FarrSight-Twin Digital Twins Accurately Replicate Cancer Clinical Trial Outcomes
Key Insights
FarrSight-Twin technology accurately recreates clinical trials using digital twins of cancer (search) patients, potentially reducing the need for extensive real-world testing.
The digital twin technology utilizes biological data from thousands of cancer (search) patients to predict individual responses to treatment.
Simulated clinical studies using FarrSight-Twin accurately predicted the outcomes of actual clinical trials across breast, pancreatic, and ovarian cancers.
Researchers have demonstrated the ability to accurately replicate clinical trials of novel cancer (search) treatments using 'digital twins' of actual cancer patients. The technology, named FarrSight®-Twin, employs algorithms originally developed in astrophysics for black hole detection and was presented at the 36th EORTC-NCI-AACR Symposium on Molecular Targets and Cancer Therapeutics in Barcelona, Spain.
Virtual Clinical Trials
According to the researchers, this approach could enable cancer (search) researchers to conduct virtual clinical trials before initiating studies in human subjects. The technology could also be used in conjunction with clinical trials, creating a digital twin for each participant to serve as a control group. Ultimately, individual patients could have different treatments tested on their digital twin to help select the most appropriate therapy.
Dr. Uzma Asghar, Co-founder and Chief Scientific Officer at Concr (search), stated, "We can use digital twins to represent individual patients, build clinical trial cohorts and compare treatments to see if they are likely to be successful before testing them out with real patients."
How FarrSight-Twin Works
Each digital twin is constructed using biological data from thousands of cancer (search) patients treated with various methods. This information is integrated to recreate a real patient's cancer, incorporating molecular data from their tumor. This digital twin allows for predicting a patient's likely response to a specific treatment.
Dr. Asghar and her team recreated published clinical trials using a digital twin for each real patient involved. The digital trials accurately predicted the outcomes of the actual clinical trials across all simulated studies. Further analysis revealed that patients receiving the treatment predicted as optimal by FarrSight®-Twin exhibited a 75% response rate, compared to a 53.5% response rate in those receiving a different treatment. The term 'response rate' refers to the proportion of patients whose tumors shrank following treatment.
The trials used in the study involved patients with breast, pancreatic, or ovarian cancer (search). These were phase II or III trials comparing two different drug therapies, including anthracyclines (search), taxanes (search), platinum-based drugs (search), capecitabine, and hormone treatments (search).
Impact on Drug Development
This technology allows researchers to simulate patient trials much earlier in the drug development process and rerun simulations multiple times to explore different scenarios and maximize the likelihood of success. It is currently being used to simulate patients as controls for comparing the effect of a new treatment with the existing standard of care.
Ongoing Research
Dr. Asghar and her colleagues are currently evaluating the technology's ability to predict treatment response for individual patients in the clinic and assist doctors in determining which chemotherapy regimens will be effective. This work is part of an ongoing observational collaborative trial between Concr (search), The Institute of Cancer (search) Research, Durham University, and the Royal Marsden Hospital (search), focusing on patients with triple-negative breast cancer (search).
Professor Timothy A Yap from the University of Texas MD Anderson Cancer (search) Center, emphasized the potential of digital tools to accelerate the development of better cancer treatments. "If we can exploit digital tools to make this process quicker and easier, that should help us find better treatments for patients more efficiently in the future."