Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles

Lynn Bi, Javad Sovizi, Kelsey Boitnott Mathieu, Wolfgang Stefan, Sara Thrower, John D Hazle, David Thomas Alfonso Fuentes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
Volume10578
ISBN (Electronic)9781510616455
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 11 2018Feb 13 2018

Other

OtherMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityHouston
Period2/11/182/13/18

Fingerprint

Pharmacokinetics
Iron oxides
inference
iron oxides
Nanoparticles
nanoparticles
Likelihood Functions
Bayes Theorem
Liver
Tissue Distribution
Blood Volume
Early Detection of Cancer
Neoplasms
Spleen
Technology
ferric oxide
compartments
Plasmas
liver
Tumors

Keywords

  • Bayesian inference
  • MULTINEST
  • biodistribution
  • model selection
  • nested sampling
  • parameter estimation
  • physiologically-based pharmacokinetic (PBPK) models
  • superparamagnetic iron oxide nanoparticles (SPIONs)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Bi, L., Sovizi, J., Mathieu, K. B., Stefan, W., Thrower, S., Hazle, J. D., & Fuentes, D. T. A. (2018). Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10578). [105782G] SPIE. https://doi.org/10.1117/12.2293953

Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. / Bi, Lynn; Sovizi, Javad; Mathieu, Kelsey Boitnott; Stefan, Wolfgang; Thrower, Sara; Hazle, John D; Fuentes, David Thomas Alfonso.

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. ed. / Barjor Gimi; Andrzej Krol. Vol. 10578 SPIE, 2018. 105782G.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bi, L, Sovizi, J, Mathieu, KB, Stefan, W, Thrower, S, Hazle, JD & Fuentes, DTA 2018, Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. in B Gimi & A Krol (eds), Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. vol. 10578, 105782G, SPIE, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, Houston, United States, 2/11/18. https://doi.org/10.1117/12.2293953
Bi L, Sovizi J, Mathieu KB, Stefan W, Thrower S, Hazle JD et al. Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. In Gimi B, Krol A, editors, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. Vol. 10578. SPIE. 2018. 105782G https://doi.org/10.1117/12.2293953
Bi, Lynn ; Sovizi, Javad ; Mathieu, Kelsey Boitnott ; Stefan, Wolfgang ; Thrower, Sara ; Hazle, John D ; Fuentes, David Thomas Alfonso. / Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. editor / Barjor Gimi ; Andrzej Krol. Vol. 10578 SPIE, 2018.
@inproceedings{b9de562d226a43db8b9549fd442b8620,
title = "Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles",
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.",
keywords = "Bayesian inference, MULTINEST, biodistribution, model selection, nested sampling, parameter estimation, physiologically-based pharmacokinetic (PBPK) models, superparamagnetic iron oxide nanoparticles (SPIONs)",
author = "Lynn Bi and Javad Sovizi and Mathieu, {Kelsey Boitnott} and Wolfgang Stefan and Sara Thrower and Hazle, {John D} and Fuentes, {David Thomas Alfonso}",
year = "2018",
month = "1",
day = "1",
doi = "10.1117/12.2293953",
language = "English (US)",
volume = "10578",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2018",
publisher = "SPIE",

}

TY - GEN

T1 - Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles

AU - Bi, Lynn

AU - Sovizi, Javad

AU - Mathieu, Kelsey Boitnott

AU - Stefan, Wolfgang

AU - Thrower, Sara

AU - Hazle, John D

AU - Fuentes, David Thomas Alfonso

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - MULTINEST

KW - biodistribution

KW - model selection

KW - nested sampling

KW - parameter estimation

KW - physiologically-based pharmacokinetic (PBPK) models

KW - superparamagnetic iron oxide nanoparticles (SPIONs)

UR - http://www.scopus.com/inward/record.url?scp=85049597200&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049597200&partnerID=8YFLogxK

U2 - 10.1117/12.2293953

DO - 10.1117/12.2293953

M3 - Conference contribution

AN - SCOPUS:85049597200

VL - 10578

BT - Medical Imaging 2018

A2 - Gimi, Barjor

A2 - Krol, Andrzej

PB - SPIE

ER -