Objective: Adenosine A 2A receptor antagonists are potential new treatments for Parkinson disease. We used positron emission tomography (PET) of the A 2A receptor radiotracer, [ 11 C]SCH442416, to assess binding of the novel A 2A antagonist, vipadenant (previously known as BIIB014), to human brain A 2A receptors and to investigate the relationship among dose, steady-state plasma levels, and receptor occupancy. Methods: We used PET to compare ["C]SCH442416 uptake before and after blockade with daily oral vipadenant (2.5―100 mg/d for 10 or 11 days) in healthy volunteers (n = 15). We estimated receptor occupancy in brain regions of interest, particularly the putamen, by kinetic modeling of PET data. We estimated the dose, minimal plasma concentration at steady state (C min ), and area under the plasma concentration curve (AUC 0-τ ) at the steady state required for saturation (≥90% receptor occupancy) using Bayesian Emax and logistic regression models. Results: The estimated receptor occupancy of vipadenant in the brain regions of interest varied from 74% to 94% at the lowest daily dose (2.5 mg) and reached saturation in all regions at 100 mg. In the putamen, the estimated minimal daily dose, steady-state C min , and steady-state AUC 0-τ required for receptor saturation were 10.2 mg (interquartile range, 28%), 0.097 μg/mL (27%), and 6 μg h/mL (21%), respectively. Conclusions: This study provides the first evidence that vipadenant occupies A 2A receptors in the human brain. Receptor occupancy was related to both dose and plasma levels of vipadenant. These results, coupled with previous efficacy results in animals, justify continued development of vipadenant as a potential treatment for Parkinson disease.

An open-label, positron emission tomography study to assess adenosine A2A brain receptor occupancy of vipadenant (BIIB014) at steady-state levels in healthy male volunteers.

https://www.pure.ed.ac.uk/ws/files/87848370/1_s2.0_S1570023218317197_main.pdf

Analysis of PK11195 concentrations in rodent whole blood and tissue samples by rapid and reproducible chromatographic method to support target-occupancy PET studies

OBJECTIVE
The conventional approach to the analysis of dynamic PET data can be described as a two-stage approach. In Stage 1, each individual's kinetic parameter estimates are obtained by modeling their PET data. Then in Stage 2, those parameter estimates are treated as though they are the observed data and compared across subjects and groups using standard statistical analyses. In this context, we explore the application of nonlinear mixed-effects (NLME) model under the assumptions of simplified reference tissue model.


METHODS
In the NLME framework, all subject's PET data are modeled simultaneously and the estimation of kinetic parameters and statistical inference across subjects are performed jointly.


RESULTS
In simulated [ 11C]WAY100635 data, this NLME approach shows improved power (6-27% increase) for detecting group differences and greater consistency of population (1.13-1.44 times greater) and individual-level parameter estimation compared to the two-stage approach applying simplified reference tissue model for pharmacokinetic modeling of PET data. We applied our NLME approach to clinical PET data and observed shrinkage of individual-level parameters that is inherent in this modeling structure.


CONCLUSION
The proposed approach is more powerful and accurate than the two-stage approach under the assumptions of simplified reference tissue model in PET data.


SIGNIFICANCE
The stability of the NLME approach not only improves the efficiency of collected data, but also comes with no additional financial cost and negligible computation cost.

Nonlinear Mixed-Effects Modeling Approach for Simplified Reference Tissue Model.

Receptor occupancy (RO) PET is a non-invasive way to determine drug on target. Given the complexity of procedures, long acquisition times, and high cost, ligand displacement imaging trials often have a limited size and produce sparse RO results over the time course of the blocking drug. To take the best advantage of the available data, we propose a Bayesian hierarchical model to analyze RO as a function of the displacing drug. The model has three components: the first estimates RO using brain regional time-radioactivity concentrations, the second shapes the pharmacokinetic profile of the blocking drug, and the last relates PK to RO. Compared to standard 2-steps RO estimation methods, our Bayesian approach quantifies the variability of the individual RO measures. The model has also useful prediction capabilities: to quantify brain RO for dosage regimens of the drug that were not tested in the experiment. This permits the optimal dose selection of neuroscience drugs at a limited cost. We illustrate the meth...

Bayesian Hierarchical Modeling of Receptor Occupancy in PET Trials

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Bayesian Hierarchical Modeling of Receptor Occupancy in PET Trials