How digital twins can enable personalized health treatment | Medical research

How digital twins can enable personalized health treatment |  Medical research

IImagine you had a digital twin who became ill, and could be tested to determine the best possible treatment, without having to go near a pill or a surgeon’s knife. Scientists believe that within five to 10 years, “in silico” trials — where hundreds of virtual organs are used to evaluate the safety and effectiveness of drugs — could become routine, while patient-specific organ models could be used to personalize and avoid treatment. Medical complications.

Digital twins are computational models of physical objects or processes, updated using data from their real-world counterparts. In medicine, this means combining huge amounts of data about the functioning of genes, proteins, cells and entire body systems with patients’ personal data to create virtual models of their organs – and, ultimately, their entire body.

“If you practice medicine today, a lot of it is not very scientific,” says Professor Peter Coveney, director of the Center for Computational Science at University College London and co-author of Virtual You.. “A lot of times, it’s the equivalent of driving a car and deciding where to go next by looking in the rearview mirror: trying to figure out how to treat the patient in front of you based on people you’ve seen in the past who had similar conditions.

“What a digital twin does is use your data within a model that represents how your physiology and pathology works. It’s not making decisions about you based on a demographic that may be completely unrepresentative. It’s truly personal.”

The current state of the art paradigm can be found in cardiology. Already, companies are using patient-specific heart models to help design medical devices, while Barcelona startup ELEM BioTech offers companies the ability to test drugs and devices on simulated models of human hearts.

“We have already conducted a number of virtual human trials on several compounds and are about to enter a new phase, where our product is ready and deployed in the cloud for external access by pharmaceutical customers,” said ELEM co-founder and president. CEO, Chris Morton.

Speaking at the Digital Twins Conference at the Royal Society of Medicine in London on Friday, Dr Caroline Rooney, of Queen Mary University of London, described efforts to develop personalized heart models that would help surgeons plan surgery for patients with irregular heartbeats. And chaotic (atrial). fibrillation).

“Often, surgeons use an approach that works on average, but making patient-specific predictions, and then predicting long-term outcomes, is a real challenge,” Rooney said. “I think there are many applications in cardiovascular disease where we will see this type of approach, such as determining what type of valve to use, or where to insert it during a heart valve replacement.”

Cancer patients are also expected to benefit. Artificial intelligence experts at the pharmaceutical company GSK are working with cancer researchers at King’s College London to build digital replicas of patients’ tumors using images, genetic and molecular data, in addition to growing patients’ cancer cells in 3D and testing their response to them. Drugs.

By applying machine learning to this data, scientists can predict how individual patients will respond to different drugs, drug combinations, and dosing regimens.

Professor Tony Ng, from King’s College, said: “You can’t do this repeatedly with a real patient who has multiple drugs and drug combinations, because every time you try a new treatment, it’s a clinical trial.”

“We are trying to find a solution while the patient is still alive, so if they come back with (cancer) recurrence we will know how to treat them, or which clinical trial to put them on.”

Proof-of-concept trials are expected to begin next year.

Researchers are also working on developing digital twins of pregnancy, which could help in developing drugs for conditions such as placental insufficiency or preeclampsia, and better understanding the physiological processes underpinning pregnancy and labor.

“You can’t do experiments on pregnant women in many cases, and there aren’t good animal models for human pregnancy,” said Professor Michelle Owen, director of the Center for Engineering Women’s Health at Washington University in St. Louis.

Owen builds placental models from ultrasound scans taken during pregnancy and high-resolution postpartum images in women with healthy, complex pregnancies, training an algorithm to recognize different tissues and create a digital copy of them.

“Our goal is to try to figure out things that we can measure in a live person to predict who is likely to have problems with placental function during pregnancy, and to intervene to prevent things like stillbirth,” Owen said.

Her colleague Professor Christine Myers, from Columbia University in New York, is building models of the cervix, uterus and membranes surrounding the fetus. Their long-term goal is to bring them all together into one model for an individual that can predict how pregnancy will occur.

“My hope is that we can do a simplified ultrasound examination of maternal anatomy, and be able to evaluate how that uterus is growing and stretching, and determine the best time for labor to occur,” Myers said. She added that it may predict prolonged or complicated labor, and help women make a more informed decision about whether to have a caesarean section.

Other researchers are building digital twins of hospitals to try to improve the efficiency of moving individual patients through the health care system.

“By tracking the digital signatures that are made every time anything happens with a patient – ​​from ordering, performing and reporting an X-ray, to when a patient is booked and attended an outpatient appointment – ​​we can build a very detailed report,” said Dr. Jacob Koris. Trauma and orthopedic surgeon and digital lead for Get It Right First Time, a national program designed to improve patient treatment and care: “A real-time picture of how patients with similar conditions move through the system.”

“Doing so can identify areas where we need to improve, but also good practices that improve patient care, which we can use to redesign the way we care for patients.”

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