# Improved drug efficacy assessment using joint Bayesian estimation framework

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $348,595

## Abstract

Abstract
The development of central nervous system (CNS) drugs requires imaging tools that can
quantify drug efficacy and target engagement as well as the effects of drug concentration on
these quantitative assessment measures. Positron emission tomography (PET) imaging has
become standard practice for quantitative assessment, thanks to its ability to image tracers that
bind to target receptors of drugs. Receptor occupancy (RO) studies, which consist in a pair of
PET scans, baseline and post-drug injection, can be used to quantify, as a function of drug
concentration, the blocking effect of a drug. The concentration yielding half-maximum blocking
effect, called EC50, is determined by first estimating receptor occupancy for each pair of scans,
then fitting a logistic model to the occupancy vs. concentration curve. The conventional method
to estimate RO and EC50 consists in reconstructing the dynamic PET data for each pair of
scans, perform kinetic fitting, typically in high binding regions, to estimate the binding potential
and calculate RO, before performing the logistic fit across concentrations to obtain EC50. The
resulting estimates have low precision due to the noise in dynamic PET images and the lack of
proper noise modeling. Moreover, estimating single RO and EC50 values discards potentially
valuable information on the spatial distribution of RO and EC50. We propose an estimation
framework that jointly estimates spatial RO maps for each pair of scans and a global EC50 map
using an end-to-end model from the PET measurements to the estimated EC50. The estimation
framework relies on advanced optimization strategies to decompose the joint estimation process
into manageable subproblems, such as image reconstruction, parametric fitting, image
denoising and logistic fitting. The method is expected to improve the estimation of RO and EC50
over conventional methods, while offering additional spatial information. The targeted
improvement in estimation would allow to reduce the sample size required in drug trials to
achieve the same statistical power as conventional approaches (or conversely, increase the
statistical power for a fixed sample size). The method will be validated in numerical simulations
and applied to in vivo animal studies.

## Key facts

- **NIH application ID:** 10774900
- **Project number:** 1R01EB035093-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Thibault Marin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $348,595
- **Award type:** 1
- **Project period:** 2024-05-17 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10774900

## Citation

> US National Institutes of Health, RePORTER application 10774900, Improved drug efficacy assessment using joint Bayesian estimation framework (1R01EB035093-01). Retrieved via AI Analytics 2026-06-16 from https://api.ai-analytics.org/grant/nih/10774900. Licensed CC0.

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