Can Vaccines Designed Using Computational Methods Surpass Animal Testing?

The development of vaccines has traditionally been linked to extensive preclinical trials in animal models. For decades, these studies have been an essential step in assessing the safety and efficacy of new candidates before their application in humans. However, advances in computational methods and artificial intelligence are raising an increasingly relevant question for the scientific community: can vaccines designed using computational models reduce—and in some respects even surpass—animal testing?

Biomedical science is currently undergoing a period of transformation driven by technological advances. Through the large-scale analysis of biological data, molecular modeling, and machine learning algorithms, it is now possible to simulate, with great accuracy, complex interactions between antigens and the human immune system. These capabilities are changing the way the early stages of vaccine design are conceived.

Computational methods make it possible to analyze thousands of genetic sequences, protein structures, and potential immune responses before a candidate ever reaches the laboratory. Through advanced simulations, researchers can predict which antigens are most likely to elicit an effective response, which might generate unwanted effects, and which combinations offer the best results. This early filtering significantly reduces the number of candidates that move on to experimental phases, optimizing time, resources, and scientific effort.

One of the main benefits of this approach is its impact on animal testing. By selecting only the candidates with the highest probability of success, preclinical studies can be carried out using fewer animal models and at more advanced stages of the process, when prior information is more robust. This not only improves scientific efficiency but also addresses growing ethical concerns about reducing the use of animals in research whenever possible.

This is not about completely eliminating animal testing in the short term. In many cases, such trials remain a key tool for validating complex results that cannot yet be fully reproduced through simulations. However, computational design is shifting the balance: animals cease to be the starting point and instead become a confirmation stage—more precise and better substantiated.

In addition, this paradigm shift has regulatory and social implications. International health agencies are beginning to recognize the value of alternative and complementary methods to animal experimentation, provided their scientific validity is demonstrated. In the long term, this evolution could redefine preclinical evaluation standards in biomedicine.

In projects such as Vaccination, developed by the AIR Institute, this approach translates into more responsible and sustainable research. The combination of artificial intelligence, reverse vaccinology, and high-performance computing makes it possible to move toward increasingly reliable predictive models, capable of anticipating biological behavior with a high degree of accuracy. This accelerates vaccine development and strengthens confidence in the results obtained.