Abstract
Nowadays, there is need to analyze data in such a way as to consider the interrelationships between variables that describe the behavior of such data. The analysis of multivariate data refers precisely to a wide variety of methods of description or inference for the analysis of these data so that the inter-relationships between the variables can be quantified and evaluated. One of the most useful methods is the nonlinear mixed effects modeling. Nonlinear mixed effects models have been implemented in a wide variety of disciplines such as social sciences, physics, and life sciences where complex data structures such as multivariate observations or longitudinal data are present. Implementing a nonlinear mixed effects model is an arduous and complicated task. This is because the estimation of the parameters is performed solving maximum likelihood functions that usually have no analytical solution. In this work, we presented an example of an implementation of nonlinear mixed effect modeling for the development of a population pharmacokinetic model using a genetic algorithm to improve the estimation of the population pharmacokinetic parameters. At the end of this work, we conducted the comparison between a classical estimation method and an estimation method using a genetic algorithm.
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References
Williams, P.J., Ette, E.I.: The role of population pharmacokinetics in drug development in light of the food and drug administration’s ‘guidance for industry: population pharmacokinetics’. Springer, Heidelberg (2000)
Tabachnick, B.G., Fidell, L.S.: Using Multivariate Satistics. Pearson, London (2013)
Pinheiro, J.C., Bates, D.M.: Mixed-Effect Models in S and S-PLUS. Springer, Heidelberg (2000)
van der Graaf, P.: Introduction to population pharmacokinetic/pharmacodynamic analysis with nonlinear mixed effects models. CPT: Pharmacomet. Syst. Pharmacol. 3(1–2), 153 (2014). doi:10.1038/psp.2014.51
Wählby, U., Jonsson, E.N., Karlsson, M.O.: Comparison of stepwise model building strate gies in population pharmacokinetic-pharmacodynamic analysis, October (2002)
Gabrielsson, J., Weiner, D.: Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. Apotekarsocieteten, Stockholm (2006)
Kim, S., Li, L.: A novel global search algorithm for nonlinear mixed-effects models. Springer Science+Business Media, Heidelberg (2011)
Gieschke, R., Serafin, D.: Development of Innovative Drug via Modeling with MATLAB. Springer, Heidelberg (2014)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Ahn, C.W.: Advances in Evolutionary Algorithms. Theory, Design and Practice. Springer, Heidelberg (2006)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Lowen, R., Verschoren, A.: Foundations of Generic Optimization. Springer, Heidelberg (2008)
Acknowledgments
We thank Instituto Politécnico Nacional (IPN), to the Comisión de Fomento y Apoyo Académico del 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, J.M., Sepúlveda, R. (2018). Estimation of Population Pharmacokinetic Model Parameters Using a Genetic Algorithm. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_23
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DOI: https://doi.org/10.1007/978-3-319-67137-6_23
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