression data Map expression to pathway Identify potential targets Drug design Strategy for finding new targets in the EGFR pathway Databases PathwayAssist, SCIpath (www.sbml.org) Visually (Structure based) Drug design

  23. Expression data visualized in a pathway

  24. Cancer expression data Availability and Format Requesting data from ‘source’ Use conversion tables Map expression to pathway Technolology available Identify potential targets Drug design Structure of target solved? Perform NMR, X-ray crystallography Problems and solutions Feasible approach for target identification

  25. Advice for future training challenges • Know general background of other participants • Proper introduction of experts • Emphasize the challenge rather than the competitions

  26. Lessons learned • Easier than expected to work with scientists from different research fields • Creative ideas arise from interdisciplinary discussions • Unstructured distributed leadership works Future collaborations • Ask advice in different disciplines

  27. Thank you INFOBIOMED!