Estimation of Population Pharmacokinetic Parameters Using a Genetic Algorithm

  • Carlos SepúlvedaEmail author
  • Oscar Montiel
  • José. M. Cornejo Bravo
  • Roberto Sepúlveda
Part of the Studies in Computational Intelligence book series (SCI, volume 667)


Population pharmacokinetics (PopPK) models are used to characterize the behavior of a drug in a particular population. Construction of PopPK models requires the estimation of optimal PopPK parameters, which is a challenging task due to the characteristics of the PopPK database. Several estimation algorithms have been proposed for estimating PopPK parameters; however, the majority of these methods are based on maximum likelihood estimation methods that optimize the probability of observing data, given a model that requires the systematic computation of the first and second derivate of a multivariate likelihood function. This work presents a genetic algorithm for obtaining optimal PopPK parameters by directly optimizing the multivariate likelihood function avoiding the computation of the first and second derivate of the likelihood function.


Population pharmacokinetic Mixed effects models Genetic algorithm 



We thank to Instituto Politécnico Nacional (IPN), to the Commission of Operation and Promotion of Academic Activities of IPN (COFAA), and the Mexican National Council of Science and Technology (CONACYT) for supporting our research activities.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Carlos Sepúlveda
    • 1
    Email author
  • Oscar Montiel
    • 1
  • José. M. Cornejo Bravo
    • 2
  • Roberto Sepúlveda
    • 1
  1. 1.Instituto Politécnico NacionalCentro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN)TijuanaMéxico
  2. 2.Facultad de Ciencias Químicas e IngenieríaUniversidad Autónoma de Baja California (UABC)TijuanaMéxico

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