Seattle Genetics to Evaluate Compugen Cancer Target
Deal gives firm option to exclusively license related mAbs.!--h2>
Compugen signed a research collaboration with Seattle Genetics covering a Compugen-discovered oncology target. The agreement provides Seattle Genetics with an initial evaluation period and an option for an exclusive, worldwide milestone- and royalty-bearing license for development and commercialization of mAb therapeutics addressing this target.
The target is a new splice variant of a known oncology target. It was initially predicted in silico through Compugen’s mAb targets discovery platform. The molecule’s existence and overexpression in several of the most prevalent solid cancers was recently demonstrated in independent studies, according to the company.
Compugen’s technology relies on its LEADS and MED capabilities, two computational biology infrastructure platforms. The LEADS platform provides a view of the human transcriptome, proteome, and peptidome and can be leveraged for the discovery of genes, transcripts, and proteins. It includes gene information and annotation, such as splice variants, antisense genes, SNPs, novel genes, RNA editing, etc. At the protein level, LEADS provides full protein annotation including homologies, domain information, subcellular localization, peptide prediction, and novelty status.
The MED platform is an integrated database composed of results from more than 40,000 public and proprietary microarray experiments, Compugen explains. Data is normalized and organized into approximately 1,400 therapeutically relevant conditions (i.e. normal tissues, malignant tissues, tissues from drug-treated patients, etc.). Utilizing a query interface, the MED platform allows simultaneous examination of gene and pathway expression across 1,400 conditions and tissues as well as 40,000 microarray experiments.
In addition to incorporating MED and LEADS, the mAb targets discovery platform utilizes multiple data sources and algorithms to predict a large number of novel membrane proteins that can serve as targets for antibody therapeutics. Selection of appropriate candidates is accomplished using submodules of algorithms and other computational tools developed specifically for each disease state or protein family.