The future of cancer treatment is even brighter. Researchers created digital twins of patients and used them as virtual guinea pigs to try different drugs and predict which would be most effective against a patient’s type of cancer.
Imagine your doctor tells you that you have cancer and need immediate treatment. You will be presented with two options and asked to select one. Obviously, you want the most effective treatment to fight your cancer based on your body structure. So how do you choose between the two treatments offered?
Researchers have made that decision much easier. By creating digital twins of cancer patients, we can use them as virtual guinea pigs to replicate clinical trials, compare the effectiveness of treatments, and predict how patients will respond.
“Across the world, we spend billions of dollars developing new cancer treatments,” says the consultant medical oncologist, currently working at the Royal Marsden NHS Foundation Trust in London, and co-founder of Concr. said Dr. Uzma Asghar, Founder and Chief Scientific Officer. A biotechnology company focused on personalized cancer treatments. “Some will be successful, but most will not. Digital twins can be used to represent individual patients, build clinical trial cohorts, and ensure that treatments are successful before being tested on real patients. You can compare whether it is possible.
Digital twins are not new. NASA claims the concept originated in the 1960s, when it created multiple simulators to evaluate the Apollo 13 oxygen tank explosion and subsequent engine damage. But now, thanks to advances in AI, next-generation mobile communications, and big data, this technology is rapidly gaining traction and threatens to disrupt many industries, including healthcare.
The researchers named their technology FarrSight-Twin. It is based on advanced algorithms commonly used by astrophysicists and applied to large amounts of molecular and patient data. This enables the integration of disparate oncology datasets into a single comprehensive model of patient response.
Simply put, each digital twin is created from biological data collected from thousands of cancer patients who have undergone different types of treatments. Combining all the data, we recreate a real patient’s cancer twin using molecular data taken from the tumor. The twins can then receive treatments derived directly from published clinical trials.
Researchers believe that a virtual clinical trial in a digital twin could be compared to an actual Phase II or III clinical trial comparing two different drug treatments in patients with breast, pancreatic, or ovarian cancer. We found that it accurately predicted outcomes. Patients who received the treatment that FarrSight-Twin predicted was best for them had a 75% response rate, while patients who received a different treatment had a 53.5% response rate. Response rate is the percentage of patients whose cancer shrinks or disappears after treatment.
“We are excited to be able to apply this type of technology by simulating clinical trials across different tumor types to predict patient response to different chemotherapy treatments. The results are encouraging,” said Asghar. said. “This technology means researchers can simulate patient trials very early in drug development, and it means the simulations can be re-run multiple times to test different scenarios and maximize the chances of success. It has already been used to simulate patients as a control to compare the effectiveness of new treatments with existing standard treatments.
Researchers are currently using Farr-Sight Twin to see if the technology can help predict the most effective available treatments for patients with triple-negative breast cancer. Triple-negative breast cancer is a more aggressive type of tumor that grows faster and has a higher risk of metastasis. This is a collaboration between researchers from Concr, the Institute of Cancer Research (ICR) in London, Durham University and the Royal Marsden Hospital, also in the UK.
They presented their research findings on using digital twin technology to predict patient responsiveness to cancer treatments at the 36th European Organization for Research and Treatment of Cancer, National Cancer Institute, American Institute for Cancer Research. Presented at the Association (EORTC-NCI-AACR/ENA) symposium. Late October 2024 in Barcelona, Spain.
Source: EORTC-NCI-AACR (ENA)
