Modeling & Data Analysis

Systems Medicine

of Infectious Diseases

Research Group

PK/PD Modeling & Simulation (M&S)

Overview: Drug development has been performed by integrating clinical observations and information from medical tests. However, this “trial and error” approach is becoming more challenging and unfeasible by the steep increase in the amount of different pieces of information and the complexity of large datasets. A systematic and tractable approach that integrates a variety of biological and medical research data into mathematical models and computational algorithms is crucial to harness knowledge to understand why some patients respond differently compare to others.


State of the art: At present, several pharmaceutical companies aim to integrate experimental and clinical knowledge on the drug candidate into mathematical modeling to facilitate quantitative decision making in order to influence the discovery and development of drugs.

Our research in Quantitative Pharmacology aims to:



  • implement PK/PD modeling to design more efficient clinical trials


  • schedule drugs with better benefit/risk profiles


  • influence the discovery and development of drugs


Relevant Publications in this Field:


  • G. Montaseri, A. Boianelli, E.A. Hernandez-Vargas, M. Meyer-Hermann. PK/PD-based adaptive tailoring of oseltamivir doses to treat within-host influenza viral infections. Progress in Biophysics and Molecular Biology. 2018 - [Link]


  • G. Hernandez-Mejia, A.Y. Alanis, E.A. Hernandez-Vargas. Neural inverse optimal control for discrete-time impulsive systems. Neurocomputing. 2018 [Link]


  • G. Hernandez-Mejia, A.Y. Alanis, and E.A. Hernandez-Vargas. Inverse optimal impulsive control based treatment of influenza Infection. Proceedings of the 20th IFAC World Congress, Toulouse, France. 2017 [PDF]
  • A. Boianelli, N. Sharma-Chawla, D. Bruder, and E.A. Hernandez-Vargas. Oseltamivir PK/PD Modeling and Simulation to Evaluate Treatment Strategies against Influenza-Pneumococcus Coinfection. Frontiers in Cellular and Infection Microbioly. 6(60):1-11, 2016. [PDF]


  • E.A. Hernandez-Vargas, P. Colaneri, and R.H. Middleton. Switching Strategies to Mitigate HIV Mutation. IEEE Transactions on Control Systems Technology, 2014. [PDF]


  • M. Haering, A. Hördt, M. Meyer-Hermann and E. A. Hernandez-Vargas. Computational study to determine when to initiate and alternate therapy in HIV infection. BioMed Research International, 2014 [PDF]
  • E.A. Hernandez-Vargas, P. Colaneri, and R. Middleton. Optimal therapy scheduling for a simplified HIV infection model. Automatica, Vol.49 (9), 2874–2880, 2013. [PDF]
  • Ferreira, E.A. Hernandez-Vargas, and R. Middleton. Computer Simulation of Structured Treatment Interruption for HIV infection. Computers Methods and Programs in Biomedicine, Vol. 104 (2), 50-61 2011. [PDF]