Evidence map›Paper›PMID 41835183›Full record

ArticleResearch (Washington, D.C.)2026

Machine-Learning-Accelerated Design of Ternary Carrier-Free Nanomedicine for Intranasal Therapy of Brain Metastatic Non-small-cell Lung Cancer.

Changkun Peng, Gaozheng Li, Xinyue Yin, Annan Xu, Guiting You, Xiangxiang Cai, Mengru Quan, Junjie Zhang, Jie Zhou, Jingying Li and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research (Washington, D.C.), 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. Review
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

11 authors.

Changkun PengNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.ORCID https://orcid.org/0009-0003-4708
Gaozheng LiCollege of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, P. R. China.
Xinyue YinCollege of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, P. R. China.
Annan XuCollege of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, P. R. China.
Guiting YouNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.
Xiangxiang CaiCollege of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, P. R. China.
Mengru QuanNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.
Junjie ZhangNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.
Jie ZhouNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.ORCID https://orcid.org/0000-0002-6016-4424
Jingying LiNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.ORCID https://orcid.org/0000-0003-3600
Huanghao YangNew Cornerstone Science Laboratory, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350108, P. R. China.ORCID https://orcid.org/0000-0001-5894

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-small-cell lung cancer (NSCLC) with brain metastases poses formidable therapeutic challenges due to acquired resistance and the inherent pharmacokinetic defects of traditional delivery. We developed an innovative lipoic acid-based self-assembled nanodrug (dabrafenib, trametinib, and lipoic acid self-assembly [DTL]) system, whose rational design was guided by a novel machine learning platform to overcome high-cost, empirical screening bottlenecks. Multifunctional lipoic acid, serving as a universal self-assembling molecule, enabled DTL's robust assembly and enhanced penetration across mucosal and solid tumor barriers via its unique thiol-mediated exchange mechanism while simultaneously exerting distinct antitumor efficacy. Intranasal administration of DTL achieved efficient dual-targeted delivery to both primary NSCLC and established intracranial metastases. Furthermore, compared to conventional targeted combination therapies, DTL induced diverse, multimodal tumor cell death (apoptosis, pyroptosis, and ferroptosis) and profoundly remodeled the immune microenvironment. In vivo, DTL markedly inhibited tumor growth with reduced toxicity, offering a clinically translatable strategy for advanced NSCLC.

Identifiers

PMID41835183
PMCPMC12982895

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.