Predictive Oncology accelerates cancer treatment using AI and machine learning

On May 22, 2025, in Pittsburgh, Pennsylvania, Predictive Oncology Inc. announced its advances in employing artificial intelligence (AI) and machine learning to enhance the process of drug discovery for cancer treatments. The company has a biobank consisting of over 150,000 live cell tumor samples and corresponding drug response data, which positions it strongly in the race for novel oncology solutions. Recently, Predictive Oncology achieved a significant milestone by developing predictive tumor response models for 21 new compounds, specifically targeting prevalent cancers such as breast, colon, and ovarian. This progress signifies a major leap towards personalized medicine in oncology, showcasing the potential of modern technology in addressing complex health challenges. The backdrop to this innovation coincides with Regeneron Pharmaceuticals' recent acquisition of 23andMe for $256 million, marking a notable trend in the industry towards data-driven drug discovery practices. This aligns with a previous successful partnership between 23andMe and GlaxoSmithKline (GSK) that began in 2018, reinforcing the growing reliance on genomic and health data for drug development. As Regeneron integrates 23andMe's extensive consumer genetic and health information into its own research and development, it demonstrates a robust industry shift focusing on utilizing personal health data for therapeutic advancements. Predictive Oncology's proprietary active machine learning platform enables them to model tumor responses across various cancer types. The company’s approach is underscored by a combination of empirical validation and advanced technologies, allowing them not only to model drug responses theoretically but also to conduct real-world testing in their CLIA-certified laboratory. This dramatically accelerates timelines and improves success rates in drug development by de-risking later phases of research and development. The potential to streamline the process presents significant implications for patients, bringing hope for more timely and effective treatment options. As highlighted by Raymond Vennare, Chairman and CEO of Predictive Oncology, the integration of artificial intelligence and machine learning is transforming oncology, not merely as a novel trend but rather as a foundational driving force in precision medicine. The vision for future drug discovery hinges on the convergence of genomics, machine learning, and biological data, indicating a pivotal change in how cancer treatments are developed and personalized for patients. With industry giants like Regeneron investing in consumer genomic data, the focus on data-driven methodologies in drug discovery is becoming increasingly pronounced, heralding a new era of precision medicine in the fight against cancer.