Metaheuristics for pharmacometrics

Abstract Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solvi...

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Autores principales:	Seongho Kim, Andrew C. Hooker, Yu Shi, Grace Hyun J. Kim, Weng Kee Wong
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/181a5c3d446e45b9be8d279ba200df3d
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spelling	oai:doaj.org-article:181a5c3d446e45b9be8d279ba200df3d2021-11-15T18:41:53ZMetaheuristics for pharmacometrics2163-830610.1002/psp4.12714https://doaj.org/article/181a5c3d446e45b9be8d279ba200df3d2021-11-01T00:00:00Zhttps://doi.org/10.1002/psp4.12714https://doaj.org/toc/2163-8306Abstract Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms.Seongho KimAndrew C. HookerYu ShiGrace Hyun J. KimWeng Kee WongWileyarticleTherapeutics. PharmacologyRM1-950ENCPT: Pharmacometrics & Systems Pharmacology, Vol 10, Iss 11, Pp 1297-1309 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Therapeutics. Pharmacology RM1-950
spellingShingle	Therapeutics. Pharmacology RM1-950 Seongho Kim Andrew C. Hooker Yu Shi Grace Hyun J. Kim Weng Kee Wong Metaheuristics for pharmacometrics
description	Abstract Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms.
format	article
author	Seongho Kim Andrew C. Hooker Yu Shi Grace Hyun J. Kim Weng Kee Wong
author_facet	Seongho Kim Andrew C. Hooker Yu Shi Grace Hyun J. Kim Weng Kee Wong
author_sort	Seongho Kim
title	Metaheuristics for pharmacometrics
title_short	Metaheuristics for pharmacometrics
title_full	Metaheuristics for pharmacometrics
title_fullStr	Metaheuristics for pharmacometrics
title_full_unstemmed	Metaheuristics for pharmacometrics
title_sort	metaheuristics for pharmacometrics
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/181a5c3d446e45b9be8d279ba200df3d
work_keys_str_mv	AT seonghokim metaheuristicsforpharmacometrics AT andrewchooker metaheuristicsforpharmacometrics AT yushi metaheuristicsforpharmacometrics AT gracehyunjkim metaheuristicsforpharmacometrics AT wengkeewong metaheuristicsforpharmacometrics
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Metaheuristics for pharmacometrics

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