基于诊断性[68Ga]Ga-HA-DOTATATE成像来预测[177Lu]Lu-HA-DOTATATE在肾脏和肿瘤中的摄取情况是有效剂量配准的关键一步。本研究评估了[177Lu]Lu-HA-DOTATATE和[68Ga]Ga-HA-DOTATATE之间的种群药物动力学（PK）差异，并基于[68Ga]Ga-HA-DOTATATE成像预测了[177Lu]Lu-HA-DOTATATE。建立了一个半生理非线性混合效应模型，包括六个亚区（代表血液、脾脏、肾脏、肿瘤病灶、其他生长抑素受体表达的器官和一个总和区）。模型参数基于先前开发的[68Ga]Ga-HA-DOTATATE生理药物动力学模型进行固定。对于[177Lu]Lu-HA-DOTATATE，PK参数基于文献数值或根据来自9名患者的扫描数据（注射后四个时间点）进行估计。最后，根据个体[68Ga]Ga-HA-DOTATATE扫描数据使用贝叶斯估计预测个体[177Lu]Lu-HA-DOTATATE对肿瘤和肾脏的摄取。使用相对预测误差（RPE）对面积下曲线（AUC）和吸收剂量进行了观察数据与预测数据的评估比较。最后，为了评估诊断成像在预测治疗暴露方面的预测价值，比较了个体预测RPE（使用贝叶斯估计）与种群预测（使用种群模型）的预测误差。相对于[68Ga]Ga-HA-DOTATATE，[177Lu]Lu-HA-DOTATATE的脾脏、肾脏和肿瘤的种群摄取速率参数相差0.29倍（15%相对标准误差（RSE））、0.49倍（15% RSE）和1.43倍（14% RSE）。模型预测适当地描述了两种肽的肾脏和肿瘤的观察数据（根据拟合效果图的视觉检查）。相对于肾脏预测（RPE AUC -53%至41%），肿瘤摄取的个体预测效果更好（RPE AUC -40%至28%）。对于肿瘤和肾脏，吸收剂量的预测效果较差（肿瘤和肾脏的RPE分别为-51%至44%和-58%至82%）。对于大多数患者而言，基于个人PK参数从诊断性成像中预测的[177Lu]Lu-HA-DOTATATE肿瘤积累比使用种群参数的预测更好。我们的半生理PK模型表明，[68Ga]Ga-HA-DOTATATE和[177Lu]Lu-HA-DOTATATE的PK参数存在明显差异。诊断影像提供了额外的信息，可以相对于使用种群方法来个体预测[177Lu]Lu-HA-DOTATATE的肿瘤摄取。此外，个体预测表明，除了PK差异之外，还有许多因素参与[177Lu]Lu-HA-DOTATATE的分布预测。© 2023. Springer Nature Switzerland AG.

Prediction of [177Lu]Lu-HA-DOTATATE kidney and tumor uptake based on diagnostic [68Ga]Ga-HA-DOTATATE imaging would be a crucial step for precision dosing of [177Lu]Lu-HA-DOTATATE. In this study, the population pharmacokinetic (PK) differences between [177Lu]Lu-HA-DOTATATE and [68Ga]Ga-HA-DOTATATE were assessed and subsequently [177Lu]Lu-HA-DOTATATE was predicted based on [68Ga]Ga-HA-DOTATATE imaging.A semi-physiological nonlinear mixed-effects model was developed for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE, including six compartments (representing blood, spleen, kidney, tumor lesions, other somatostatin receptor expressing organs and a lumped rest compartment). Model parameters were fixed based on a previously developed physiologically based pharmacokinetic model for [68Ga]Ga-HA-DOTATATE. For [177Lu]Lu-HA-DOTATATE, PK parameters were based on literature values or estimated based on scan data (four time points post-injection) from nine patients. Finally, individual [177Lu]Lu-HA-DOTATATE uptake into tumors and kidneys was predicted based on individual [68Ga]Ga-HA-DOTATATE scan data using Bayesian estimates. Predictions were evaluated compared to observed data using a relative prediction error (RPE) for both area under the curve (AUC) and absorbed dose. Lastly, to assess the predictive value of diagnostic imaging to predict therapeutic exposure, individual prediction RPEs (using Bayesian estimation) were compared to those from population predictions (using the population model).Population uptake rate parameters for spleen, kidney and tumors differed by a 0.29-fold (15% relative standard error (RSE)), 0.49-fold (15% RSE) and 1.43-fold (14% RSE), respectively, for [177Lu]Lu-HA-DOTATATE compared to [68Ga]Ga-HA-DOTATATE. Model predictions adequately described observed data in kidney and tumors for both peptides (based on visual inspection of goodness-of-fit plots). Individual predictions of tumor uptake were better (RPE AUC -40 to 28%) compared to kidney predictions (RPE AUC -53 to 41%). Absorbed dose predictions were less predictive for both tumor and kidneys (RPE tumor and kidney -51 to 44% and -58 to 82%, respectively). For most patients, [177Lu]Lu-HA-DOTATATE tumor accumulation predictions based on individual PK parameters estimated from diagnostic imaging outperformed predictions based on population parameters.Our semi-physiological PK model indicated clear differences in PK parameters for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE. Diagnostic images provided additional information to individually predict [177Lu]Lu-HA-DOTATATE tumor uptake compared to using a population approach. In addition, individual predictions indicated that many aspects, apart from PK differences, play a part in predicting [177Lu]Lu-HA-DOTATATE distribution.© 2023. Springer Nature Switzerland AG.