Development of Prostate-Specific Lysosome-Targeting Degraders

Targeted protein degradation (TPD) technologies have emerged as transformative therapeutic modality for treating cancers and other diseases. While significant progress has been achieved in intracellular protein degradation, degradation of membrane proteins and extracellular targets remains in an early stage. In this study, we developed a prostate-specific lysosome-targeting degradation strategy using a prostate-specific membrane antigen (PSMA) as a lysosome-targeting receptor (LTR). We demonstrated that both extracellular and membrane proteins can be selectively degraded in prostate cancer cells via the lysosome pathway. These PSMA TArgeting Chimeras (PTACs) were shown to facilitate lysosomal degradation in a selective, potent, rapid, and sustained manner. Notably, <b>Ctx-L3</b> and <b>Atz-L5</b> exhibited exceptional degradation potencies in LNCaP cells, with DC<sub>50</sub> values of 4.3 pM for EGFR and 2 pM for PD-L1, respectively─among the most potent degraders reported to date. Furthermore, the application of PTACs to degrade PD-L1, using both antibody- and small-molecule-based formats, highlights the versatility of this platform. Collectively, this work advances the application of TPD technology and offers promising avenues for precision medicine in prostate-related diseases.

Authors

Institutions

Publication Details

Journal
Journal of the American Chemical Society
Published
2026-02-11
DOI
https://doi.org/10.1021/jacs.5c18594
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Development of Prostate-Specific Lysosome-Targeting Degraders

Xuankun Chen, Deqin Cai, Y. Zhou, Weiping Tang et al.
Journal of the American Chemical Society
Protein Degradation and Inhibitors
article

Development of Prostate-Specific Lysosome-Targeting Degraders

Xuankun Chen, Deqin Cai, Y. Zhou, Weiping Tang, Reinier Hernandez, Malick Bio Idrissou
article en

Abstract

Targeted protein degradation (TPD) technologies have emerged as transformative therapeutic modality for treating cancers and other diseases. While significant progress has been achieved in intracellular protein degradation, degradation of membrane proteins and extracellular targets remains in an early stage. In this study, we developed a prostate-specific lysosome-targeting degradation strategy using a prostate-specific membrane antigen (PSMA) as a lysosome-targeting receptor (LTR). We demonstrated that both extracellular and membrane proteins can be selectively degraded in prostate cancer cells via the lysosome pathway. These PSMA TArgeting Chimeras (PTACs) were shown to facilitate lysosomal degradation in a selective, potent, rapid, and sustained manner. Notably, <b>Ctx-L3</b> and <b>Atz-L5</b> exhibited exceptional degradation potencies in LNCaP cells, with DC<sub>50</sub> values of 4.3 pM for EGFR and 2 pM for PD-L1, respectively─among the most potent degraders reported to date. Furthermore, the application of PTACs to degrade PD-L1, using both antibody- and small-molecule-based formats, highlights the versatility of this platform. Collectively, this work advances the application of TPD technology and offers promising avenues for precision medicine in prostate-related diseases.

Journal of the American Chemical SocietyVol. 148(7)
University of Wisconsin–Madison (US), Pharmaceutical Product Development (India) (IN)
National Institutes of Health, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 59%
Protein Degradation and Inhibitors
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.