Marin Thibault, Belov Vasily, Chemli Yanis, Ouyang Jinsong, Najmaoui Yassir, Fakhri Georges El, Duvvuri Sridhar, Iredale Philip, Guehl Nicolas J, Normandin Marc D, Petibon Yoann
IEEE Trans Biomed Eng. 2025 Mar;72(3):1057-1066. doi: 10.1109/TBME.2024.3486191. Epub 2025 Feb 20.
Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions.
In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans.
The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions.
Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators.
The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions.
This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.
使用正电子发射断层扫描（PET）神经成像进行的受体占有率（RO）研究在中枢神经系统（CNS）靶向药物的研发中起着关键作用。估计药物受体占有率的传统方法是估计两次PET扫描（基线扫描和药物注射后扫描）之间的结合潜力变化。这种估计通常是对每次扫描分别进行的，首先重建动态PET扫描数据，然后将动力学模型拟合到时间-活度曲线。这种方法未能正确模拟PET测量中的噪声，导致RO估计效果不佳，尤其是在低受体密度区域。
在这项研究中，我们评估一种新型的联合直接参数重建框架，以从一对基线和药物注射后的动态PET扫描中直接估计大脑中RO和其他动力学参数的分布。
所提出的方法将RO图上的正则化与交替优化相结合，即使在低结合区域也能实现占有率估计。
模拟结果表明，该方法在占有率的准确性和精确性方面比传统方法有定量改进。所提出的方法还在临床前体内实验中使用11C-MK-6884和一种毒蕈碱型乙酰胆碱受体4正变构调节剂药物进行了评估，与传统估计方法相比，显示出对受体占有率的估计有所改善。
与传统方法相比，所提出的联合直接估计框架改进了RO估计，尤其是在中低结合区域。
这项工作可能有助于评估新型CNS靶向候选药物。
