Evidence map›Paper›PMID 40207975›Full record

ArticleDrug delivery2025

Reliable high-PAP-1-loaded polymeric micelles for cancer therapy: preparation, characterization, and evaluation of anti-tumor efficacy.

Fang Ye, Qi Li, Longping Huang, Naikai Liao

Abstract read
In one paragraph

Article in Drug delivery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Fang YeSchool of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, P. R. China.
Qi LiSchool of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, P. R. China.
Longping HuangSchool of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, P. R. China.
Naikai LiaoDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mitochondrial potassium channel Kv1.3 is a critical therapeutic target, as its blockade induces cancer cell apoptosis, highlighting its therapeutic potential. PAP-1, a potent and selective membrane-permeant Kv1.3 inhibitor, faces solubility challenges affecting its bioavailability and antitumor efficacy. To circumvent these challenges, we developed a tumor-targeting drug delivery system by encapsulating PAP-1 within pH-responsive mPEG-PAE polymeric micelles. These self-assembled micelles exhibited high entrapment efficiency (91.35%) and drug loading level (8.30%). As pH decreased, the micelles exhibited a significant increase in particle size and zeta potential, accompanied by a surge in PAP-1 release. Molecular simulations revealed that PAE's tertiary amine protonation affected the self-assembly process, modifying hydrophobicity and resulting in larger, loosely packed particles. Furthermore, compared to free PAP-1 or PAP-1 combined with MDR inhibitors, PAP-1-loaded micelles significantly enhanced cytotoxicity and apoptosis induction in Jurkat and B16F10 cells, through mechanisms involving decreased mitochondrial membrane potential and elevated caspase-3 activity.

Indexed as

Antineoplastic AgentsPolymersAnimalsApoptosisCell Line, TumorDrug CarriersDrug Delivery SystemsDrug LiberationHumansHydrogen-Ion ConcentrationJurkat CellsMembrane Potential, MitochondrialMiceMice, Inbred C57BLMicellesParticle SizeAntineoplastic AgentsDrug CarriersMicellesPolyethylene GlycolsPolymersanti-tumorMitochondrial potassium channel Kv1.3PAP-1pH-responsivepolymeric micelles

Identifiers

PMID40207975
PMCPMC11986873

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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.