Purpose: Pharmacokinetic modeling can be applied to quantify the kinetics of fluorescently labeled compounds using longitudinal micro-computed tomography and fluorescence-mediated tomography (μCT-FMT). However, fluorescence blurring from neighboring organs or tissues and the vasculature within tissues impede the accuracy in the estimation of kinetic parameters. Contributions of elimination and retention activities of fluorescent probes inside the kidneys and liver can be hard to distinguish by a kinetic model. This study proposes a deconvolution approach using a mixing matrix to model fluorescence contributions to improve whole-body pharmacokinetic modeling.
Procedures: In the kinetic model, a mixing matrix was applied to unmix the fluorescence blurring from neighboring tissues and blood vessels and unmix the fluorescence contributions of elimination and retention in the kidney and liver compartments. Accordingly, the kinetic parameters of the hepatobiliary and renal elimination routes and five major retention sites (the kidneys, liver, bone, spleen, and lung) were investigated in simulations and in an in vivo study. In the latter, the pharmacokinetics of four fluorescently labeled compounds (indocyanine green (ICG), HITC-iodide-microbubbles (MB), Cy7-nanogels (NG), and OsteoSense 750 EX (OS)) were evaluated in BALB/c nude mice.
Results: In the simulations, the corrected modeling resulted in lower relative errors and stronger linear relationships (slopes close to 1) between the estimated and simulated parameters, compared to the uncorrected modeling. For the in vivo study, MB and NG showed significantly higher hepatic retention rates (P<0.05 and P<0.05, respectively), while OS had smaller renal and hepatic retention rates (P<0.01 and P<0.01, respectively). Additionally, the bone retention rate of OS was significantly higher (P<0.01).
Conclusions: The mixing matrix correction improves pharmacokinetic modeling and thus enables a more accurate assessment of the biodistribution of fluorescently labeled pharmaceuticals by μCT-FMT.
Keywords: Computed tomography; Elimination routes; Fluorescence-mediated tomography; Intensity diffusion; Mixing matrix; Pharmacokinetic modeling; Relative blood volume; Retention sites.
© 2021. The Author(s).