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Bull Math Biol. 2017 Dec 11. doi: 10.1007/s11538-017-0374-2. [Epub ahead of print]

Quantitative PET Imaging in Drug Development: Estimation of Target Occupancy.

Author information

1
PET Center, Department of Radiology and Biomedical Imaging, Yale University, 15 York Street/LMP 89A, P. O. Box 208048, New Haven, CT, 06520-8048, USA.
2
Department of Biomedical Engineering, Yale University, 15 York Street/LMP 89A, P. O. Box 208048, New Haven, CT, 06520-8048, USA.
3
PET Center, Department of Radiology and Biomedical Imaging, Yale University, 15 York Street/LMP 89A, P. O. Box 208048, New Haven, CT, 06520-8048, USA. richard.carson@yale.edu.
4
Department of Biomedical Engineering, Yale University, 15 York Street/LMP 89A, P. O. Box 208048, New Haven, CT, 06520-8048, USA. richard.carson@yale.edu.

Abstract

Positron emission tomography, an imaging tool using radiolabeled tracers in humans and preclinical species, has been widely used in recent years in drug development, particularly in the central nervous system. One important goal of PET in drug development is assessing the occupancy of various molecular targets (e.g., receptors, transporters, enzymes) by exogenous drugs. The current linear mathematical approaches used to determine occupancy using PET imaging experiments are presented. These algorithms use results from multiple regions with different target content in two scans, a baseline (pre-drug) scan and a post-drug scan. New mathematical estimation approaches to determine target occupancy, using maximum likelihood, are presented. A major challenge in these methods is the proper definition of the covariance matrix of the regional binding measures, accounting for different variance of the individual regional measures and their nonzero covariance, factors that have been ignored by conventional methods. The novel methods are compared to standard methods using simulation and real human occupancy data. The simulation data showed the expected reduction in variance and bias using the proper maximum likelihood methods, when the assumptions of the estimation method matched those in simulation. Between-method differences for data from human occupancy studies were less obvious, in part due to small dataset sizes. These maximum likelihood methods form the basis for development of improved PET covariance models, in order to minimize bias and variance in PET occupancy studies.

PMID:
29230702
DOI:
10.1007/s11538-017-0374-2

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