The EVONANO platform allows scientists to grow virtual tumors and use artificial intelligence to automatically optimize nanoparticle designs and treat them.
The ability to grow and treat virtual tumors is an important step in developing the next new treatment. cancer.. Importantly, scientists can use virtual tumors to optimize the design of nanoparticle-based drugs before testing them in the laboratory or in patients.
The treatise, “Evolutionary Computation Platform for Automatic Discovery of NanoCarriers” Cancer treatment, ”Was published in Nature Journal today Calculation material. This treatise is the result of the European project EVONANO, involving Dr. Sabine Hauert and Dr. Namid Stillman of the University of Bristol and led by Dr. Igor Balaz of the University of Novi Sad.
“Simulation allows many treatments to be tested very quickly against a wide variety of tumors. Given the complex nature of the disease, the creation of virtual tumors is still in its infancy, but these Even simple digital tumors are expected to help design cancer nanomedicines more efficiently, “says Dr. Hauert.
Dr. Hauert said having software to grow and treat virtual tumors could help develop targeted cancer treatments.
“In the future, creating digital twins of patient tumors will enable the design of new nanoparticle therapies to meet their needs without the need for extensive trial and error or laboratory work. There is a sex. About a solution that is suitable for each individual patient. “
Nanoparticle-based drugs have the potential to improve the targeting of cancer cells.The reason for that Nanoparticles A small vehicle that can be designed to transport the drug to the tumor. Their design alters the ability to move in the body and targets cancer cells correctly. Bioengineers may, for example, change the size, charge, or material of nanoparticles, coat the nanoparticles with molecules that are easily recognized by cancer cells, or load various drugs to kill the cancer cells. there is.
The team was able to use the new EVONANO platform to simulate simple tumors and more complex tumors with cancer stem cells that are difficult to treat and can lead to recurrence in some cancer patients. This strategy has identified nanoparticle design, which has been shown to work in previous studies, and potential new strategies for nanoparticle design.
As Dr. Balaz emphasizes, “The tools developed at EVONANO represent a rich platform for testing hypotheses about the effectiveness of nanoparticles for a variety of tumor scenarios. Fine-tuning nanoparticle parameters. Physiological effects can now be simulated at levels of detail that are nearly impossible to achieve experimentally. “
The challenge in that case is to design the right nanoparticles. Researchers used a machine learning technique called artificial evolution to design nanoparticles until they were able to process all the scenarios tested while maintaining healthy cells and limit potential side effects. Fine-tune.
Dr. Stillman, co-author of the treatise with Dr. Balaz, said: Find new and exciting ways to treat cancer. “
In the future, such platforms will be used to grow virtual versions of tumors using data from individual patients and optimize appropriate treatments to bring digital twins closer to reality. I am aiming. In the short term, the platform will be used to discover new nanoparticle strategies that can be tested in the laboratory. Because the software is open source, it is also expected that other researchers will use it to build their own AI-powered cancer nanomedicines.
“To get closer Clinical practiceFuture work will focus on reproducing tumor heterogeneity and the emergence of drug resistance. We believe these are the most important aspects of why cancer treatments for solid tumors often fail, “Dr. Baraz said.
Namid R. Stillman et al, Evolutionary Computation Platform for Automatic Discovery of NanoCarriers for Cancer Treatment, npj calculation material (2021). DOI: 10.1038 / s41524-021-00614-5
University of Bristol
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Growth and treatment of virtual tumors using AI-designed nanoparticles
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