Human Immunity: How Genetic Variability Drives Computational Design of More Precise Vaccines

Why do some people develop a strong immune response to a disease while others barely develop any protection? Why can the same vaccine be highly effective in some individuals and less so in others? The answer lies, to a large extent, in the genetic variability of the human immune system.
Understanding these differences is one of the great challenges of modern immunology. Thanks to computational design and artificial intelligence, vaccine research has tools capable of integrating this biological complexity to move toward more precise, effective, and personalized solutions.

An Immune System as Diverse as Humanity

The human immune system is not uniform. Key genes, such as those of the major histocompatibility complex (HLA), exhibit enormous diversity among individuals and populations. This variability determines how the body recognizes pathogens, activates its defenses, and develops immunological memory.

Therefore, the same infection can trigger very different responses among individuals, and not all vaccine antigens will be equally effective across the entire population, with varying intensity and duration of protection.

For decades, vaccine design has had to simplify this reality, relying on "average" formulations that work well in most cases. However, today the challenge is how to incorporate this into the vaccine research and development process without unduly increasing the time, cost, and complexity of trials.

This is where traditional methods run into difficulties. Experimentally analyzing all possible immunological variations is practically impossible. For this reason, this diversity has long been treated as a secondary variable.
Thanks to artificial intelligence, deep learning, and computational modeling, it is possible to simulate how different genetic profiles of the immune system may respond to the same vaccine candidate. This allows for the analysis of large volumes of genomic and immunological data, and from this, the prediction of the affinity between antigens and different HLA molecule variants.

Instead of evaluating efficacy once a vaccine has been developed, computational design allows us to anticipate immune response before it even reaches the laboratory. AI doesn't replace experimental research, but rather enhances it. By reducing initial uncertainty, it optimizes resources, accelerates development, and improves scientific decision-making.

In projects like Vaccination, driven by the AIR Institute, these technologies are applied to build models that connect genetics, immunology, and computing, laying the foundation for a smarter and more adaptive vaccinology.