Oncology drug development makes up almost half of the annual R&D expenditure of biopharmaceutical companies. Only 10% of drugs advanced to the clinical stage are successfully brought to market, with half of all failures attributed to lack of clinical efficacy. This means that approximately $60 billion dollars are spent annually on unsuccessful drugs.
Predictive Oncology addresses this disparity by utilizing its extensive on-demand biobank of 150,000 live cell tumor samples and the application of its AI-driven high-throughput screening (HTS) and high-content imaging (HCI), to enable drug discovery and drug development teams to analyze and model patient response to drug candidates.
This integrated approach of applying AI-driven drug discovery to capture the heterogeneity of patient response profiles well in advance of clinical trials can significantly increase the probability of technical success by removing years of experimentation from the drug discovery and development timeline.
By the numbers
The application of artificial intelligence to the analysis of more than 150,000 specimens in Predictive Oncology’s on-demand biobank of live-cell tumor samples enables drug discovery and development partners to capture the heterogeneity of patient response profiles well in advance of clinical trials and to significantly increase the probability of technical success.
Download the white paper today
Want to learn more? Download our white paper on biobank reproducibility for an in-depth look at how our biobank supports groundbreaking research in oncology.