Numerical simulation method for the assessment of the effect of molar activity on the pharmacokinetics of radioligands in small animals

Abstract

Background

It is well recognized that the molar activity of a radioligand is an important pharmacokinetic parameter, especially in positron emission tomography (PET) of small animals. Occupation of a significant number of binding sites by radioligand molecules results in low radioligand accumulation in a target region (mass effect). Nevertheless, small-animal PET studies have often been performed without consideration of the molar activity or molar dose of radioligands. A simulation study would therefore help to assess the importance of the mass effect in small-animal PET. Here, we introduce a new compartmental model-based numerical method, which runs on commonly used spreadsheet software, to simulate the effect of molar activity or molar dose on the pharmacokinetics of radioligands.

Results

Assuming a two-tissue compartmental model, time-concentration curves of a radioligand were generated using four simulation methods and the well-known Runge–Kutta numerical method. The values were compared with theoretical values obtained under an ultra-high molar activity condition (pseudo-first-order binding kinetics), a steady-state condition and an equilibrium condition (second-order binding kinetics). For all conditions, the simulation method using the simplest calculation yielded values closest to the theoretical values and comparable with those obtained using the Runge–Kutta method. To satisfy a maximum occupancy less than 5%, simulations showed that a molar activity greater than 150 GBq/μmol is required for a model radioligand when 20 MBq is administered to a 250 g rat and when the concentration of binding sites in target regions is greater than 1.25 nM.

Conclusions

The simulation method used in this study is based on a very simple calculation and runs on widely used spreadsheet software. Therefore, simulation of radioligand pharmacokinetics using this method can be performed on a personal computer and help to assess the importance of the mass effect in small-animal PET. This simulation method also enables the generation of a model time-activity curve for the evaluation of kinetic analysis methods.