AI and supercomputing identify new cancer drug candidate that avoids toxic side effects

 

Developing new cancer drugs is risky because effective treatments often cause dangerous side effects. A team at Lawrence Livermore National Laboratory used powerful supercomputers and AI to solve this problem, creating a new drug candidate called BBO-10203. This drug targets a specific signaling pathway used by aggressive tumors to grow. In the past, drugs that attacked this pathway caused severe high blood sugar (hyperglycemia), making them unsafe for patients. By using supercomputers to simulate interactions at the atomic level, the researchers designed a molecule that hits the cancer target perfectly without disrupting the body's insulin levels.

This achievement highlights the power of using technology to design drugs before testing them in humans. In early laboratory tests, BBO-10203 successfully stopped tumor growth in RAS-driven cancers without causing the toxic blood sugar spikes seen with older drugs. This "bench-to-bedside" approach saves years of trial and error. For patients with difficult-to-treat cancers, this offers hope for a therapy that is both potent against the tumor and safe for the rest of the body, proving that AI can help build better medicines.

Read the original article at: https://medicalxpress.com/news/2025-06-cancer-drug-candidate-supercomputing-ai.html



Follow us on Instagram, Twitter, and Facebook to stay up to date with what's new in healthcare all around the world.

Comments

Post a Comment

Popular posts from this blog

Guesswork gone. A new scoring system (ST-RADS) predicts soft-tissue tumor malignancy with 99.2% accuracy.

Image
 Radiologists may soon have a powerful new standard for evaluating soft-tissue tumors. A study detailed in Radiology Business introduces "ST-RADS" (Soft-Tissue Tumor Reporting and Data System), a structured MRI scoring framework designed to replace the often vague descriptive reports currently in use. In a validation study involving roughly 200 patients, the ST-RADS system demonstrated exceptional precision, achieving a 99.2% accuracy rate in predicting malignancy—significantly outperforming the 92.8% accuracy of standard radiological reports. Crucially, the system was perfect (100% accuracy) in identifying benign tumors, a capability that could drastically reduce unnecessary biopsies and patient anxiety. By standardizing how these complex images are interpreted, ST-RADS offers a clear, objective roadmap for clinicians, ensuring that aggressive cancers are flagged immediately while harmless lumps are safely monitored without invasive intervention. Read the original artic...
Read more

The system is too slow. New data reveals that missed "waiting time" targets are hiding the true, deadly cost of cancer care.

Image
 A critical new analysis argues that current targets for cancer treatment waiting times are failing patients and obscuring the true extent of delays. The report highlights that arbitrary benchmarks such as the 62 day target from referral to treatment are frequently missed and do not account for the increasing complexity of modern cancer care. Patients requiring multiple diagnostic tests often face much longer waits that are not accurately reflected in official data. These delays are not merely administrative nuisances they significantly increase the risk of mortality.  The authors contend that simply setting stricter targets is ineffective without addressing the underlying lack of resources. Instead they call for a national learning system driven by data and collaboration to identify bottlenecks in real time and prioritize patients based on clinical urgency rather than outdated metrics. Read the original article at: http://www.bmj.com/content/392/bmj.s14.short?rss=1 Follow ...
Read more

Digital Oncology Insights: 4th December - 10th December

Image
  1. CTCeptor technology simultaneously isolates tumor cells and fibroblasts for better diagnosis. Diagnosing cancer accurately is often difficult because standard liquid biopsies can miss important cells hiding in the blood. To solve this, researchers at DGIST have developed "CTCeptor," a new technology that captures both circulating tumor cells and the "helper" cells (fibroblasts) that support cancer growth. Most current tests try to filter cells based on their size or specific markers, but they often fail to catch cancer cells that look slightly different. CTCeptor uses a unique extraction method that successfully isolates these cells regardless of their size, providing a much more complete picture of the disease. The impact of this technology on patient care could be profound. In recent trials with breast cancer patients, CTCeptor detected 15 times more tumor cells than existing methods. By analyzing both the cancer cells and their environment, doctors can b...
Read more