The researchers say a complete digital model of the immune system will accelerate precision medicine personalized immune system treatment research
Healthcare spending for immune system disorders and the expensive medications they require make the immune system a prime target for developing precision medicine programs. While hospital and health system leaders and immunologists may be all too familiar with the problem, they may be less familiar with the ambitious precision medicine technology developing to improve treatments and outcomes.
A cutting-edge approach called the “digital twin,” that has been used in manufacturing and other industries, is relatively new to healthcare. As digital twinning has already become a useful tool, some researchers believe that a digital twin of the human immune system could enable risky experiments toward better, more predictable outcomes. To this end, an international group of medical researchers has proposed a framework for developing an immune digital twin. The related paper, published in Nature Digital Medicine in May, unveiled the framework for the immune digital twin computer model.
Co-authored by 10 researchers from six universities around the world, the perspective paper describes a “roadmap” and four-stage process. According to the researchers, this process would result in a high-quality digital twin of the human immune system that would then evolve continuously by collecting and using individual patient data.
Analyzing immune system responses is a growing field of medicine. “Spurred on by the SARS-CoV-2 pandemic, several community efforts emerged to create large-scale computational models of within-host disease dynamics …. Much can be learned from these experiences,” the authors wrote, also proposing the establishment of a Consortium for Predictive Immunology that should work to integrate the “currently fragmented transdisciplinary researchers” to make immune digital twins (IDTs) a reality.
Digital Twin Defined
“A digital twin is a real-time digital model of an object or process that incorporates all available data and updates as new data becomes available,” stated Arash Ghazanfari for TechTarget. “Researchers use digital twins to safely and economically test scenarios before trying them in real-life situations or environments.”
The researchers highlighted cardiovascular diagnostics and insulin pump control as “major successes” in already deployed digital twins. These earlier successes suggest potential for immune system disorders and other diseases, according the scientists.
Who Builds Digital Twins and Why?
Developing a digital twin of the immune system will be a complex, demanding project. “This is an effort that will require the collaboration of computational biologists, immunologists, clinicians, mathematicians and computer scientists,” explained Tomas Helikar, PhD, co-author of the recent paper and associate professor in biochemistry at the University of Nebraska-Lincoln, in a university statement. “Trying to break down this complexity into measurable and achievable steps has been a challenge. This paper is addressing that.”
While a digital twin would initially focus on how the immune system functions in general populations, it would allow for personalized experimentation. The ultimate goal of developing a digital twin would be for precision medicine research for different types of patients, Helikar said.
“The dream and goal are for it to be used for precision medicine at the level of an individual,” he explained. “Importantly, we change over time. Our immune system is programmed, reprogrammed and tweaked over time. It develops from birth and as we get older, it continues developing, often in ways we don’t like. It becomes weaker, we have cancers and our immune system is not keeping up. Our goal is to create digital twins that are not just specific to ourselves, but specific to that point in time—taking into consideration all of our past.”
An immune system digital twin supporting precision medicine is still rather far from prime time; however, existing research is sufficient to begin developing an initial prototype, according to the researchers.
“I think we have enough data and technological advancements in terms of methods and software tools, that the first draft or first version of the virtual immune system could be built with data that already exists,” Helikar said. “It may not be personalizable yet, but you could start with it as a working prototype.”
Moving forward, Helikar’s Digital Twin Innovation Lab will benefit from a five-year, $5,039,652 grant through the University of Nebraska-Lincoln’s Grand Challenges Catalyst Competition, the university announced in August. The laboratory will continue building the infrastructure for a digital twin of the human immune system.
Digital Twins: Systems Approach or Gene Activity Approach
Developing a digital twin of the immune system would be a massive undertaking when compared to existing digital twin developments, such as research out of Sweden that focused on the individual proteins involved in hay fever.
Published in Genome Medicine, also in May, researchers from Linköping University and Karolinska Institutet developed a digital twin of gene activity and interactions between cells. The related article recounts how researchers focused specifically on individuals with seasonal allergic rhinitis, or hay fever, comparing their genes with individuals without hay fever. This enabled them to construct a digital twin that they could then use to predict how certain drugs would impact hay fever.
This Swedish digital twin experiment enabled researchers to pinpoint new drug targets for specific patients, creating the potential to personalize hay fever treatments for specific patients. Using a digital twin instead of experimenting directly on the patients themselves allowed for a much wider variety of treatments to be tested, and it reduced the complexity and risks.
While this experiment provided actionable insights, it was focused on a very specific component of the immune system with exclusive attention to a single condition, an allergy. The newly proposed digital twin of the entire immune system would be magnitudes greater in complexity but could have greater potential for the future of medicine.
Earlier Interventions for Autoimmune Disorders and Diseases
“The earlier an autoimmune disease manifests, the longer an individual must live with accruing damage, increased risks of complications and other conditions, and adverse effects on quality of life,” stated a 2022 National Academies of Sciences, Engineering, and Medicine committee report. “Many autoimmune diseases affect women predominantly, and the incidence and prevalence of these diseases appears to be rising in certain groups, such as children and adolescents in the case of two of the most common autoimmune diseases, namely inflammatory bowel disease and type 1 diabetes.”
While digital twin technology is still in its infancy, healthcare leaders will want to be aware of how this new method of experimentation is set to affect precision medicine research. It is also important to recognize how ambitiously digital twin initiatives are being approached and targeted, with the potential that major body systems could be modeled digitally. Funding for digital twin research includes the National Institutes of Health and the U.S. Department of the Interior.
Much could be learned from programming digital twins in parallel with clinical trials of new drugs, new combinations of drugs, and repurposed drugs. If digital twinning can then simulate and predict response successfully, whether it is in immunology or another medical specialty, the long-term implications for medicine in general and personalized medicine, specifically, will be profound.
—Liz Carey, Caleb Williams
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Enhancing NIH Research on Autoimmune Disease