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

Abstract

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.

Fig. 1

Whole-body PET images of [¹¹C]glyburide distribution in healthy volunteers, without and with pre-infusion of rifampicin. Three subjects were randomly selected, one from each group: subject 14 (group 1), subject 9 (group 2), and subject 3 (group 3).

Fig. 2

Diagram of the method. Each tissue is represented by a colored background and a letter that is used to index the volume of the corresponding compartment: blood (B, red), liver (L, yellow), kidneys (K, purple), pancreas (P, pink), and spleen (S, blue). Each block represents a modeling step. The text in gray represents the elements tested, while the text in bold black represents the elements included in the final model. B blood, L liver, K kidneys, S spleen, P pancreas, V volume, Cc concentration, k exchange rate between tissues, kₑ elimination rate, BW body weight, Rif rifampicin, COSSAC Conditional Sampling Use for Stepwise Approach Based on Correlation Tests, IIV interindividual variability, IOV interoccasion variability

Fig. 3

Diagram of the structural model. Each tissue is represented by a colored background and a letter that is used to index the volume of the corresponding compartment: blood (B, red), liver (L, yellow), kidneys (K, purple), pancreas (P, pink), and spleen (S, blue). Exchanges between tissues are symbolized by arrows, called k_xy. where x denotes the origin and y the destination. For example, k_BL1 denotes the rate constant of transfer from blood to the first liver compartment. The kidneys and liver are divided into two subcompartments with total volumes V_K and V_L, respectively. The rifampicin effect is highlighted by the green star. Finally, k_e denotes the elimination rate. B blood, L liver, K kidneys, S spleen, P pancreas, V volume, k exchange rate between tissues, kₑ elimination rate, I(t) perfused amount, Tk0 estimated duration of the perfusion, $V_{\text{B}}$ estimated blood volume, $V_{\text{L}}$ estimated liver volume, $V_{\text{K}}$ estimated kidney volume, $V_{\text{S}}$ estimated spleen volume, $V_{\text{P}}$ estimated pancreas volume, $k_{\text{L}1\text{B}}$ exchange rate between liver and blood, $k_{\text{BL}1}$ exchange rate between blood and liver, $k_{\text{L}1\text{L}2}$ exchange rate between “liver 1” and “liver 2”, $k_{\text{L}2\text{L}1}$ exchange rate between “liver 2” and “liver 1”, $k_{\text{K}1\text{B}}$ exchange between kidneys and blood, $k_{\text{BK}1}$ exchange between blood and kidneys, $k_{\text{K}1\text{K}2}$ exchange between “kidney 1” and “kidney 2”, $k_{\text{K}2\text{K}1}$ exchange between “kidney 2” and “kidney 1”, $k_{\text{BS}}$ exchange between blood and spleen, $k_{\text{SB}}$ exchange between spleen and blood, $k_{\text{BP}}$ exchange between blood and pancreas, $k_{\text{PB}}$ exchange between pancreas and blood, ${\beta }_{\text{L}1{\text{B}}_{\text{Rif}}}$, rifampicin effect on the liver–blood exchange.

Fig. 4

Individual fits. We used the same color code as shown in Fig. 3. Each row corresponds to three randomly selected subjects (one from each group) to compare the real data (blue points) and the estimation (purple curve). Each column corresponds to a specific tissue on one occasion