We use the Diagnostic Cortex® platform, a system that employs modern artificial intelligence techniques, to design diagnostic tests that are reproducible, robust, and answer critical clinical questions for our partners.
How often has your biomarker strategy for a drug in your pipeline not validated in clinical trials? Classical biomarker discovery solutions use a hypothesis-dependent approach based on presumed biological rationale for the drug, but unfortunately, there remains a high risk of clinical failure.
We are at the forefront of personalized medicine using a hypothesis-independent approach to biomarker discovery. We are able to incorporate multi-omic biomarker data, clinico-pathologic characteristics, and clinical outcome data into our artificial intelligence (AI) platform to design diagnostic tests that identify patients who are likely to benefit from your therapies. Once a test is developed, a biological pathway correlation is performed to help relate to your drugs mechanism of action.
We have our own proprietary AI platform, the Diagnostic Cortex®, which incorporates patients’ multi-omic data, clinico-pathologic characteristics, and clinical outcome data to develop robust clinical diagnostic tests for drugs in your clinical development programs.
Our Diagnostic Cortex AI platform incorporates traditional machine learning concepts and advances in deep learning. It was optimized for test development in cases with more attributes than samples, with the goal of minimizing the potential for overfitting and promoting the ability of the developed tests to generalize to unseen datasets.
We have successfully developed both standalone and companion diagnostic tests for target populations, realizing the potential of precision medicine. Our tests support treatment decisions including the selection of immunotherapies, novel therapy combinations, or alternative treatment pathways.
A test developed for anti-PD-1 therapy in second-line NSCLC that identifies a group of patients that likely won’t demonstrate long term benefit from nivolumab and is likely predictive for nivolumab vs. docetaxel.
Source: Goldberg 2017 (SITC poster)
We discover hypothesis-independent tests with AI that answer critical clinical questions and identify distinct patient groups. The question then becomes: what is the biology behind the classification of these patient groups? Answering this question reveals the biological underpinnings for the test; it can also inform combination therapies or even suggest expansion indications for patients who have similar underlying disease biology.