Newborns, with their developing immune systems, face high risks of infection in the critical early months of life. While maternal vaccines can bolster infant immunity by delivering antibodies through the placenta, these vaccines and their administration schedule are not optimized, and they don't always provide uniform protection for the whole population. Researchers from the University of Virginia's Department of Biomedical Engineering have created an advanced computational model that includes the key factors modulating antibody transfer from mother to fetus. In their recent review paper published in Nature Immunology, they describe how a mathematical modeling framework opens doors for safer, more effective maternal vaccines for vulnerable populations and has broad implications for newborn immunity.

Led by assistant professor of Biomedical Engineering Sepideh Dolatshahi and Ph.D. student Remziye Wessel, the UVA team's research sheds light on the mechanisms driving selective antibody transfer across the placenta, particularly focusing on the IgG antibody that provides immunity to newborns. "Our model captures the kinetics and dynamic complexities of placental antibody transfer in ways that previous research simply couldn't," Dolatshahi said.

Dolatshahi also said that a mechanistic modeling approach paves the way for the rational design of maternal vaccines and personalized therapies by providing a framework for quantitatively assessing the relative impact of regulatory factors, as well as individual and population-level vulnerabilities, to more effectively protect all infants, including those born preterm, from serious infections.

This is a significant departure from traditional empirical vaccine design, which relied largely on static models or animal studies with limited applicability to human physiology. Instead, this model can integrate the dynamic changes in placental structure and maternal immune response over time, making it possible to identify kinetic bottlenecks that limit antibodies from reaching the fetus. By understanding these bottlenecks, researchers can develop maternal vaccines that are more effective at delivering the exact antibodies newborns need for immune defense.

Potential Impact for Newborns and Broader Patient Populations

The implications of vaccine design from a modeling lens extend beyond newborns to other high-risk populations, including immunocompromised patients and elderly adults, who may benefit from similar models tailored to their unique needs.

For infants, the impact of optimized maternal vaccines could be life-saving, especially for those born prematurely, who often lack the full maternal antibody protection provided during a full term pregnancy. Additionally, the model could enable the design of vaccines targeting specific infectious diseases that pose significant risks to newborns, potentially reducing neonatal mortality worldwide.

"Think of the placenta as a selective filter," Dolatshahi describes. "Our model helps reveal which antibodies can pass through effectively and when." Armed with this knowledge, the team's ultimate goal is to develop vaccines that optimize this natural transfer process, strengthening immunity for vulnerable newborns during their first critical months.

Beyond infancy, this modeling approach could eventually inform vaccine strategies for populations with varying immune capabilities, such as individuals with chronic diseases or those undergoing treatments that suppress the immune system. The framework may also offer insights into how antibody transfer efficiencies vary by patient characteristics, such as age, exposure to stressors, or genetic background, paving the way for a more personalized approach to immunization.

"The computational model is currently a research tool, requiring further validation before direct clinical application," Wessel said. "With additional refinement and validation, it could potentially be tested in the clinic to guide patient-specific vaccine strategies in the near future."