Evidence map›Paper›PMID 41161489›Full record

ArticleJournal of advanced research2026

Development of a selectively AURKB targeting peptide degradation drug with artificial intelligence-assisted design for the treatment of acute lymphoblastic leukemia.

Yun Xie, Hui Feng, Yinong Huang, Runyu Yang, Hong Lei, Wei He, Bingyu Yang, Fan Niu, Bohan Ma

Abstract read
In one paragraph

Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Supramolecular Degraders: An Emerging Paradigm in Targeted Protein Degradation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

9 authors.

Yun XieMedical Laboratory, Northwest Women's and Children's Hospital, Xi'an, China.
Hui FengThe Department of Hematology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yinong HuangShaanxi Institute of Pediatric Diseases, Xi'an Children's Hospital, Xi'an, China. Electronic address: ynhuang@xjtu.edu.cn.
Runyu YangThe Department of Hematology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Hong LeiShaanxi Institute of Pediatric Diseases, Xi'an Children's Hospital, Xi'an, China.
Wei HeShaanxi Institute of Pediatric Diseases, Xi'an Children's Hospital, Xi'an, China.
Bingyu YangThe Department of Hematology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Fan NiuThe Department of Hematology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. Electronic address: niufan@xjtufh.edu.cn.
Bohan MaThe department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, China. Electronic address: bohanma@xjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAcute lymphoblastic leukemia (ALL) is a highly heterogeneous hematologic malignancy with poor prognosis in refractory and relapsed cases. Aurora kinase B (AURKB), a member of the Aurora kinase family, is markedly overexpressed in ALL patients with cytogenetic abnormalities and is associated with unfavorable clinical outcomes. Targeting AURKB represents a promising therapeutic approach to address this unmet clinical need.

objectivesThis study aimed to develop and validate a novel, selective degradation strategy against AURKB in ALL, utilizing artificial intelligence-assisted drug design to create a peptide-based degrader. The goal was to demonstrate the efficacy of this degrader in reducing AURKB expression and suppressing leukemic cell growth.

methodsWe performed database analyses and confirmed AURKB overexpression in patient-derived ALL samples. Using AI-assisted design, we developed ATPD (AURKB Targeting Peptide Degrader), a selective peptide-based degrader of AURKB. The activity of ATPD was evaluated through in vitro cell proliferation assays, in vivo leukemia models, and ex vivo cytotoxicity tests using primary ALL cells.

resultsATPD effectively induced the degradation of AURKB and significantly inhibited the proliferation of ALL cells both in vitro and in vivo. Furthermore, ATPD exhibited potent cytotoxic activity against primary leukemic cells derived from ALL patients, B-All PDX, and mini-PDX models.

conclusionOur findings demonstrate that ATPD is the first AI-designed, selective AURKB degrader with potent anti-leukemic activity. This study highlights ATPD's potential as a novel, precise therapeutic strategy for the treatment of ALL, addressing a critical gap in managing refractory and relapsed disease.

Indexed as

Antineoplastic AgentsArtificial IntelligenceAurora Kinase BPeptidesPrecursor Cell Lymphoblastic Leukemia-LymphomaProtein Kinase InhibitorsAnimalsCell Line, TumorCell ProliferationDrug DesignFemaleHumansMaleMiceXenograft Model Antitumor AssaysAntineoplastic AgentsAURKB protein, humanAurora Kinase BPeptidesProtein Kinase InhibitorsAcute lymphoblastic leukemiaArtificial intelligenceAURKBDegradation drugPeptide drug

Identifiers

PMID41161489
PMCPMC13316543

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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.