Pharma has invested heavily in genomics and “big data” to understand each patient’s genome to target therapies, yet success rates for targeted therapies are low and uptake in clinical practice is patchy. There is a growing realization now that “just genomics” is not enough and a clear unmet need for a multi-omic approach, which may offer a greater chance of success, but such data is difficult to access quickly. Few comprehensive, multi-omic datasets exist and it is time consuming to initiate prospective data collection especially in cancer.
Predictive Oncology has a solution to Pharma’s need for multi-omic data by leveraging two unique assets from its Helomics division.
Multi-omics models capable of predicting drug response have both research and clinical applications. Predictive Oncology intends to combine these predictive models with its smart tumor profiling platform in clinical and translational research projects with Pharma, BioPharma and Diagnostic companies.
|Research||Drug Development||Clinical Decision Support|
|Biomarker discovery||Patient enrichment||Patient stratification|
|Drug discovery||Clinical trial optimization||Patient risk assessment|
|Drug-repurposing||Adaptive trials||Treatment selection|