Evidence map›Paper›PMID 40830098›Full record

ArticleCell death discovery2025

Discovery of a novel potent tubulin inhibitor through virtual screening and target validation for cancer chemotherapy.

Peipei Shan, Kai-Lu Liu, Xiu Jiang, Guangzhao Zhou, Kongkai Zhu, Hua Zhang

Abstract read
In one paragraph

Article in Cell death discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

6 authors.

Peipei Shan *Institute of Translational Medicine, the Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China. shanpeipei@qdu.edu.cn.ORCID http://orcid.org/0000-0002-1615-4640
Kai-Lu Liu *School of Biological Science and Technology, University of Jinan, Jinan, China.
Xiu JiangQingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, China.
Guangzhao ZhouDepartment of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Kongkai ZhuSchool of Biological Science and Technology, University of Jinan, Jinan, China. hkhhh.k@163.com.
Hua ZhangInstitute of Translational Medicine, the Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China. bio_zhangh@ujn.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82073729
6 · The paper itself

Abstract

Microtubules, critical to diverse cellular processes, represent a clinically validated target for anticancer therapeutics. In this study, a virtual screening of the Specs library, consisting of 200,340 compounds, was conducted to target the taxane and colchicine binding sites on tubulin, resulting in the identification of 93 promising candidates for further analysis. Subsequent characterization revealed a nicotinic acid derivative (compound 89) as a potent tubulin inhibitor, demonstrating significant anti-tumor efficacy in vitro and in vivo, with no observable toxicity at therapeutic doses in mice. Notably, compound 89 also exhibited robust antitumor activity in patient-derived organoids. Mechanistic studies, including EBI competitive binding assays and molecular docking, confirmed its inhibition toward tubulin polymerization via selective binding to the colchicine site. Furthermore, compound 89 disrupted tubulin assembly dynamics through modulation of the PI3K/Akt signaling pathway. This work presents a novel tubulin-inhibiting scaffold with potential for advancing next-generation microtubule-targeted chemotherapies.

Identifiers

PMID40830098
PMCPMC12365163

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Registered trials

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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.