Compugen Leads Consortium to Model Cancer Related Kinase Pathway

European Commission to Fund SIMAP Consortium

10-Mar-2006

Compugen Ltd. announced that it is leading a consortium for the development of a platform to simulate the MAP-kinase pathway, a signaling pathway related to cancer which is already targeted by a number of cancer therapies and diagnostic tests.

The Simulation Modeling of the MAP-kinase Pathway (SIMAP) consortium is funded by the European Commission which has allocated 3.1 million Euro for the project over a three year period. In addition to Compugen, the consortium includes Aureus Pharma of France, Consejo Superior de Investigaciones Científicas of Spain, the Institut De Recerca Hospital Universitari Vall De Hebron from Spain, Istituto Nazionale Tumori of Milan-Italy, the Max Planck Institute for Infection Biology from Germany, the University of Glasgow from the UK, the Weizmann Institute of Science from Israel, and Arttic Israel, a consultancy and management company.

The MAP-kinase pathway is a major pathway that relays signals from the plasma membrane into the nucleus. A deep understanding of this pathway is important for the development of rational anti-cancer therapies. The SIMAP consortium intends to develop a comprehensive and robust simulation model of the pathway, which will incorporate data from the literature, as well as experimental and clinical work. The model is expected to create qualitative predictions, followed by experimental verification. It is expected to integrate and analyze data from various types of resources ranging from single molecule information, to pathway modeling, to clinical data and patients' response.

This approach is expected to enable hypothesis-driven research aimed at the establishment of systems level computational platforms available for various pharmaceutical applications. The concepts and methods intended to be developed could help in the design of new therapeutic drugs, decrease the attrition rate of new drugs and make it possible to select patients for treatment on the basis of individual parameters. Model-driven predictions regarding the impact of drug combinations could allow dramatic improvement in the design of pre-clinical and clinical trials, enhance patient response and limit adverse effects of drugs.

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