Evidence map›Paper›PMID 33540922›Full record

ReviewPolymers2021

Micellar Drug Delivery Systems Based on Natural Biopolymers.

Leonard Ionut Atanase

Abstract readReview
In one paragraph

Review in Polymers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Bridging bioactive metabolites ofFrontiers in pharmacology · 2026
    Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Review
  17. Review
  18. Review
  19. Article
  20. Article

10 more citing papers are in PubMed but not listed here.

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

1 author.

Leonard Ionut AtanaseDepartment of Biomaterials, Faculty of Medical Dentistry, "Apollonia" University of Iasi, Pacurari Street, No. 11, 700511 Iasi, Romania.ORCID 0000-0002-3657-1492

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The broad diversity of structures and the presence of numerous functional groups available for chemical modifications represent an enormous advantage for the development of safe, non-toxic, and cost-effective micellar drug delivery systems (DDS) based on natural biopolymers, such as polysaccharides, proteins, and peptides. Different drug-loading methods are used for the preparation of these micellar systems, but it appeared that dialysis is generally recommended, as it avoids the formation of large micellar aggregates. Moreover, the preparation method has an important influence on micellar size, morphology, and drug loading efficiency. The small size allows the passive accumulation of these micellar systems via the permeability and retention effect. Natural biopolymer-based micellar DDS are high-value biomaterials characterized by good compatibility, biodegradability, long blood circulation time, non-toxicity, non-immunogenicity, and high drug loading, and they are biodegraded to non-toxic products that are easily assimilated by the human body. Even if some recent studies reported better antitumoral effects for the micellar DDS based on polysaccharides than for commercial formulations, their clinical use is not yet generalized. This review is focused on the studies from the last decade concerning the preparation as well as the colloidal and biological characterization of micellar DDS based on natural biopolymers.

Indexed as

drug delivery systemsmicellepeptidespolysaccharidesproteinsself-assembly

Identifiers

PMID33540922
PMCPMC7867356

What OpenQuestion 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 OpenQuestion graph.