Trusova Valeriya, Malovytsia Uliana, Kuznietsov Pylyp, Yakymenko Ivan, Gorbenko Galyna
Department of Medical Physics and Biomedical Nanotechnologies, V.N. Karazin Kharkiv National University, 4 Svobody Sq., Kharkiv, 61022, Ukraine.
O.I. Akhiezer Department for Nuclear Physics and High Energy Physics, V.N. Karazin, Kharkiv National University, 4 Svobody Sq., Kharkiv, 61022, Ukraine.
J Comput Aided Mol Des. 2025 Sep 3;39(1):75. doi: 10.1007/s10822-025-00655-8.
Fluorine-18-labeled radiopharmaceuticals are central to PET-based oncology imaging, yet comparative evaluations of their mechanistic behavior and diagnostic potential remain fragmented. In this study, we present a multidimensional in silico framework integrating pharmacokinetic modeling, structural ADMET prediction, and unsupervised clustering to systematically evaluate five widely used F-labeled PET radiopharmaceuticals: [F]FDG, [F]FET, [F]DOPA, [F]FMISO, and [F]FLT. Each radiopharmaceutical was simulated using a harmonized three-compartment model in COPASI to capture uptake dynamics under both normal and pathological conditions. Key pharmacokinetic parameters, including area under the curve, tumor-to-normal tissue ratios, and early-phase uptake slope, were computed and subjected to local sensitivity analysis to assess model robustness. In parallel, in silico ADMET descriptors were extracted via ADMETlab 3.0, providing quantitative insight into lipophilicity, permeability, distribution volume, and metabolic clearance. All features were normalized and integrated into a joint dataset for principal component analysis and hierarchical clustering. The resulting stratification revealed two distinct mechanistic clusters: [F]FDG and [F]FLT were characterized by irreversible trapping and high intracellular retention, whereas [F]FET, [F]DOPA, and [F]FMISO exhibited transporter-mediated uptake with greater sensitivity to permeability and efflux parameters. Diagnostic strengths varied by context, with [F]FET optimal for early-phase imaging and [F]FMISO demonstrating superior tumor selectivity at later timepoints. ADMET features reinforced kinetic signatures, supporting the structure-function rationale underlying radiopharmaceutical performance. This multidimensional in silico evaluation establishes a mechanistically interpretable platform for PET radiopharmaceutical profiling and stratification, advancing preclinical radiopharmaceutical selection and informing precision multiradiopharmaceutical imaging protocols in oncology. However, while our computational approach offers a mechanism-driven platform for radiopharmaceutical stratification, future validation against experimental PET imaging data in both healthy individuals and patients with relevant pathologies is essential to confirm its predictive value and clinical applicability.
氟 - 18标记的放射性药物是基于正电子发射断层扫描（PET）的肿瘤成像的核心，但对其作用机制和诊断潜力的比较评估仍然零散。在本研究中，我们提出了一个多维度的计算机模拟框架，该框架整合了药代动力学建模、结构ADMET预测和无监督聚类，以系统地评估五种广泛使用的F标记PET放射性药物：[F]FDG、[F]FET、[F]DOPA、[F]FMISO和[F]FLT。使用COPASI中的统一三室模型对每种放射性药物进行模拟，以捕捉正常和病理条件下的摄取动态。计算关键药代动力学参数，包括曲线下面积、肿瘤与正常组织比率和早期摄取斜率，并进行局部敏感性分析以评估模型稳健性。同时，通过ADMETlab 3.0提取计算机模拟ADMET描述符，提供对亲脂性、渗透性、分布体积和代谢清除的定量见解。所有特征均进行归一化并整合到联合数据集中进行主成分分析和层次聚类。所得分层揭示了两个不同的作用机制簇：[F]FDG和[F]FLT的特征是不可逆捕获和高细胞内保留，而[F]FET、[F]DOPA和[F]FMISO表现出转运体介导的摄取，对渗透性和外排参数更敏感。诊断优势因情况而异，[F]FET最适合早期成像，[F]FMISO在后期时间点显示出优异的肿瘤选择性。ADMET特征强化了动力学特征，支持放射性药物性能背后的结构 - 功能原理。这种多维度的计算机模拟评估建立了一个可从作用机制上解释的PET放射性药物分析和分层平台，推进临床前放射性药物选择，并为肿瘤学中的精确多放射性药物成像方案提供信息。然而，虽然我们的计算方法为放射性药物分层提供了一个基于机制的平台，但未来针对健康个体和相关病理患者的实验PET成像数据进行验证对于确认其预测价值和临床适用性至关重要。
