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.