Abstract
BACKGROUND: Drug occupancy studies with positron emission tomography imaging are used routinely in early phase drug development trials. Recently, our group introduced the Lassen Plot Filter, an extended version of the standard Lassen plot to estimate voxel-level occupancy images. Occupancy images can be used to create an EC(50) image by applying an E(max) model at each voxel. Our goal was to apply functional clustering of occupancy images via a clustering algorithm and produce a more precise EC(50) image while maintaining accuracy. METHOD: A digital brain phantom was used to create 10 occupancy images (corresponding to 10 different plasma concentrations of drug) that correspond to a ground truth EC(50) image containing two bilateral local "hot spots" of high EC(50) (region-1: 25; region-2: 50; background: 6-10 ng/mL). Maximum occupancy was specified as 0.85. An established noise model was applied to the simulated occupancy images and the images were smoothed. Simple Linear Iterative Clustering, an existing k-means clustering algorithm, was modified to segment a series of occupancy images into K clusters (which we call "SLIC-Occ"). EC(50) images were estimated by nonlinear estimation at each cluster (post SLIC-Occ) and voxel (no clustering). Coefficient of variation images were estimated at each cluster and voxel, respectively. The same process was also applied to human occupancy data produced for a previously published study. RESULTS: Variability in EC(50) estimates was reduced by more than 80% in the phantom data after application of SLIC-Occ to occupancy images with only minimal loss of accuracy. A similar, but more modest improvement was achieved in variability when SLIC-Occ was applied to human occupancy images. CONCLUSIONS: Our results suggest that functional segmentation of occupancy images via SLIC-Occ could produce more precise EC(50) images and improve our ability to identify local "hot spots" of high effective affinity of a drug for its target(s).