Digital Twins Help Determine Proper Medication
Jeffrey Heimgartner posted on August 12, 2019 |

While digital twins are frequently talked about when it comes to industrial engineering—or the Internet of Things (IoT)—it isn’t the only industry to benefit from them. A research team at Linköping University in Sweden, led by Mikael Benson, recently published its method for using a patient’s digital twin to determine the best medication before it is prescribed.

The team’s research originated from the fact that medication for common diseases can be ineffective for 40 to 70 percent of patients. Since a disease isn’t just one single mutated cell but rather is dependent on altered interactions among many cells, as well as the fact that each individual has their own unique genetic makeup, receiving the same diagnosis as another patient doesn’t always result in the same treatment plan.

To improve the effectiveness of medications for more patients, the team constructed computational disease models of the altered gene interactions in numerous cells.

“Our aim is to develop those models into ‘digital twins’ of individual patients’ diseases in order to tailor medication to each patient,” Benson said. “Ideally, each twin will be computationally matched with and treated with thousands of drugs before actually selecting the best drug to treat the patient.”

Researchers analyze T cells from patients with 13 diseases to develop models that can be used as digital twins to help determine the best medication for a specific patient. (Image courtesy of Magnus Johansson.)
Researchers analyze T cells from patients with 13 diseases to develop models that can be used as digital twins to help determine the best medication for a specific patient. (Image courtesy of Magnus Johansson.)

The beginnings of the digital twin started with a model of a mouse with human rheumatoid arthritis. The team used single-cell RNA sequencing—a method for examining individual cell sequencing that can reveal mutations, gene relationships and track cell lineage—to track the gene activity in each cell of the mouse’s sick joints to analyze the complex network.

“Networks can be used to describe and analyze most complex systems,” Benson explained. “A simple example is a soccer team, in which the players are connected into a network based on their passes. The player that exchanges passes with most other players may be most important.”

The same methods were used to identify cell types in order to create the digital twin and computationally match it with potential drugs to determine the optimal one for treatment. In their findings, the researchers were able to use the models as a way to diagnose 13 diseases by focusing on that same disease cell type, T cells.

“Since T cells function as a sort of spy satellite, which is continuously surveying the body to discover and combat disease as early as possible, it may be possible to use this cell type for the early diagnosis of many different diseases,” Benson said.

Interested in learning more about technological innovations in health care? Check out Wireless Movement-Tracking System Collects Health and Behavioral Data and Smart Speakers Could Save Thousands of Cardiac Arrest Victims Each Year.


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