Validation of Occupancy Images from PET Data. A Novel Endpoint for Drug Discovery

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Abstract The most useful application of PET imaging in CNS drug development is to measure occupancy of new drug candidates at target binding sites (e.g., receptors, transporters, and enzymes). These target occupancy (TO) studies -often starting in primates and progressing to Phase 1 in humans - are very helpful for in vivo dose- finding, deciding whether to advance a candidate compound to more costly later phase trials, and optimizing the design of later phase studies. Current State of the Art: A common and informative approach to analysis of TO data is the “Lassen plot”. The Lassen plot yields two point estimates: a fractional occupancy of the target by the drug (ODrug) and the nondisplaceable uptake of the tracer (VND). Assumptions underlying this popular method are that (a) both measures are uniform across the whole brain and (b) the PET tracer binds to the identical population of target sites as the candidate drug. Technology Gap: There are important cases in which one or more assumption of the Lassen plot is violated. In such cases, (1) the outcome of the TO study may be biased, (2) manual intervention may be required, (3) proper interpretation will depend on prior knowledge of the spatial distribution of target subtypes, and in any case, (4) the standard method offers little to no information on regional variation in ODrug. Basic development in the lab: In our research group at Yale, we have developed an extension of the standard Lassen plot that provides information about local variation in ODrug and VND. The outcomes of our new “Lassen Plot Filter” (LPF) algorithm are voxel-by-voxel estimates of ODrug, VND and EC50 (i.e., `occupancy images', `nondisplaceable uptake, and drug affinity, images'). We believe these novel images represent much more informative outcomes of TO studies than standard measures for identifying precise locations of maximal specific action of drugs. For example, when assumptions are violated, occupancy images will be less biased than point estimates. The new images could serve as richer endpoints to drive go/no-go decisions on candidate compounds based on drug action at specific brain locations of greatest therapeutic interest. Academic-Industrial Partnership. Our industrial partner, a company with extensive experience in conducting TO studies, will provide us with very valuable archival data in humans and nonhuman primates for testing and validating our LPF algorithm. In the present project, we will (1) Analyze simulated data with known occupancy distributions. (2) Re-analyze archival data that represent different cases and/or violations of standard assumptions. (3) Perform circumscribed studies in primates to confirm the biological interpretations of our new images. The work will be complete when we have fully characterized and optimized the performance of our LPF algorithm. In the long term, our goal is for a validated version of our voxel-by-voxel analysis of TO studies, to be adopted widely, to the benefit of end-users, to speed drug development.