An artificial intelligence-assisted PARP-1-targeted PET imaging agent [⁶⁸Ga]Ga-DOTA-FZPF: From bench to clinic.
4 affiliations
- 1Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- 3Center for Biomedical Imaging, Fudan University, Shanghai, 200032, China.
- 4Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, 200032, China.
Abstract
Considering that clinical data for ⁶⁸Ga-labeled poly(ADP-ribose) polymerase (PARP)-targeted probes have not yet been reported and integrated artificial intelligence (AI) workflows for radiotracer development are still lacking, to support radiotracer development and clinical translation, we constructed an AI-assisted multi-stage prioritization workflow and then synthesized two ⁶⁸Ga-labeled PARP-1-targeted probes, validating their performance through in vitro, in vivo, and clinical evaluations. An AI-guided multi-objective workflow (encoder-decoder recurrent neural network (RNN) generation, convolutional neural network (CNN) docking scoring, Synthetic Complexity Learned from a Reaction Corpus (SCScore)-based tractability triage, and deep neural network (DNN)) was used to prioritize a derivatization-ready Fuzuloparib analog (NH₂-FZPF) and to guide chelator installation, yielding DOTA-FZPF and NOTA-FZPF precursors for subsequent ⁶⁸Ga radiolabeling. Both [⁶⁸Ga]Ga-DOTA-FZPF and [⁶⁸Ga]Ga-NOTA-FZPF exhibited high radiochemical purity, stability, and PARP-1-targeted specificity. In tumor models, [⁶⁸Ga]Ga-DOTA-FZPF displayed favorable tumor uptake and rapid background clearance, supporting further evaluation. Exploratory positron emission tomography (PET) assessments of Fuzuloparib target engagement show the specificity and functionality of [⁶⁸Ga]Ga-DOTA-FZPF, supporting in vivo evaluation of PARP inhibitor target engagement and pharmacodynamics. This study recruited 4 cancer patients and performed positron emission tomography/computed tomography (PET/CT) scans using [⁶⁸Ga]Ga-DOTA-FZPF. Biopsy specimens showed that PET/CT-positive lesions corresponded to regions of high PARP-1 expression. This study demonstrates how an AI rational design workflow can support decision-making in radiotracer design while maintaining synthetic feasibility and translational potential. These preliminary findings support the clinical feasibility of [⁶⁸Ga]Ga-DOTA-FZPF and suggest that it is promising for non-invasive assessment of PARP-1 expression and dynamic monitoring of in vivo target occupancy and pharmacodynamics.
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BibTeX
@article{ji2026artificial,
title = {{An artificial intelligence-assisted PARP-1-targeted PET imaging agent [⁶⁸Ga]Ga-DOTA-FZPF: From bench to clinic.}},
author = {Mengjing Ji and Chunjuan Jiang and Yue Xi and Shuai Liu and Simin He and Jianping Zhang and Xiaoping Xu and Zhongyi Yang and Xiangwei Wang and Shaoli Song},
journal = {Materials today. Bio},
year = {2026},
month = oct,
doi = {10.1016/j.mtbio.2026.103728},
url = {https://europepmc.org/article/MED/42898447},
note = {CC BY},
}
RIS
TY - JOUR
TI - An artificial intelligence-assisted PARP-1-targeted PET imaging agent [⁶⁸Ga]Ga-DOTA-FZPF: From bench to clinic.
AU - Mengjing Ji
AU - Chunjuan Jiang
AU - Yue Xi
AU - Shuai Liu
AU - Simin He
AU - Jianping Zhang
AU - Xiaoping Xu
AU - Zhongyi Yang
AU - Xiangwei Wang
AU - Shaoli Song
PY - 2026
DA - 2026/10/07
JO - Materials today. Bio
DO - 10.1016/j.mtbio.2026.103728
UR - https://europepmc.org/article/MED/42898447
AB - Considering that clinical data for ⁶⁸Ga-labeled poly(ADP-ribose) polymerase (PARP)-targeted probes have not yet been reported and integrated artificial intelligence (AI) workflows for radiotracer development are still lacking, to support radiotracer development and clinical translation, we constructed an AI-assisted multi-stage prioritization workflow and then synthesized two ⁶⁸Ga-labeled PARP-1-targeted probes, validating their performance through in vitro, in vivo, and clinical evaluations. An AI-guided multi-objective workflow (encoder-decoder recurrent neural network (RNN) generation, convolutional neural network (CNN) docking scoring, Synthetic Complexity Learned from a Reaction Corpus (SCScore)-based tractability triage, and deep neural network (DNN)) was used to prioritize a derivatization-ready Fuzuloparib analog (NH₂-FZPF) and to guide chelator installation, yielding DOTA-FZPF and NOTA-FZPF precursors for subsequent ⁶⁸Ga radiolabeling. Both [⁶⁸Ga]Ga-DOTA-FZPF and [⁶⁸Ga]Ga-NOTA-FZPF exhibited high radiochemical purity, stability, and PARP-1-targeted specificity. In tumor models, [⁶⁸Ga]Ga-DOTA-FZPF displayed favorable tumor uptake and rapid background clearance, supporting further evaluation. Exploratory positron emission tomography (PET) assessments of Fuzuloparib target engagement show the specificity and functionality of [⁶⁸Ga]Ga-DOTA-FZPF, supporting in vivo evaluation of PARP inhibitor target engagement and pharmacodynamics. This study recruited 4 cancer patients and performed positron emission tomography/computed tomography (PET/CT) scans using [⁶⁸Ga]Ga-DOTA-FZPF. Biopsy specimens showed that PET/CT-positive lesions corresponded to regions of high PARP-1 expression. This study demonstrates how an AI rational design workflow can support decision-making in radiotracer design while maintaining synthetic feasibility and translational potential. These preliminary findings support the clinical feasibility of [⁶⁸Ga]Ga-DOTA-FZPF and suggest that it is promising for non-invasive assessment of PARP-1 expression and dynamic monitoring of in vivo target occupancy and pharmacodynamics.
SN - 2590-0064
ER -