利用预先计算的单粒子轨迹标准 DNA 损伤数据,对放射性核素靶向治疗中的放射生物学效应进行逐个细胞的快速蒙特卡洛模拟。

A Lim, M Andriotty, T Yusufaly, G Agasthya, B Lee, C Wang
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引用次数: 0

摘要

简介:我们开发了一种新方法,可大幅加快放射生物学蒙特卡洛辐射轨迹结构(MC-RTS)计算的速度:我们开发了一种新方法,可大大加快逐个细胞的放射生物学蒙特卡洛辐射轨迹结构(MC-RTS)计算速度:该技术基于随机抽样和单粒子轨道(SPT)标准 DNA 损伤(SDD)文件的叠加,这些文件来自使用 RTS 代码 TOPAS-nBio 构建的 "预计算 "数据文件库,并人工添加了 "时间戳 "以纳入剂量率效应。这种带有时间戳的 SDD 文件随后可输入 MEDRAS,这是一种机理动力学模型,可计算各种辐射诱导的生物终点,如 DNA 双链断裂(DSB)、错误修复和染色体畸变以及细胞死亡。作为该方法的基准验证,我们计算了预测的随能量变化的DSB产量和DNA直接损伤与总损伤之比,两者均与已发表的体外实验数据一致。随后,我们应用该方法对神经内分泌肿瘤细胞均匀培养 177Lu 的体外实验系统进行了逐个细胞的超快速模拟:辐照后24小时和48小时的残余DSB结果与已发表的文献值一致。我们的工作证明了经济有效的 "硅学克隆细胞存活测定 "的可行性,可用于放射性药物和新型放射治疗方法的计算设计和开发。
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A fast Monte Carlo cell-by-cell simulation for radiobiological effects in targeted radionuclide therapy using pre-calculated single-particle track standard DNA damage data.

Introduction: We developed a new method that drastically speeds up radiobiological Monte Carlo radiation-track-structure (MC-RTS) calculations on a cell-by-cell basis.

Methods: The technique is based on random sampling and superposition of single-particle track (SPT) standard DNA damage (SDD) files from a "pre-calculated" data library, constructed using the RTS code TOPAS-nBio, with "time stamps" manually added to incorporate dose-rate effects. This time-stamped SDD file can then be input into MEDRAS, a mechanistic kinetic model that calculates various radiation-induced biological endpoints, such as DNA double-strand breaks (DSBs), misrepairs and chromosomal aberrations, and cell death. As a benchmark validation of the approach, we calculated the predicted energy-dependent DSB yield and the ratio of direct-to-total DNA damage, both of which agreed with published in vitro experimental data. We subsequently applied the method to perform a superfast cell-by-cell simulation of an experimental in vitro system consisting of neuroendocrine tumor cells uniformly incubated with 177Lu.

Results and discussion: The results for residual DSBs, both at 24 and 48 h post-irradiation, are in line with the published literature values. Our work serves as a proof-of-concept demonstration of the feasibility of a cost-effective "in silico clonogenic cell survival assay" for the computational design and development of radiopharmaceuticals and novel radiotherapy treatments more generally.

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