文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
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
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
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 @ 2025

多尺度计算建模助力放射性药物治疗疗效最大化并最小化副作用:氨基酸输注建模

Multi-scale computational modeling towards efficacy in radiopharmaceutical therapies while minimizing side effects: Modeling of amino acid infusion.

作者信息

Golzaryan Aryan, Souri Mohammad, Kashkooli Farshad M, Rahmim Arman, Soltani M

机构信息

Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada.

出版信息

PLoS Comput Biol. 2025 Jul 16;21(7):e1013247. doi: 10.1371/journal.pcbi.1013247.


DOI:10.1371/journal.pcbi.1013247
PMID:40668873
Abstract

Amino acid infusion (AAI) is a technique used in radiopharmaceutical therapy (RPT) to reduce toxicity in kidney and increase clearance rate of radiopharmaceuticals from body. In this study our aim is to evaluate its effect in personalized RPT considering kidney and salivary glands as dose limiting organs using a multiscale modeling framework. We developed a Physiologically-Based Pharmacokinetic (PBPK) model consisting of 19 compartments, personalized it for four prostate cancer patients using data derived from gamma camera imaging. This model was used to investigate the influence of AAI on the absorbed dose to tumors and organs at risk. We then computed the maximum safe injected activity based on the PBPK model. To address the effects of interstitial fluid pressure (IFP) and tumor heterogeneity, we coupled the PBPK model with convection-diffusion-reaction (CDR) equations. To compare the effectiveness of our modeling approaches, we calculated absorbed doses to the tumors with and without AAI, using both the standalone PBPK model and the coupled PBPK-CDR model. Our findings revealed a relative error (RE) of 9.6% ± 2.2% (mean ± SD) in total tumor absorbed dose calculation between PBPK and CDR equations, attributable to the consideration of IFP. Moreover, AAI proved beneficial for RPT when the kidney was designated as the organ-at-risk. It enabled an increase in radiopharmaceutical injection from 12.3 ± 6.32 MBq (mean ± SD) to 15.45 ± 6.95 MBq (RE: 28.5% ± 15.7%), resulting in a corresponding increase in tumor absorbed dose from 67.8 ± 47.45 Gy to 72.43 ± 51.03 Gy (RE: 8.6% ± 5.4%), while maintaining critical kidney absorbed dose limits. However, this was not observed when the salivary gland was considered the dose-limiting organ. Although, AAI allowed for increased therapeutic injection ranging from 4.22 ± 2.23 MBq to 5.25 ± 3.14 MBq (RE: 19.2% ± 9.9%), it results in a minimal increase in tumor absorbed dose of 0.22 ± 0.04 (RE: 1.4% ± 1.3%). Statistical analysis using the Wilcoxon Signed-Rank Test revealed significant effects of AAI on administered activity and tumor absorbed dose (p-value = 0.007 < 0.05). Finally, a local sensitivity analysis was performed on selected radiation and tumor transportation parameters individually to evaluate their impact on the tumor absorbed dose. In conclusion, selection of organ-at-risk in personalized RPT is critical, as it determines the injected activity amount and the efficacy of delivery-enhancing techniques.

摘要

氨基酸输注(AAI)是放射性药物治疗(RPT)中使用的一种技术,用于降低肾脏毒性并提高放射性药物从体内的清除率。在本研究中,我们的目的是使用多尺度建模框架,以肾脏和唾液腺作为剂量限制器官,评估其在个性化RPT中的效果。我们开发了一个由19个隔室组成的基于生理的药代动力学(PBPK)模型,并使用来自伽马相机成像的数据对4名前列腺癌患者进行了个性化处理。该模型用于研究AAI对肿瘤和危险器官吸收剂量的影响。然后,我们基于PBPK模型计算了最大安全注射活度。为了解决间质液压力(IFP)和肿瘤异质性的影响,我们将PBPK模型与对流-扩散-反应(CDR)方程相结合。为了比较我们建模方法的有效性,我们使用独立的PBPK模型和耦合的PBPK-CDR模型,计算了有和没有AAI时肿瘤的吸收剂量。我们的研究结果显示,由于考虑了IFP,PBPK和CDR方程在总肿瘤吸收剂量计算中的相对误差(RE)为9.6%±2.2%(平均值±标准差)。此外,当将肾脏指定为危险器官时,AAI被证明对RPT有益。它使放射性药物注射量从12.3±6.32 MBq(平均值±标准差)增加到15.45±6.95 MBq(RE:28.5%±15.7%),导致肿瘤吸收剂量相应地从67.8±47.45 Gy增加到72.43±51.03 Gy(RE:8.6%±5.4%),同时保持关键肾脏吸收剂量限值。然而,当将唾液腺视为剂量限制器官时,未观察到这种情况。尽管AAI允许治疗性注射量从4.22±2.23 MBq增加到5.25±3.14 MBq(RE:19.2%±9.9%),但它导致肿瘤吸收剂量仅略有增加,为0.22±0.04(RE:1.4%±1.3%)。使用Wilcoxon符号秩检验进行的统计分析显示,AAI对给药活度和肿瘤吸收剂量有显著影响(p值=0.007<0.05)。最后,对选定的辐射和肿瘤传输参数分别进行了局部敏感性分析,以评估它们对肿瘤吸收剂量的影响。总之,在个性化RPT中选择危险器官至关重要,因为它决定了注射活度的量和递送增强技术的疗效。

相似文献

[1]
Multi-scale computational modeling towards efficacy in radiopharmaceutical therapies while minimizing side effects: Modeling of amino acid infusion.

PLoS Comput Biol. 2025-7-16

[2]
Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Clin Orthop Relat Res. 2024-12-1

[3]
Systemic pharmacological treatments for chronic plaque psoriasis: a network meta-analysis.

Cochrane Database Syst Rev. 2021-4-19

[4]
Intravenous magnesium sulphate and sotalol for prevention of atrial fibrillation after coronary artery bypass surgery: a systematic review and economic evaluation.

Health Technol Assess. 2008-6

[5]
Management of urinary stones by experts in stone disease (ESD 2025).

Arch Ital Urol Androl. 2025-6-30

[6]
Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?

Clin Orthop Relat Res. 2024-9-1

[7]
Does Augmenting Irradiated Autografts With Free Vascularized Fibula Graft in Patients With Bone Loss From a Malignant Tumor Achieve Union, Function, and Complication Rate Comparably to Patients Without Bone Loss and Augmentation When Reconstructing Intercalary Resections in the Lower Extremity?

Clin Orthop Relat Res. 2025-6-26

[8]
Systemic pharmacological treatments for chronic plaque psoriasis: a network meta-analysis.

Cochrane Database Syst Rev. 2020-1-9

[9]
Point-cloud segmentation with in-silico data augmentation for prostate cancer treatment.

Med Phys. 2025-4-3

[10]
Falls prevention interventions for community-dwelling older adults: systematic review and meta-analysis of benefits, harms, and patient values and preferences.

Syst Rev. 2024-11-26

本文引用的文献

[1]
Mechanical Forces in Tumor Growth and Treatment: Perspectives From Biology, Physics, Engineering, and Mathematical Modeling.

WIREs Mech Dis. 2025

[2]
Comparing computational times for simulations when using PBPK model template and stand-alone implementations of PBPK models.

Front Toxicol. 2025-2-19

[3]
Whole-body Bacteriophage Distribution Characterized by a Physiologically based Pharmacokinetic Model.

bioRxiv. 2025-2-6

[4]
Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.

Nat Rev Cancer. 2025-5

[5]
Personalized metronomic radiopharmaceutical therapy through injection profile optimization via physiologically based pharmacokinetic (PBPK) modeling.

Sci Rep. 2025-2-3

[6]
Current Use of Physiologically Based Pharmacokinetic modeling in New Medicinal Product Approvals at EMA.

Clin Pharmacol Ther. 2025-3

[7]
Agent-based modeling for the tumor microenvironment (TME).

Math Biosci Eng. 2024-11-25

[8]
A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron.

Drug Deliv Transl Res. 2025-4

[9]
Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics.

Toxics. 2024-6-14

[10]
A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.

Pharm Res. 2024-7

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索