Russian Scientists Develop a 'Virtual Immune System'
Researchers at Sechenov University, the Russian Academy of Sciences' Institute of Numerical Mathematics, and Simurg Pharm have developed a mathematical model that describes the behavior of the B-cell arm of the immune system. The findings have been published in Frontiers in Immunology.
Before testing a new drug in thousands of volunteers, scientists can first evaluate it using an accurate digital representation of the human immune system. Although that may sound like science fiction, researchers at Sechenov University, the Institute of Numerical Mathematics of the Russian Academy of Sciences, and Simurg Pharm have now demonstrated such an approach. The team has introduced a mathematical model of the B-cell immune response. Rather than being a purely theoretical construct, the model provides a practical framework that could reshape the development of therapies for autoimmune diseases.
The Immune System in Silico: Why Build a 'Virtual' Patient?
B cells are responsible for producing antibodies and play a central role in adaptive immunity. When their function becomes dysregulated, however, they can attack the body's own tissues, contributing to disorders such as rheumatoid arthritis, multiple sclerosis, and type 1 diabetes. The new model captures the entire life cycle of B cells, from their activation in lymph nodes to their migration into bone marrow and distribution across tissues.
One of the major challenges in drug development is that a treatment can prove highly effective for one patient while providing little benefit to another. The digital model enables researchers to conduct virtual experiments that help explain this variability. It could allow scientists to predict how candidate drugs are likely to behave, identify more precise therapeutic targets, and optimize dosing strategies before expensive clinical trials begin. Ultimately, such an approach could support the development of safer and more personalized medicines.
From Fragmented Data to an Integrated Platform
An assessment conducted by Sechenov University in 2024 found that mathematical models existed for only 13 of the roughly 180 known autoimmune diseases. The new model addresses a critical gap by providing a quantitative framework for B-cell biology.
The work is also part of a broader research ecosystem that is beginning to take shape. In 2026, Sechenov University and the Institute of Numerical Mathematics carried out a quantitative analysis of targeted therapies for systemic sclerosis by integrating data from thousands of patients. In parallel, researchers are building databases to support treatment selection for lupus and developing models of immune system ageing. Together, these efforts are laying the foundation for an integrated quantitative pharmacology platform comparable to those being developed at Sirius University.
Building Biotechnology Independence
For Russia's information technology and biopharmaceutical sectors, these developments also address the broader goal of technological sovereignty. Expanding quantitative systems pharmacology capabilities reduces dependence on foreign technologies while lowering the cost of developing innovative domestic medicines. Virtual preclinical studies could shorten the time required to bring new drugs to market while reducing financial risks for pharmaceutical companies.
The broader scientific potential is considerable. Variability in patient responses to therapy remains a global challenge across biomedical research. If the Russian model is successfully validated using clinical data, it could become an internationally relevant tool and contribute to the global toolkit used to design next-generation therapies.
Researchers now face three principal tasks: validating the model with real-world clinical data, extending it to additional diseases, and integrating it more deeply with medical databases. If these objectives are achieved, Russia could strengthen its position as one of the leading centers for digital medicine, where treatment decisions are guided not by trial and error but by quantitative mathematical modelling.
