• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

放射性药物治疗中时间-活度曲线的药代动力学模型测定

A Pharmacokinetic Model Determination of Time Activity Curves in Radiopharmaceutical Therapy.

作者信息

Steiner Joseph, Nguyen Brandon, Jafari Farhad

机构信息

Department of Radiology, University of Chicago, Chicago, IL, USA.

Department of Radiology, University of Minnesota Twin Cities, Minneapolis, MN, USA.

出版信息

Mol Imaging. 2024 Nov 3;23:15353508241280015. doi: 10.1177/15353508241280015. eCollection 2024 Jan-Dec.

DOI:10.1177/15353508241280015
PMID:40098749
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11911383/
Abstract

INTRODUCTION AND PURPOSE

Radiopharmaceutical therapy (RPT) dosimetry can be challenging to perform due to sparse data measurements and variations in how the time activity curve (TAC) is determined. In this work, a single system of equations was theoretically derived to estimate the TAC.

METHODS

A pharmacokinetic (PK) model was developed to estimate patient specific rate constants for a given set of body compartments. The PK model and an optimizer were numerically implemented to determine the rate constants and, using these physiologic data, to generate TACs and time integrated activities (TIAs) for 3 tissue systems from clinical data gathered in 5 patients. A fourth (aggregate) tissue compartment is added using conservation of activity considerations.

RESULTS

Feasibility of the PK model was demonstrated by successfully generating TACs and TIAs for all patients in a manner comparable to existing methods in the literature. The data are compared to smaller sampling regimes. Differences between the 3- and 4-compartment models show that conservation of activity considerations should be part of TAC estimations.

CONCLUSION

The results here suggest a new paradigm in RPT in using the rate constants so identified as a diagnostic tool and as a vehicle to achieving individualized tumorcidal dose and/or the maximum tolerable dose to normal tissues.

摘要

引言与目的

由于数据测量稀疏以及时间-活度曲线(TAC)确定方式的差异,放射性药物治疗(RPT)剂量测定颇具挑战性。在本研究中,从理论上推导了一个单一方程组来估计TAC。

方法

建立了一个药代动力学(PK)模型,以估计给定身体隔室组的患者特异性速率常数。对PK模型和优化器进行了数值实现,以确定速率常数,并利用这些生理数据,根据5例患者收集的临床数据生成3个组织系统的TAC和时间积分活度(TIA)。通过考虑活度守恒增加了第四个(总合)组织隔室。

结果

通过以与文献中现有方法相当的方式成功为所有患者生成TAC和TIA,证明了PK模型的可行性。将数据与较小的采样方案进行了比较。三室模型和四室模型之间的差异表明,活度守恒考虑应成为TAC估计的一部分。

结论

此处结果表明RPT的一种新范式,即使用如此确定的速率常数作为诊断工具,并作为实现个体化肿瘤杀伤剂量和/或正常组织最大耐受剂量的手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/f92050f4f322/10.1177_15353508241280015-fig19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/b51a09d0b30f/10.1177_15353508241280015-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/64e0bba46aad/10.1177_15353508241280015-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/8318cba7e7c4/10.1177_15353508241280015-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/23321b933e51/10.1177_15353508241280015-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/a68e386b54fb/10.1177_15353508241280015-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/766507be9fd1/10.1177_15353508241280015-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/32a4d4b47722/10.1177_15353508241280015-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/8f94db55c867/10.1177_15353508241280015-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/8c077da19095/10.1177_15353508241280015-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/06c110d513e4/10.1177_15353508241280015-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/955a7f3d9ca8/10.1177_15353508241280015-fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/532a771e3d07/10.1177_15353508241280015-fig12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/fbfaee6cbcdd/10.1177_15353508241280015-fig13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/b8ddf22f66d9/10.1177_15353508241280015-fig14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/b129adaf3efd/10.1177_15353508241280015-fig15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/e9042dcfb85b/10.1177_15353508241280015-fig16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/df8d7db9c0f0/10.1177_15353508241280015-fig17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/1d9d3188a667/10.1177_15353508241280015-fig18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/f92050f4f322/10.1177_15353508241280015-fig19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/b51a09d0b30f/10.1177_15353508241280015-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/64e0bba46aad/10.1177_15353508241280015-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/8318cba7e7c4/10.1177_15353508241280015-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/23321b933e51/10.1177_15353508241280015-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/a68e386b54fb/10.1177_15353508241280015-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/766507be9fd1/10.1177_15353508241280015-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/32a4d4b47722/10.1177_15353508241280015-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/8f94db55c867/10.1177_15353508241280015-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/8c077da19095/10.1177_15353508241280015-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/06c110d513e4/10.1177_15353508241280015-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/955a7f3d9ca8/10.1177_15353508241280015-fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/532a771e3d07/10.1177_15353508241280015-fig12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/fbfaee6cbcdd/10.1177_15353508241280015-fig13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/b8ddf22f66d9/10.1177_15353508241280015-fig14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/b129adaf3efd/10.1177_15353508241280015-fig15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/e9042dcfb85b/10.1177_15353508241280015-fig16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/df8d7db9c0f0/10.1177_15353508241280015-fig17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/1d9d3188a667/10.1177_15353508241280015-fig18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e29/11911383/f92050f4f322/10.1177_15353508241280015-fig19.jpg

相似文献

1
A Pharmacokinetic Model Determination of Time Activity Curves in Radiopharmaceutical Therapy.放射性药物治疗中时间-活度曲线的药代动力学模型测定
Mol Imaging. 2024 Nov 3;23:15353508241280015. doi: 10.1177/15353508241280015. eCollection 2024 Jan-Dec.
2
Time-Activity data fitting in molecular Radiotherapy: Methodology and pitfalls.分子放射治疗中的时间-活性数据拟合:方法学与陷阱。
Phys Med. 2024 Jan;117:103192. doi: 10.1016/j.ejmp.2023.103192. Epub 2023 Dec 5.
3
Linear Boltzmann equation solver for voxel-level dosimetry in radiopharmaceutical therapy: Comparison with Monte Carlo and kernel convolution.用于放射性药物治疗体素水平剂量学的线性 Boltzmann 方程求解器:与蒙特卡罗和核卷积的比较。
Med Phys. 2024 Aug;51(8):5604-5617. doi: 10.1002/mp.16996. Epub 2024 Mar 4.
4
Technical note: A wearable radiation measurement system for collection of patient-specific time-activity data in radiopharmaceutical therapy: system design and Monte Carlo simulation results.技术说明:一种可穿戴辐射测量系统,用于收集放射性药物治疗中患者特定的时间-活性数据:系统设计和蒙特卡罗模拟结果。
Med Phys. 2021 Dec;48(12):8117-8126. doi: 10.1002/mp.15311. Epub 2021 Nov 23.
5
Particle filter de-noising of voxel-specific time-activity-curves in personalized Lu therapy.体素特异性时间-活性曲线在个体化~(177)Lu 治疗中的粒子滤波去噪。
Z Med Phys. 2020 May;30(2):116-134. doi: 10.1016/j.zemedi.2019.10.005. Epub 2019 Dec 17.
6
Dosimetry for radiopharmaceutical therapy.放射性药物治疗的剂量学。
Semin Nucl Med. 2014 May;44(3):172-8. doi: 10.1053/j.semnuclmed.2014.03.007.
7
Voxel-Level Dosimetry for Combined Iodine 131 Radiopharmaceutical Therapy and External Beam Radiation Therapy Treatment Paradigms for Head and Neck Cancer.体素水平剂量学在头颈部癌症碘 131 放射性药物治疗与外照射放射治疗联合治疗方案中的应用。
Int J Radiat Oncol Biol Phys. 2024 Jul 15;119(4):1275-1284. doi: 10.1016/j.ijrobp.2024.02.005. Epub 2024 Feb 16.
8
The role of preclinical models in radiopharmaceutical therapy.临床前模型在放射性药物治疗中的作用。
Am Soc Clin Oncol Educ Book. 2014:e121-5. doi: 10.14694/EdBook_AM.2014.34.e121.
9
Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics.通过对 PSMA 导向放射性药物治疗中的器官内异质性进行特征描述,实现治疗前 PET 体素剂量预测。
Eur J Nucl Med Mol Imaging. 2024 Sep;51(11):3450-3460. doi: 10.1007/s00259-024-06737-3. Epub 2024 May 9.
10
Application of a whole-body pharmacokinetic model for targeted radionuclide therapy to NM404 and FLT.全身药代动力学模型在 NM404 和 FLT 靶向放射性核素治疗中的应用。
Phys Med Biol. 2012 Mar 21;57(6):1641-57. doi: 10.1088/0031-9155/57/6/1641. Epub 2012 Mar 7.

本文引用的文献

1
Lu-177-PSMA dosimetry for kidneys and tumors based on SPECT images at two imaging time points.基于两个成像时间点的SPECT图像对肾脏和肿瘤进行镥-177-前列腺特异性膜抗原剂量测定。
Front Med (Lausanne). 2023 Nov 13;10:1246881. doi: 10.3389/fmed.2023.1246881. eCollection 2023.
2
Optimization of the radiation dosimetry protocol in Lutetium-177-PSMA therapy: toward clinical implementation.镥-177-前列腺特异性膜抗原治疗中放射剂量测定方案的优化:迈向临床应用
EJNMMI Res. 2023 Jan 24;13(1):6. doi: 10.1186/s13550-023-00952-z.
3
Toward Single-Time-Point Image-Based Dosimetry of Lu-PSMA-617 Therapy.
基于单次成像的 Lu-PSMA-617 治疗剂量学研究。
J Nucl Med. 2023 May;64(5):767-774. doi: 10.2967/jnumed.122.264594. Epub 2023 Jan 19.
4
Current use and future potential of (physiologically based) pharmacokinetic modelling of radiopharmaceuticals: a review.当前放射性药物(基于生理学的)药代动力学模型的应用和未来潜力:综述。
Theranostics. 2022 Nov 14;12(18):7804-7820. doi: 10.7150/thno.77279. eCollection 2022.
5
An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 1: Design and Early Results of the SNMMI Dosimetry Challenge.国际影响剂量计算变异性因素研究,第 1 部分:SNMMI 剂量挑战的设计和初步结果。
J Nucl Med. 2021 Dec;62(Suppl 3):36S-47S. doi: 10.2967/jnumed.121.262748.
6
Influence of dosimetry method on bone lesion absorbed dose estimates in PSMA therapy: application to mCRPC patients receiving Lu-177-PSMA-I&T.剂量测定方法对PSMA治疗中骨病变吸收剂量估计的影响:应用于接受Lu-177-PSMA-I&T的mCRPC患者
EJNMMI Phys. 2021 Mar 12;8(1):26. doi: 10.1186/s40658-021-00369-4.
7
A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After Lu-DOTATATE.一种使用非线性混合模型的新型时-活度信息分享方法,可在减少成像时间点的情况下进行个体化剂量学:Lu-DOTATATE 后 SPECT/CT 的应用。
J Nucl Med. 2021 Aug 1;62(8):1118-1125. doi: 10.2967/jnumed.120.256255. Epub 2020 Dec 18.
8
A Rapid and Safe Infusion Protocol for Lu Peptide Receptor Radionuclide Therapy.Lu 肽受体放射性核素治疗的快速安全输注方案。
J Nucl Med. 2021 Jun 1;62(6):816-822. doi: 10.2967/jnumed.120.252494. Epub 2020 Nov 27.
9
Current Status of Radiopharmaceutical Therapy.放射性药物治疗的现状
Int J Radiat Oncol Biol Phys. 2021 Mar 15;109(4):891-901. doi: 10.1016/j.ijrobp.2020.08.035. Epub 2020 Aug 14.
10
Radiopharmaceutical therapy in cancer: clinical advances and challenges.放射性药物治疗癌症:临床进展与挑战。
Nat Rev Drug Discov. 2020 Sep;19(9):589-608. doi: 10.1038/s41573-020-0073-9. Epub 2020 Jul 29.