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Our study aimed to develop a method to mathematically predict the kinetic parameters K  1 (influx rate constant), k  2 (efflux rate constant), and BP ND (nondisplaceable binding potential) of amyloid PET tracers and obtain SUV ratios (SUVRs) from predicted time-activity curves of target and reference regions. Methods: We investigated 10 clinically applied amyloid PET radioligands: 11 C-Pittsburgh compound B, 11 C-BF-227, 11 C-AZD2184, 11 C-SB-13, 18 F-FACT, 18 F-florbetapir, 18 F-florbetaben, 18 F-flutemetamol, 18 F-FDDNP, and 18 F-AZD4694. For each tracer, time-activity curves of both target and reference regions were generated using a simplified 1-tissue-compartment model, with an arterial plasma input function and the predicted kinetic parameters. K  1 , k  2 , and BP ND were derived from the lipophilicity (log P ), apparent volume, free fraction in plasma, free fraction in tissue, dissociation constant, and density of amyloid β using biomathematic modeling. Density was fixed at 3 nM to represent healthy control conditions and 50 nM to represent severe Alzheimer disease (AD). Predicted SUVRs for the healthy and AD groups were then obtained by dividing the integrated time-activity curve of the target region by that of the reference region. To validate the presented method, the predicted K  1 , k  2 , BP ND , and SUVR for the healthy and AD groups were compared with the respective clinically observed values. Results: The correlation between predicted and clinical kinetic parameters had an R  2 value of 0.73 for K  1 in the healthy group, 0.71 for K  1 in the AD group, 0.81 for k  2 in the healthy group, 0.85 for k  2 in the AD group, and 0.63 for BP ND in the AD group. The regression relationship between the predicted SUVR ( y ) and the clinical SUVR ( x ) for the healthy and the AD groups was y = 2.73 x - 2.11 ( R  2 = 0.72). Conclusion: The proposed method showed a good correlation between predicted and clinical SUVR for the 10 clinically applied amyloid tracers.

Prediction of the Clinical SUV Ratio in Amyloid PET Imaging Using a Biomathematic Modeling Approach Toward the Efficient Development of a Radioligand