An artificial intelligence-assisted PARP-1-targeted PET imaging agent [⁶⁸Ga]Ga-DOTA-FZPF: From bench to clinic.

Authors

Mengjing Ji, Chunjuan Jiang, Yue Xi, Shuai Liu, Simin He, Jianping Zhang, Xiaoping Xu, Zhongyi Yang, Xiangwei Wang, Shaoli Song

Published in

Materials today. Bio. Volume 41. Pages 103728. Epub Oct 07, 2026.

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.

PMID:
42898447
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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