Evidence map›Paper›PMID 42414544›Full record

ReviewActa pharmacologica Sinica2026

Targeted protein degradation: bridging chemical biology and clinical translation.

Yu-Bo Zhang, Jun-Wei Fu, Yue Liu, Rui-Lin Wu, Yan-Ting Liang, Wen-Han Zhang, Tao Yuan, Hong Zhu, Qiao-Jun He, Bo Yang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Acta pharmacologica Sinica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yu-Bo Zhang *Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Jun-Wei Fu *Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Yue LiuZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Rui-Lin WuZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Yan-Ting LiangZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Wen-Han ZhangZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Tao YuanZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Hong ZhuZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. hongzhu@zju.edu.cn.
Qiao-Jun HeZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. qiaojunhe@zju.edu.cn.
Bo YangZhejiang Province Key Laboratory of Anti-Cancer Drug Research, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. yang924@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional occupancy-driven pharmacology and emerging event-driven targeted protein degradation (TPD) play distinct roles in drug discovery. Although small-molecule inhibitors typically depend on sustained engagement of functional binding and often require systemic exposure, TPD induces selective protein elimination and can access targets that are refractory to conventional inhibition, namely, "undruggable" proteins. Expanding degradation mechanisms mediated by the ubiquitin‒proteasome system (UPS) and alternative pathways is therefore a central strategy for next-generation therapeutics. In this review, we provide an overview of the developmental trajectory of the TPD field and discuss how diverse modalities can be leveraged to address intracellular, membrane-associated, and extracellular protein targets. We summarize the relevant chemical design principles and translational challenges, with a focus on mitigating off-target toxicity, improving selectivity, and increasing bioavailability. The current evidence suggests that proteolysis-targeting chimeras, molecular glues, autophagy-targeting chimeras, and transferrin receptor-targeting chimeras could be key approaches. In addition, we discuss some innovative discovery platforms for expanding E3 ligase repertoires that may enable more rational degrader design. Finally, we highlight the current landscape of clinical trials and discuss opportunities and remaining hurdles for advancing TPD toward clinical translation.

Indexed as

molecular gluesoncotherapyPROTACstargeted protein degradationubiquitin‒proteasome systemundruggable targets

Identifiers

PMID42414544

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.