The breakthrough in pancreatic cancer treatment emerged in Chicago this spring with the introduction of daraxonrasib, a drug that nearly doubled survival rates for patients with advanced disease. During the 2026 American Society of Clinical Oncology meeting, attendees stood for 42 seconds as phase 3 RASolute 302 trial results appeared on screen, showing patients on the drug lived an average of 13.2 months compared to 6.7 months with chemotherapy alone. This marked a rare moment of progress in a field where advancements had previously stalled.
For Peg Squier, now leading medical affairs at Incyte, the standing ovation carried personal significance. She had previously treated patients with drugs that failed to deliver results. The FDA approved daraxonrasib—commercially known as RasonqueAugust 26, 2026, six months ahead of its original timeline. However, the rapid development masked ongoing challenges: patients demanded immediate access while payers and clinical guidelines struggled to keep pace.
By the time the drug appeared in the National Comprehensive Cancer Network guidelines, nearly three months after approval, some patients had already secured access through alternative pathways. Expanded access programs became the primary solution, though these created inequities by favoring patients already connected to academic medical centers.
The drug’s development reflects a broader transformation in how new therapies are discovered. A decade ago, researchers provided chemists with 20 new molecules every few weeks for testing. Today, artificial intelligence screens vast datasets before any laboratory work begins. This shift has compressed timelines, reducing the number of molecules synthesized while improving their effectiveness before human testing.
Some developers are now exploring ghost trials, simulated studies that predict enrollment challenges, patient retention, and logistical hurdles before trials begin. David Spigel of the Sarah Cannon Research Institute described testing whether studies could realistically recruit participants. However, these tools depend on accessible data, which remains a contentious issue.
Hospitals often restrict data access to retain patients, while pharmaceutical companies prioritize datasets relevant to their own therapies. This tension intensifies when patients themselves claim ownership rights, raising questions about data control and who ultimately benefits.
While AI has accelerated drug discovery in oncology, challenges remain in delivering these treatments to patients outside major medical centers. Vicky Brown of Boehringer Ingelheim noted that up to 80% of cancer patients receive care in community settings, where clinical trials and advanced testing are often limited. Decentralized trials using telemedicine and remote monitoring remain uncommon in oncology, where treatments are frequently toxic and patients are often frail.
Despite advancements in discovery tools, the gap between breakthroughs and real-world availability persists.
