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Compugen Presents Prediction Based Discovery Strategy at “Targets and Strategies for Drug Discovery” Conference
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Compugen Presents Prediction Based Discovery Strategy at “Targets and Strategies for Drug Discovery” Conference

Compugen Presents Prediction Based Discovery Strategy at “Targets and Strategies for Drug Discovery” Conference
News

Compugen Presents Prediction Based Discovery Strategy at “Targets and Strategies for Drug Discovery” Conference

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Speaking at the Targets and Strategies in Drug Discovery Summit in Las Vegas, Dr. Gady Cojocaru, Head of Target Discovery at Compugen Ltd., presented Compugen’s prediction based discovery strategy by focusing on two of the methodologies being utilized by the Company for the identification of novel targets for monoclonal antibody (mAb) cancer therapy.

Also included in the presentation was the disclosure of CGEN-15022 and CGEN-15092, two of the B7/CD28-like proteins discovered by the Company that have shown potential in initial validation studies for this therapeutic use.

Dr. Cojocaru stated, “As with all of our discovery methodologies, or platforms, the two methodologies being presented today rely on our underlying and continuously growing predictive discovery infrastructure. The first discovery platform was created for the prediction of novel cancer targets, including drug resistant and advanced stage cancer. This methodology relies in large part on query algorithms focused on the integrated statistical analysis of both expression and clinical data within the infrastructure and has resulted to date in multiple discoveries, such as the previously disclosed CGEN-928 with potential use for late stage, aggressive and drug resistant multiple myeloma and Rituximab resistant non-Hodgkin’s lymphoma.”

Dr. Cojocaru continued, “The second platform was developed to predict targets specifically for the use of monoclonal antibodies in cancer immunotherapy, and is based on a more general Compugen platform for identifying novel members of protein families of high industry interest. Since most traditional approaches for identifying novel members of known protein families are largely based on sequence homology or function, the set of query algorithms for this platform was designed to first discover other types of characteristics shared between known members of the family of interest. Based on this, we select proteins from our discovery infrastructure that share these characteristics and therefore could potentially be unknown family members.”

Dr. Cojocaru continued, “The protein family on which we focused the second platform was the B7/CD28 family, since known members of this family are of high industry interest, have low sequence homology, and have been shown to be appropriate targets for cancer immunotherapy. Our efforts to date with this platform have been very successful, significantly adding to the number of B7/CD28-like proteins previously known. Included in these Compugen discovered molecules are the previously disclosed CGEN-15001T, and also CGEN-15022 and CGEN-15092 which are being disclosed today. CGEN-15022 and CGEN-15092 are the membrane proteins related to the previously disclosed soluble Fc proteins for autoimmune disease, CGEN-15021 and CGEN-15091, respectively. Based on earlier experimental results relating to CGEN-15022 and CGEN-15092, both of which are in our Pipeline Program, these molecules appear to be very promising targets for cancer mAb based therapy and were recently selected by Compugen to be advanced to the mAb development stage.”

Dr. Cojocaru continued, “As previously stated, our ability to rapidly create multiple discovery methodologies in areas of high industry interest rests on our unique and proprietary discovery infrastructure incorporating predictive understandings of numerous biological phenomena at the molecular level. These predictive understandings were accomplished during a decade long and on-going pioneering research effort by Compugen’s scientists and are based on sophisticated analysis of huge amounts of data of various types, such as genetic, molecular, structural, clinical, biological pathways and others. A key aspect of this effort was the development of algorithmic building blocks and other proprietary techniques for the accurate integration of this enormous amount and diverse data. This has resulted in our proven ability to utilize our discovery infrastructure to provide output in the form of meaningful biological information, in addition to continuing enhancement of the infrastructure itself.”

Dr. Cojocaru continued, “As demonstrated by the two methodologies being presented today, a critical component of our infrastructure, which is different for each therapeutic or diagnostic need directed methodology, is development of a set of query algorithms designed for the identification of molecules predicted to address that specific need. Following these infrastructure based activities, all of which are performed totally by computer, the predicted product candidates for the clinical need of interest are validated utilizing well-accepted laboratory experimental procedures. In addition to providing validation of the predicted candidates, these experimental results provide key information for further refining the query algorithms and other aspects of the infrastructure.”

Dr. Cojocaru concluded, “It has taken over a decade for Compugen to create the comprehensive infrastructure required for predictive drug discovery. However, based on the substantial inherent advantages of hypothesis driven research and our excellent results to date, we look forward to both the continuing development and commercialization of our existing discoveries, and an increasing number of attractive product candidates within our focus areas of oncology and immunology.”

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