Estimation of population pharmacokinetic parameters using a genetic algorithm

Carlos Sepúlveda; Oscar Montiel; José M. Cornejo Bravo; Roberto Sepúlveda

doi:10.1007/978-3-319-47054-2_32

Estimation of population pharmacokinetic parameters using a genetic algorithm

Carlos Sepúlveda, Oscar Montiel, José M. Cornejo Bravo, Roberto Sepúlveda

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	493-503
Number of pages	11
Journal	Studies in Computational Intelligence
Volume	667
DOIs	https://doi.org/10.1007/978-3-319-47054-2_32
State	Published - 1 Jan 2017
Externally published	Yes

Keywords

Genetic algorithm
Mixed effects models
Population pharmacokinetic

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10.1007/978-3-319-47054-2_32

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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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AU - Sepúlveda, Carlos

AU - Montiel, Oscar

AU - Cornejo Bravo, José M.

AU - Sepúlveda, Roberto

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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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KW - Mixed effects models

KW - Population pharmacokinetic

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DO - 10.1007/978-3-319-47054-2_32

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VL - 667

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JO - Studies in Computational Intelligence

JF - Studies in Computational Intelligence

ER -

Estimation of population pharmacokinetic parameters using a genetic algorithm

Abstract

Keywords

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