Sequentially Dual-Targeted Molecular Theranostic Probe for Radionuclide and Anti-Hypoxic Activatable Photodynamic Therapy of Cancer

Type I photodynamic therapy (PDT), which utilizes type I photoreactions and is less O2-dependent, is expected to exert ideal synergistic therapy performance when combined with targeted radionuclide therapy (TRT). However, this combination has been less explored. To achieve it, this study presents a sequentially dual-targeted molecular theranostic probe for combining TRT and antihypoxic activatable PDT against prostate cancer. The probe achieves prostate-specific membrane antigen (PSMA)-mediated lock-and-key targeting ability and monoamine oxidase A (MAO-A) hydrolysis with activated fluoro-photoacoustic signals and O2•– generation. The PSMA motif not only enhances the targeting specificity but also increases the intracellular accumulation capacity of probe and promotes more MAO-A activation. In this regard, an enhanced inhibition of tumor growth is obtained for combined activatable antihypoxic PDT and TRT in comparison to any single regimen, including the commercial 177Lu-PSMA-617, thereby demonstrating the effective antitumor effect. This study provides a potential solution for a combination of TRT and PDT for effective tumor therapy.

Authors

Institutions

Publication Details

Journal
ACS Materials Letters
Published
2024-10-18
DOI
https://doi.org/10.1021/acsmaterialslett.4c01564
Citations
4
Primary Topic
Nanoplatforms for cancer theranostics
Type
article
Field-Weighted Citation Impact
0.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sequentially Dual-Targeted Molecular Theranostic Probe for Radionuclide and Anti-Hypoxic Activatable Photodynamic Therapy of Cancer

Yuan Zhang, Qingqing Miao, Chen Wan, Baoliang Cheng et al.
4 citations
ACS Materials Letters
Nanoplatforms for cancer theranostics
0.74
article

Sequentially Dual-Targeted Molecular Theranostic Probe for Radionuclide and Anti-Hypoxic Activatable Photodynamic Therapy of Cancer

Yuan Zhang, Qingqing Miao, Chen Wan, Baoliang Cheng, Yuyang Zhang, Qing Li, Wei Gu, Jieli Zhu
article en
4 citations

Abstract

Type I photodynamic therapy (PDT), which utilizes type I photoreactions and is less O2-dependent, is expected to exert ideal synergistic therapy performance when combined with targeted radionuclide therapy (TRT). However, this combination has been less explored. To achieve it, this study presents a sequentially dual-targeted molecular theranostic probe for combining TRT and antihypoxic activatable PDT against prostate cancer. The probe achieves prostate-specific membrane antigen (PSMA)-mediated lock-and-key targeting ability and monoamine oxidase A (MAO-A) hydrolysis with activated fluoro-photoacoustic signals and O2•– generation. The PSMA motif not only enhances the targeting specificity but also increases the intracellular accumulation capacity of probe and promotes more MAO-A activation. In this regard, an enhanced inhibition of tumor growth is obtained for combined activatable antihypoxic PDT and TRT in comparison to any single regimen, including the commercial 177Lu-PSMA-617, thereby demonstrating the effective antitumor effect. This study provides a potential solution for a combination of TRT and PDT for effective tumor therapy.

ACS Materials LettersVol. 6(11)
University of Science and Technology of China (CN), Anhui Agricultural University (CN), Soochow University (CN), National Institute for Radiological Protection (CN), PRG S&Tech (South Korea)
National Natural Science Foundation of China
Openalex Percentile: Top 32%
Nanoplatforms for cancer theranostics
0.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.