Abstract
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.
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Acknowledgments
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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Sepúlveda, C., Montiel, O., Cornejo Bravo, J.M., Sepúlveda, R. (2017). Estimation of Population Pharmacokinetic Parameters Using a Genetic Algorithm. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_32
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DOI: https://doi.org/10.1007/978-3-319-47054-2_32
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