Our Predictive Computational Discovery Approach

We discover novel immuno-oncology (I/O) drug targets and biomarkers through a unique, predictive, cloud-based computational approach.

Our proprietary in-silico targets and biomarkers discovery platform combines our expertise in data sciences as applied in the analysis of vast amounts of publicly available and proprietary data sets. Our multi-omics data analysis is designed to identify first-in-class drug target candidates, which are generally difficult to identify using traditional experimental approaches. We contend that biology, including targets and biomarkers discovery, consists of complex scientific systems requiring an integrative multi-dimensional approach relying on multiple tools to generate the best results, and should not be bound to a specific technology. Our target discovery process is a flexible process enabling tailor-made solutions designed to address unmet clinical needs and consists of a toolbox of various omics data, a suite of computational solutions and purpose-built algorithms, augmented with human expertise to optimize and review the output.  We have demonstrated the applicability of our computational discovery approach in I/O and progressed in-silico predicted targets such as PVRIG, TIGIT and ILDR2 to the clinic.

To date, our discovery capabilities have resulted in the following achievements:

  • An innovative immuno-oncology pipeline: three therapeutic programs addressing targets that we have discovered computationally and validated in-house are currently in Phase 1 clinical trials: COM701 and COM902 being developed by us, and BAY 1905254 being developed by Bayer.
  • I/O strategic collaborations with both leading pharmaceutical companies and academia. 
  • Patents and publications: over 120 granted and pending patents and over 80 peer reviewed publications.

Predictive discovery of novel immune checkpoints:

A key Compugen accomplishment in this field is the discovery of novel protein members belonging to various known and clinically important protein families by our predictive computational discovery platform. The platform was developed for the identification of novel immune checkpoints, and more specifically, immunomodulators belonging to the B7/CD28 protein family of co-stimulators/co-inhibitors. The platform consist of specialized algorithms developed internally based on protein characteristics among known B7/CD28 proteins, such as gene structure, protein domains, predicted cellular localization and expression pattern and was applied for the identification of new immune checkpoints.

Predictive discovery of myeloid targets:

In order to identify myeloid targets, we have used a combination of our discovery approaches,  which incorporate vast amounts of multi-dimensional omics data from a wide variety of data sources, for the discovery of targets that are expressed within the suppressive myeloid lineages, such as tumor associated macrophages (TAMs). TAMs are an important component of the tumor microenvironment and play a major role in creating the immunosuppressive environment that enables tumor development. Proteins having the potential to modulate the tumor microenvironment may serve as potential targets for cancer immunotherapy.

Predictive discovery of mechanisms driving immune resistance:

More recently, we have expanded our discovery efforts to include the discovery of drug targets involved in driving mechanisms of immune resistance to PD-1 blockers. In many cases, anti-PD-1 antibodies are rendered ineffective due to the patient developing adaptive immune resistance following treatment. Therefore, with the increased use of PD-1 blockers in various cancer indications, we are starting to see many patients relapsing following treatment. Discovering new therapeutic solutions for these patients has become an area of high interest, with a dynamic competitive landscape.

To this end, we are enhancing our computational discovery platform to identify proteins and pathways which are driving immune resistance mechanisms to PD-1 therapy. With our computational discovery platform, we attempt to overcome certain challenges in the field; the limited publicly available data derived from cancer patients with clinical annotation as to their PD-1 response as well as the subtle molecular signals seen in many analytical approaches differentiating responders and non-responders to PD-1 treatment. We aim for the enhanced platform to provide us with new insights into such immune resistance mechanisms to enable the discovery of new potentially worthy drug targets and biomarkers.

Target Validation

Our predictive computational target discovery process is followed by a therapeutically focused validation phase, in which the candidates undergo an extensive set of experiments to confirm the in-silico prediction and provide additional detailed data as to their relevance and potential as drug targets for therapeutic development. This validation process is performed in-house as well as via scientific collaborations with top academic laboratories. Promising validated targets are then advanced in our mAb therapeutics pipeline. The experimental validation results are fed back towards continuous optimization of our proprietary algorithms and in-silico prediction of novel targets and biomarkers.


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