Evidence map›Paper›PMID 36594020›Full record

ReviewiScience2023

Covalently triggered self-assembly of peptide-based nanodrugs for cancer theranostics.

Yamei Liu, Ruirui Xing, Junbai Li, Xuehai Yan

Abstract readReview
In one paragraph

Review in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. 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.

Yamei LiuState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
Ruirui XingState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
Junbai LiBeijing National Laboratory for Molecular Sciences, CAS Key Lab of Colloid, Interface and Chemical Thermodynamics, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
Xuehai YanState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Covalently triggered peptide self-assembly is achieved through sequential integration of spontaneous covalent reaction and noncovalent interactions, thus both enhancing the physiological stability and extending unexpected functionality of the resulting peptide-based assemblies, different from popular supramolecular peptide self-assembly merely associated with noncovalent interactions. This review summarizes the recent progress on the development of covalently triggered peptide self-assembly for cancer theranostics. Especially, we propose the fundamental design principle of covalently triggered peptide self-assembly for constructing a variety of peptide-based assemblies including nanoparticles, nanofibers, hollow nanospheres, and other nanoarchitectures. Subsequently, the discussion is anchored in an overview of representative covalently assembled peptide-based nanodrugs for the cancer theranostics. Finally, the challenges and perspectives on the clinical potential of the covalently assembled peptide-based nanodrugs are highlighted. This review will provide new insights into construction of peptide-based nanodrugs through combination of covalent reaction and noncovalent self-assembly and prompt their clinical applications in cancer diagnosis and therapeutics.

Indexed as

CancerDrugsMolecular self-assemblyNanotechnology

Identifiers

PMID36594020
PMCPMC9804138

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.