Introduction <p>Whole-body dynamic (WB4D) positron emission tomography (PET) imaging data using radiolabeled analogs of drugs are mostly analyzed using descriptive approaches, with no relationship to traditional pharmacokinetic studies based on blood sampling. Here, we build a pharmacokinetic (PK) model from WB4D PET data obtained using a microdose of radiolabeled glyburide ([<sup>11</sup>C]glyburide) in humans, aiming to describe the biodistribution of this drug and compare estimated pharmacokinetic parameters with the parameters obtained in standard PK studies.</p> Methods <p>The present work analyzes data acquired over 40 min after injection of [<sup>11</sup>C]glyburide in 16 healthy subjects using non-linear mixed-effect models (NLMEM). In 10 subjects, a second PET acquisition was performed after rifampicin administration, which may cause a drug–drug interaction and inhibit the liver uptake transport of glyburide. Arterial blood, liver, kidneys, pancreas, and spleen kinetics were modeled using NLMEM. The model-building strategy involved selecting the structural model using baseline [<sup>11</sup>C]glyburide PET data and then selecting the covariate model (rifampicin, age, and gender) and refining the structure of the interindividual variability model using both administration periods. Model selection was based on the corrected Bayesian information criterion and implemented in Monolix software.</p> Results <p>The final model included seven compartments, with two compartments each for the Liver and kidneys to account for within-tissue exchanges. Rifampicin decreased the Liver distribution by 261%.</p> Discussion <p>The estimated central volume of distribution (<i>V</i> = 3.6 L) and elimination rate (<i>k</i> = 0.8 h<sup>-1</sup>) were consistent with the known pharmacokinetics of glyburide, which is a promising first step in leveraging microdose data to study the WB4D biodistribution.</p> Registration <p>EudraCT identifier no. 2017-001703-69</p>

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Modeling Whole-Body Dynamic PET Microdosing Data to Predict the Whole-Body Pharmacokinetics of Glyburide in Humans

  • Léa Comin,
  • Solène Marie,
  • Moreno Ursino,
  • Sarah Zohar,
  • Nicolas Tournier,
  • Emmanuelle Comets

摘要

Introduction

Whole-body dynamic (WB4D) positron emission tomography (PET) imaging data using radiolabeled analogs of drugs are mostly analyzed using descriptive approaches, with no relationship to traditional pharmacokinetic studies based on blood sampling. Here, we build a pharmacokinetic (PK) model from WB4D PET data obtained using a microdose of radiolabeled glyburide ([11C]glyburide) in humans, aiming to describe the biodistribution of this drug and compare estimated pharmacokinetic parameters with the parameters obtained in standard PK studies.

Methods

The present work analyzes data acquired over 40 min after injection of [11C]glyburide in 16 healthy subjects using non-linear mixed-effect models (NLMEM). In 10 subjects, a second PET acquisition was performed after rifampicin administration, which may cause a drug–drug interaction and inhibit the liver uptake transport of glyburide. Arterial blood, liver, kidneys, pancreas, and spleen kinetics were modeled using NLMEM. The model-building strategy involved selecting the structural model using baseline [11C]glyburide PET data and then selecting the covariate model (rifampicin, age, and gender) and refining the structure of the interindividual variability model using both administration periods. Model selection was based on the corrected Bayesian information criterion and implemented in Monolix software.

Results

The final model included seven compartments, with two compartments each for the Liver and kidneys to account for within-tissue exchanges. Rifampicin decreased the Liver distribution by 261%.

Discussion

The estimated central volume of distribution (V = 3.6 L) and elimination rate (k = 0.8 h-1) were consistent with the known pharmacokinetics of glyburide, which is a promising first step in leveraging microdose data to study the WB4D biodistribution.

Registration

EudraCT identifier no. 2017-001703-69