Evidence map›Paper›PMID 35214000›Full record

ReviewPharmaceutics2022

Extracellular Vesicle-Based Hybrid Systems for Advanced Drug Delivery.

Diego A Rodríguez, Pieter Vader

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. Review
  20. 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

2 authors.

Diego A RodríguezCDL Research, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
Pieter VaderCDL Research, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.

Funding

European Research Council 851936
6 · The paper itself

Abstract

The continuous technological advancement of nanomedicine has enabled the development of novel vehicles for the effective delivery of therapeutic substances. Synthetic drug delivery systems are nano-sized carriers made from various materials that can be designed to deliver therapeutic cargoes to cells or tissues. However, rapid clearance by the immune system and the poor targeting profile of synthetic drug delivery systems are examples of the pressing obstacles faced in nanomedicine, which have directed the field toward the development of alternative strategies. Extracellular vesicles (EVs) are nanoscale particles enclosed by a protein-rich lipid bilayer; they are released by cells and are considered to be important mediators of intercellular communication. Owing to their natural composition, EVs have been suggested to exhibit good biocompatibility and to possess homing properties to specific cell types. Combining EVs with synthetic nanoparticles by defined hybridization steps gives rise to a novel potential drug delivery tool, i.e., EV-based hybrid systems. These novel therapeutic vehicles exhibit potential advantageous features as compared to synthetic drug delivery systems such as enhanced cellular uptake and cargo delivery, immuno-evasive properties, capability of crossing biological barriers, and tissue targeting profile. Here, we provide an overview of the various strategies practiced to produce EV-based hybrid systems and elucidate those advantageous features obtained by synthetic drug delivery systems upon hybridization with EVs.

Indexed as

drug deliveryextracellular vesicleshybridssynthetic nanoparticles

Identifiers

PMID35214000
PMCPMC8878585

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.