Evidence map›Paper›PMID 38505779›Full record

ReviewBiophysics reviews2023

Microfluidic approaches for producing lipid-based nanoparticles for drug delivery applications.

Caterina Piunti, Elisa Cimetta

Abstract readReview
In one paragraph

Review in Biophysics reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The importance of drug delivery for disease treatment is supported by a vast literature and increasing ongoing clinical studies. Several categories of nano-based drug delivery systems have been considered in recent years, among which lipid-based nanomedicines, both artificial and cell-derived, remain the most approved. The best artificial systems in terms of biocompatibility and low toxicity are liposomes, as they are composed of phospholipids and cholesterol, the main components of cell membranes. Extracellular vesicles-biological nanoparticles released from cells-while resembling liposomes in size, shape, and structure, have a more complex composition with up to hundreds of different types of lipids, proteins, and carbohydrates in their membranes, as well as an internal cargo. Although nanoparticle technologies have revolutionized drug delivery by enabling passive and active targeting, increased stability, improved solubilization capacity, and reduced dose and adverse effects, the clinical translation remains challenging due to manufacturing limitations such as laborious and time-consuming procedures and high batch-to-batch variability. A sea change occurred when microfluidic strategies were employed, offering advantages in terms of precise particle handling, simplified workflows, higher sensitivity and specificity, and good reproducibility and stability over bulk methods. This review examines scientific advances in the microfluidics-mediated production of lipid-based nanoparticles for therapeutic applications. We will discuss the preparation of liposomes using both hydrodynamic focusing of microfluidic flow and mixing by herringbone and staggered baffle micromixers. Then, an overview on microfluidic approaches for producing extracellular vesicles and extracellular vesicles-mimetics for therapeutic applications will describe microfluidic extrusion, surface engineering, sonication, electroporation, nanoporation, and mixing. Finally, we will outline the challenges, opportunities, and future directions of microfluidic investigation of lipid-based nanoparticles in the clinic.

Identifiers

PMID38505779
PMCPMC10903496

What OpenQuestion holds

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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.