title: Selectivity requirements for diagnostic imaging of neurofibrillary lesions in Alzheimer's disease: a simulation study.
abstract: Whole-brain imaging is a promising strategy for premortem detection of tau-bearing neurofibrillary lesions that accumulate in Alzheimer's disease. However, the approach is complicated by the high concentrations of potentially confounding binding sites presented by beta-amyloid plaques. To predict the contributions of relative binding affinity and binding site density to the imaging-dynamics and selectivity of a hypothetical tau-directed radiotracer, a nonlinear, four-tissue compartment pharmacokinetic model of diffusion-mediated radiotracer uptake and distribution was developed. Initial estimates of nonspecific binding and brain uptake parameters were made by fitting data from a previously published kinetic study of Pittsburgh Compound B, an established amyloid-directed radiotracer. The resulting estimates were then used to guide simulations of tau binding selectivity while assuming early-stage accumulation of disease pathology. The simulations suggest that for tau aggregates to represent at least 80% of specific binding signal, binding affinity or density selectivities for tau over beta-amyloid should be at least 20- or 50-fold, respectively. The simulations also suggest, however, that overcoming nonspecific binding will be an additional challenge for tau-directed radiotracers owing to low concentrations of available binding sites. Overall, nonlinear modeling can provide insight into the performance characteristics needed for tau-directed radiotracers in vivo.
authors.affiliation: Department of Molecular and Cellular Biochemistry, The Ohio State University College of Medicine, Columbus, Ohio 43210, USA.
authors.forename: Kelsey N
authors.initials: KN
authors.lastname: Schafer
authors.forename: Sohee
authors.initials: S
authors.lastname: Kim
authors.forename: Anastasios
authors.initials: A
authors.lastname: Matzavinos
authors.forename: Jeff
authors.initials: J
authors.lastname: Kuret
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000000981:diagnostic imaging* / Q000378:metabolism*; D000814:Aniline Compounds; D052160:Benzothiazoles / Q000493:pharmacokinetics*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D008959:Models, Neurological; D016874:Neurofibrillary Tangles / Q000000981:diagnostic imaging* / Q000378:metabolism*; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013844:Thiazoles
chemical_list: C475519:2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole; D000814:Aniline Compounds; D052160:Benzothiazoles; D019275:Radiopharmaceuticals; D013844:Thiazoles