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December 18, 2023

PhD Gustavo Hernandez Mejia: impressive list of publications

FIAS doctorate on influenza infections

Gustavo Hernandez Mejia from Esteban Vargas' FIAS research group, supervised by FIAS Fellow Franziska Matthäus, defended his doctoral thesis on 18 December. During his doctorate, he studied affinity mechanisms of broad protection against influenza infections and developed strategies that contribute to the paradigm of personalized medicine.

Infectious diseases continue to be a global threat, such as infections caused by influenza viruses. According to the World Health Organization (WHO), annual influenza epidemics result in one billion infections, leading to up to 650.000 deaths. Vaccination and antiviral treatment provide the best protection framework against infection.

Gustavo Hernandez Mejia wants to understand the still hidden mechanisms that lead to optimal vaccine efficacy and improve antiviral treatment strategies: "During my time at FIAS, I developed mathematical models that help us to understand mechanisms that drive antibody cross-reaction to influenza infections. Also, I devised control-based strategies to adapt influenza treatment to infection progression".

In the last decade, diverse animal model experiments and human immunologic data have reported the antibody cross-reaction phenomena, especially in sequential infections. During his PhD, Gustavo constructed computational models for the affinity maturation process abstracting characteristics of the influenza hemagglutinin (antigen) such as the conserved and variable areas, the antigen-B-cell affinity thresholds in such areas, the presence of key mutations, and the effects of threshold flexibility between infections. 

Gustavo found that improved antibodies cross-reaction occurs when slightly increasing the affinity threshold of the variable area (critical mutation demanded) for the second infection. "These results provide theoretical evidence that affinity also develops dynamically in sequential infections and has a central role in the breadth and magnitude in the antibody response", he indicates. 

On the other hand, Gustavo highlights that, unlike the current vision, future medicine envisages a more personalized scheme where, for instance, the treatment is tailored to the individual disease progression. "I developed a control-base treatment tailoring strategy integrating viral load data from clinical trials, mathematical models of influenza infection at the host level, and the pharmacologic characteristics of the WHO-recommended oseltamivir, a neuraminidase inhibitor", says Gustavo. Following a virtual clinical trial approach, the proposed control-based scheme not only reaches the treatment efficacy of the current recommendations using a fixed-dose strategy (over 90%) but also the strategy reduces the total amount of drug by up to 44%. "The dose can be reduced as the viral load decreases," Gustavo underlines. 

The frameworks developed at FIAS have been reported in an impressive list of publications. They can help provide in silico platforms to understand infection and immune mechanisms, support decisions in the development of new drugs and vaccines, and translate data into insights to improve preparedness and treatment of infectious diseases.

Gustavo currently is a research fellow at the Clinical Epidemiology Unit of the Institute of Epidemiology and Social Medicine at the University of Münster. He is looking forward to applying his research schemes in the pharmacology and medical devices industry.

Selected Publications (for a complete list, visit Google Scholar)

  • Hernandez-Mejia, G., & Hernandez-Vargas, E. A. (2021). Uncovering antibody cross-reaction dynamics in influenza A infections. Bioinformatics, 37(2), 229–235. https://doi.org/10.1093/bioinformatics/btaa691
  • Hernandez-Mejia, G., Alanis, A. Y., Hernandez-Gonzalez, M., Findeisen, R., & Hernandez-Vargas, E. A. (2020). Passivity-based inverse optimal impulsive control for influenza treatment in the host. IEEE Transactions on Control Systems Technology, 28(1), 94–105. https://doi.org/10.1109/tcst.2019.2892351
  • Hernandez-Mejia, G., Alanis, A. Y., & Hernandez-Vargas, E. A. (2018). Neural inverse optimal control for discrete-time impulsive systems. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.06.034
  • Hernandez-Mejia, G., Sánchez, E. N., Chan, V. M., & Hernandez-Vargas, E. A. (2022). Impulsive neural control to schedule antivirals and immunomodulators for COVID-19. Proceedings of the IEEE Conference on Decision & Control, 2022, 5633–5638. https://doi.org/10.1109/cdc51059.2022.9992454
  • Hernandez-Mejia, G., Du, X., Alanis, A. Y., & Hernandez-Vargas, E. A. (2021). Bounded input impulsive control for scheduling therapies. Journal of Process Control, 102, 34–43. https://doi.org/10.1016/j.jprocont.2021.03.003
  • Hernandez-Mejia, G., & Hernandez-Vargas, E. A. (2020). When is SARS-CoV-2 in your shopping list? Mathematical Biosciences, 328(108434), 108434. https://doi.org/10.1016/j.mbs.2020.108434
  • Hernandez-Mejia, G., & Hernandez-Vargas, E. A. (2020). PK/PD-based impulsive control to tailor therapies in infectious diseases. IFAC-PapersOnLine, 53(2), 16055–16060. https://doi.org/10.1016/j.ifacol.2020.12.418
  • Hernandez-Mejia, G., Hernandez-Vargas, E. A., Alanis, A. Y., & Arana-Daniel, N. (2018). Recurrent high-order neural networks identification for infectious diseases. 2018 International Joint Conference on Neural Networks (IJCNN). DOI: 10.1109/IJCNN.2018.8489067
  • Hernandez-Vargas, E. A., Martinez-Picado, J., & Hernandez-Mejia, G. (2018). Long-term impact of antiretroviral strategies for a functional HIV cure: A virtual clinical trial. IFAC-PapersOnLine, 51(27), 80–85. https://doi.org/10.1016/j.ifacol.2018.11.663
  • Hernandez-Mejia, G., Alanis, A. Y., & Hernandez-Vargas, E. A. (2017). Inverse Optimal Impulsive Control Based Treatment of Influenza Infection. IFAC-PapersOnLine, 50(1), 12185–12190. https://doi.org/10.1016/j.ifacol.2017.08.2272

Book Chapters





Gustavo Hernandez Mejia