The company’s technically, scientifically and commercially validated PEDAL platform is able to predict if a tumor will respond to certain drugs with 92% accuracy. This enables drug developers to more confidently understand which molecules may, or may not, be successful drug candidates before designing or conducting clinical trials. Predictive Oncology provides pharmaceutical companies and research institutions a robust AI/ML drug discovery engine in a CLIA laboratory environment. With approximately 40 employees, Predictive Oncology is headquartered in Pittsburgh, PA, a global life sciences hub and thriving center for innovation.
PEDAL: More than just an AI platform but a scientific methodology
Predictive Oncology is revolutionizing drug discovery as we know it by harnessing the power of artificial intelligence, scientific expertise and laboratory services to introduce the “human element” into the earliest phases of drug development. Predictive Oncology is the only AI-enabled company with an expansive proprietary biobank of 150,000 heterogenous human tumor samples across more than 130 tumor sub-types, a repository of more than 25 years of drug response data, 40,000 tissue blocks and a library of more than 200,000 pathology slides which are digitized and integrated into the PEDAL platform. The company’s CLIA wet lab capabilities ensure that every model that is generated in silico is validated in vitro. Together, these assets enable biopharma to reduce drug development costs, extend patent life, increase revenue and provide much-needed effective treatments to patients in their fight against cancer.
PEDAL: Accelerating drug discovery and mitigating risk
The average length of time to bring a drug to market is 12 years. Of the nearly 1,500 cancer drugs selected for clinical trials in the past 20 years, only 115 were approved. Less than 8% of all cancer drugs that enter Phase 1 clinical trials are ultimately approved by the FDA. In a world where that process costs nearly $650 million on average for a single cancer drug, a 92% rate of failure means billions of dollars and thousands of hours wasted.
