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 (K(d)), non-specific binding in brain tissue (V(u,brain)), and target protein expression (B(max)). METHODS: A dataset of 35 functional and 12 non-functional central nervous system (CNS) PET tracers was compiled. Data was identified in literature for K(d) and B(max), whereas a brain slice methodology was used to determine values for V(u,brain). A mathematical prediction model for the target-bound fraction of tracer in the brain (f(tb)) 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13550-014-0050-6) contains supplementary material, which is available to authorized users.