Researchers have shown that clinical trials of new treatments can be precisely replicated using “digital twins” of real cancer patients. The technology, called FarrSight®-Twin, is based on an algorithm used by astrophysicists to discover black holes and will be presented today (Friday) at the 36th Annual Conference on Molecular Targets and Cancer Treatments in Barcelona, Spain. It will be presented at the EORTC-NCI-AACR Symposium.
Researchers say cancer researchers could use this approach to conduct virtual clinical trials before testing new treatments on patients. Digital twins can also be used in parallel with clinical trials for each participating patient, and together the digital twins can form a control group for any trial. Ultimately, this may mean allowing patients to test different treatments with digital twins and choose the best treatment ahead of time.
The study was presented by Dr Uzma Asghar, co-founder and chief scientific officer of Concr, and consultant medical oncologist currently working at the Royal Marsden NHS Foundation Trust in London, UK. “Around the world, we spend billions of dollars developing new cancer treatments. Some will succeed, but most will not.”
“Digital twins can be used to represent individual patients, build clinical trial cohorts, and compare whether treatments are likely to be successful before being tested on real patients.”
Each digital twin is created from the biological data of thousands of cancer patients treated in different ways. This information is combined to recreate the tumor’s molecular data and the actual patient’s cancer. This digital twin makes it possible to predict how patients will respond to treatment.
Dr. Asghar and colleagues used this approach to recreate published clinical trials using a digital twin representing each actual patient who participated in the trial. Overall, digital clinical trials accurately predicted real clinical trial outcomes in all simulated clinical trials. Upon further testing, patients who received FarrSight®-Twin’s best predicted treatment had a 75% response rate, compared to a 53.5% response rate for patients who received alternative treatments. “Response rate” refers to the percentage of patients whose tumors shrink after treatment.
The test they used in the study presented at the symposium involved patients with either breast, pancreatic, or ovarian cancer. These were phase II or phase III trials comparing two different drug treatments, including anthracyclines, taxanes, platinum-based drugs, capecitabine, and hormone therapy.
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, and we are seeing promising results. I did.
This technology means researchers can simulate patient trials very early in drug development, and the simulations can be re-run multiple times to test different scenarios and maximize the chances of success. Masu. This is already being used to simulate patients to serve as controls to compare the effects of new treatments with existing standard treatments.
We are currently developing this technology to help predict individual patient treatment responses in the clinic and help doctors understand which chemotherapy treatments are helpful and which are not, and this research It’s in progress. ”
Dr. Uzma Asghar, Co-Founder and Chief Scientific Officer of Concr
In an observational collaborative trial between Concr, Durham University Cancer Research Institute, and research institutions, Dr. Asghar and his colleagues conducted research to predict which available treatments will be most effective for patients with triple-negative breast cancer. We are testing this technique to see if it helps. Royal Marsden Hospital.
Professor Timothy A Yap of the University of Texas MD Anderson Cancer Center in Houston, USA, is co-chair of the EORTC-NCI-AACR symposium but was not involved in the study. “Despite significant improvements in cancer treatment, there are still many types of cancer for which treatment options are limited. Designing and testing new cancer treatments is difficult, time-consuming, and time-consuming. “It’s expensive. We can speed up this process with digital tools.” It should help us find better treatments for patients more easily and more efficiently in the future. ”
sauce:
European Agency for Research and Treatment of Cancer
