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Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles
Author(s): Lynn Bi; Javad Sovizi; Kelsey Mathieu; Wolfgang Stefan; Sara Thrower; John Hazle ; David Fuentes
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Paper Abstract

The growing use of superparamagnetic iron oxide nanoparticles (SPIONs) in early cancer detection technologies has created a demand for physiologically-based pharmacokinetic (PBPK) models that accurately model and predict the biodistribution of SPIONs in the mouse and human model. The objective of this work is to use a Bayesian approach built upon nested-sampling to select a model based on qualitative criteria of the fit of the model and the likelihood function landscape, as well as quantitative criteria of the evidence and maximum likelihood values. Four first-order PBPK compartmental models of ranging complexity are considered. Compartments included in the models comprise of a combination of the plasma, liver, spleen, tumor, and “other” (the remaining body tissue), with parameters including the volume, blood flow rate, and plasma:tissue distribution ratios. The model parameters for each model are evaluated using Bayesian inference, in addition to the respective evidence integrals, maximum log-likelihoods, and Bayes factors. The model containing all compartments and the model containing the plasma, liver, tumor and “other” had the highest log-likelihood and evidence values, indicating both a high goodness-of-fit and a high likelihood of the model given the data. This is similarly reflected in a faithful quality-of-fit and non-flat log-likelihood landscapes. Overall, these findings illustrate the strength of the Bayesian model selection framework in ranking different models to determine the best model that accurately represents the experimental data.

Paper Details

Date Published: 12 March 2018
PDF: 6 pages
Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105782G (12 March 2018); doi: 10.1117/12.2293953
Show Author Affiliations
Lynn Bi, Columbia Univ. (United States)
Javad Sovizi, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Kelsey Mathieu, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Wolfgang Stefan, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Sara Thrower, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
John Hazle , Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
David Fuentes, Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 10578:
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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