Identification of positron emission tomography (PET) tracer candidates by prediction of the target-bound fraction in the brain.

Background: Development of tracers for imaging with positron emission tomography (PET) is often a time-consuming process associated with considerable attrition. In an effort to simplify this process, we herein propose a mechanistically integrated approach for the selection of tracer candidates based on in vitro measurements of ligand affinity (Kd), non-specific binding in brain tissue (Vu,brain), and target protein expression (Bmax).

Methods: A dataset of 35 functional and 12 non-functional central nervous system (CNS) PET tracers was compiled. Data was identified in literature for Kd and Bmax, whereas a brain slice methodology was used to determine values for Vu,brain. A mathematical prediction model for the target-bound fraction of tracer in the brain (ftb) was derived and evaluated with respect to how well it predicts tracer functionality compared to traditional PET tracer candidate selection criteria.

Results: The methodology correctly classified 31/35 functioning and 12/12 non-functioning tracers. This predictivity was superior to traditional classification criteria or combinations thereof.

Conclusions: The presented CNS PET tracer identification approach is rapid and accurate and is expected to facilitate the development of novel PET tracers for the molecular imaging community.