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DRP™ Companion Diagnostic


The unique biomarker technology, the Drug Response Predictor (DRP™) platform, includes proprietary systems biology analytics and a big-data algorithm, combined with transcriptomic expression profiling (messenger RNA (mRNA) and micro RNA (miRNA)), to generate drug-specific DRP™ Biomarkers used for predicting the likelihood of patient response to a given drug. The DRP™ technology uses a filter, comprising of drug response data from a large collection of patient tumor biopsies (n = ~3,000), to refine preliminary drug response data from the expression profiling of the NCI60 cancer cell line panel (or equivalent panels) after exposure to a particular candidate drug. A “response prediction score” is a bioinformatic and mathematical prediction of a particular patient’s likelihood of response to the drug, and this is generated based on a comparison of a patient’s own transcriptomic profile against the DRP™ Biomarker for the drug. Each DRP™ Biomarker is intended to be used as a “companion diagnostic” (CDx) for a given drug, meaning that regulatory agencies (such as the FDA in the U.S.) require the CDx to be used to identify likely responder patients before the drug may be prescribed to a given patient.

By using the DRP™ we will be able to add insight to what cellular events are driving an individual’s cancer, including which genes are correlated and which genes are not correlated to therapeutic response, what is important for an individual’s cancer to keep growing and locate vulnerabilities that can be attacked – i.e. which drugs can most effectively be used to fight an individual’s cancer. The DRP™ technology enables drug developers to focus on important and relevant data from the lab, results from the clinic and the personal data from the individual patients’ genes to deliver precise data that, once analyzed via the DRP tool , can predict whether the patient will benefit from one of 2X’s drugs.

We are developing drug specific DRP™’s for each of our pipeline products and these will enable us to identify and predict which patients are most likely to respond and thereby benefit from a given pipeline product.