
Drug repurposing using the Predictive Oncology machine learning approach and proprietary biobank of frozen dissociated tumor cells (DTCs) is outlined in this new white paper.
Unlocking the Potential of Abandoned Oncology Drugs with AI
Every year, promising oncology drugs are abandoned after early clinical trial setbacks because they don’t deliver in their original indication. With the cost of new drug development topping $2 billion, repurposing these “forgotten” compounds could be a faster, more cost-effective way to get treatments to patients who need them.
At Predictive Oncology, we applied our active machine learning platform and extensive biobank of patient tumor samples to explore how these shelved drugs might work in different cancers. Our AI-driven approach let us dramatically cut down the number of wet lab experiments needed while uncovering exciting new opportunities for these treatments.
The results? Our study revealed several drugs with potential to outperform standard therapies in certain cancers—and they could be ready for the clinic much quicker than any single new drug.
Want to see which drugs showed the most promise and how we found them?
Fill out the form below to download the full whitepaper and explore the complete data and analysis.