Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Autor: Lam NN; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. nicholas.lam@pg.canterbury.ac.nz., Murray R; School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand., Docherty PD; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.; Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Baden-Württemberg, Germany.
Jazyk: angličtina
Zdroj: Bulletin of mathematical biology [Bull Math Biol] 2024 May 08; Vol. 86 (6), pp. 70. Date of Electronic Publication: 2024 May 08.
DOI: 10.1007/s11538-024-01304-1
Abstrakt: Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.
(© 2024. The Author(s).)
Databáze: MEDLINE
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