Predictive Oncology is a knowledge driven precision medicine company focused on applying data and artificial intelligence to personalized medicine and drug discovery. We apply our smart tumor profiling and AI platform to extensive genomic and biomarker patient data sets to build predictive models of tumor drug response to improve clinical outcomes for the cancer patients of today and tomorrow.
THE UNMET NEED IN PRECISION MEDICINE
Pharma has invested heavily in genomics and “big data” to understand each patient’s genome to deliver targeted therapeutics, yet success rates for targeted therapies are low and uptake in clinical practice is patchy. There’s a growing realization that “just genomics” is not enough to achieve the promise of personalized therapeutics. A clear unmet need has emerged for a multi-omic approach that may offer a much greater chance of success. However, few comprehensive, multi-omic datasets exist and such data is difficult to access quickly as it is both costly and time consuming to initiate prospective data collection – especially in cancer.
ANSWERING THE NEED– LEVERAGING THE HELOMICS ASSET
Predictive Oncology currently has about 150,000 clinically validated cases on its molecular information platform, 38,000+ specific to ovarian cancer. The data in POAI’s molecular information platform are highly differentiated, having both drug response data and access to historical outcome data from those patient samples. Predictive Oncology intends to generate additional sequence data from these tumor samples to deliver on the clear unmet market need across the pharmaceutical industry for a multi-omic approach to new drug development and, most importantly, improved patient outcomes.
APPLICATION OF PREDICTIVE MODELS
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.