02 May 2024

One of the most challenging problems in healthcare is that many patients do not feel that their medications benefit them. This results not only in significant suffering for patients, but also in enormous costs for healthcare. A study by researchers from LiU shows that patients’ digital twins can be analysed using thousands of medications to find the most effective treatment.

An illustration of the digital twin strategy for personalised medicine.
The digital twin strategy for personalised medicine.

Diseases of the human body are very complicated. In one person, thousands of genes can affect billions of cells in different parts of their body. It is very difficult to treat such complex problems with traditional treatment methods. Furthermore, two patients with the same disease may need different medications because they differ at gene and cell levels. A digital twin can help. The use of digital twins is a technology that involves creating a virtual copy of a patient’s biological data and analysing it to tailor medication.
Individually tailored medication for autoimmune diseases has taken a big step forward thanks to the use of advanced data processing. A recently published study in the journal Genome Medicine by researchers from LiU shows that patients’ digital twins can be analysed using thousands of medications to find the most effective treatment.

New innovative approach to precision medicine

Samuel Schaefer, PhD student at the Department of Biomedical and Clinical Sciences (BKV) at Linkoping University and doctor at the Department of Gastrointestinal Medicine at Linköping University Hospital, has conducted research on tailored treatments for autoimmune diseases. Samuel’s research focuses on addressing the common concern in medicine that many patients do not respond well to standard treatments for autoimmune diseases.
“It’s a problem that patients often need to try several treatments before an effective treatment is found. This can lead to long periods of suffering and illness, while patients wait to find the right treatment,” says Samuel.

Currently, there are no methods to find which patient will respond to which drug. This is what Samuel and his colleagues in the research paper tried to make progress on. In collaboration with his supervisor during his doctoral studies, Mikael Benson, who now works at Karolinska Institutet, Samuel has developed a method that uses data to analyse gene expression at individual cell levels. This high-resolution and innovative method, which they named scDrugPrio, provides a detailed picture of the condition of the tissue and enables predictions of which drugs may be effective for each individual patient.

From mouse models to human patients

In a study conducted on a mouse model for rheumatoid arthritis, Samuel and his research colleagues showed that their predictions could identify several existing drugs that were promising for the treatment of the disease. In addition, new drugs that had previously not been linked to rheumatoid arthritis were tested, resulting in positive responses in the mouse model. To validate the effectiveness of the method in humans, further studies were conducted on patients.

“We applied the method to 11 patients with Crohn’s disease, where both healthy and inflamed tissue samples were taken from the same patient. One advantage of this approach is that it allows us to compare the patient’s own tissue samples and identify specific changes that occur in the inflamed intestine. Through this, we were able to conclude that our drug prediction method, scDrugPrio, was relatively successful in predicting patients’ responses to standard treatment. This insight gives us hope that the method can be a step forward for healthcare. However, it should be noted that the study was based on a limited group of 11 patients, which underlines the importance of larger and more extensive studies to validate the results.”

The path to clinical use

The falling costs of high-resolution data may open up the possibility that personalised drug selection represents a more cost-effective approach compared to traditional treatment steps, which may open the way for a more cost-effective approach compared to traditional treatment methods.

“The cost of producing the high-resolution data used by scDrugPrio has fallen significantly in recent years. Where it previously cost about SEK 150,000 per patient, it’s now around SEK 20,000–30,000 per patient. This should be seen in the light of the fact that biological drugs cost SEK 200,000–350,000 per patient and year. Investing SEK 20,000 to assess the benefits of the next SEK 100,000 in drug costs can be a cost-effective step forward.”

Despite this, the challenge remains to ensure the reliability and safety of the method before it can be widely implemented in clinics.

“The timing of when it can reach clinics depends a lot on whether we manage to repeat our results in larger studies. Of course, we want to make sure that it is a safe and reliable method. Right now, I’m planning for more studies, including at the Department of Gastrointestinal Medicine at Linköping University Hospital,” says Samuel.

Samuel’s work and the new research method have also received media attention, with a report from Vetenskapsradion (Swedish science radio) that highlights the importance of digital twins in medical research and the future of healthcare.

Read more


Samuel Schäfer, Martin Smelik, Oleg Sysoev, Yelin Zhao, Desiré Eklund, Sandra Lilja, Mika Gustafsson, Holger Heyn, Antonio Julia, Istvan A. Kovacs, Joseph Loscalzo, Sara Marsal, Huan Zhang, Xinxiu Li, Danuta Gawel, Hui Wang, Mikael Benson (2024) scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases Genome Medicine, Vol. 16, Article 42 Continue to DOI


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