Evidence map›Paper›PMID 37241911›Full record

ReviewMolecules (Basel, Switzerland)2023

The Power of Field-Flow Fractionation in Characterization of Nanoparticles in Drug Delivery.

Juan Bian, Nemal Gobalasingham, Anatolii Purchel, Jessica Lin

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 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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  15. Drug Product Development of Lipid Nanoparticle-Based mRNA Therapeutics and Vaccines.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
    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.

Juan BianGenentech Research and Early Development, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
Nemal GobalasinghamWyatt Technology Corporation, 6330 Hollister Ave, Santa Barbara, CA 93117, USA.
Anatolii PurchelWyatt Technology Corporation, 6330 Hollister Ave, Santa Barbara, CA 93117, USA.
Jessica LinGenentech Research and Early Development, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Asymmetric-flow field-flow fractionation (AF4) is a gentle, flexible, and powerful separation technique that is widely utilized for fractionating nanometer-sized analytes, which extend to many emerging nanocarriers for drug delivery, including lipid-, virus-, and polymer-based nanoparticles. To ascertain quality attributes and suitability of these nanostructures as drug delivery systems, including particle size distributions, shape, morphology, composition, and stability, it is imperative that comprehensive analytical tools be used to characterize the native properties of these nanoparticles. The capacity for AF4 to be readily coupled to multiple online detectors (MD-AF4) or non-destructively fractionated and analyzed offline make this technique broadly compatible with a multitude of characterization strategies, which can provide insight on size, mass, shape, dispersity, and many other critical quality attributes. This review will critically investigate MD-AF4 reports for characterizing nanoparticles in drug delivery, especially those reported in the last 10-15 years that characterize multiple attributes simultaneously downstream from fractionation.

Indexed as

Fractionation, Field FlowNanoparticlesNanostructuresDrug Delivery SystemsParticle SizePolymersPolymersasymmetrical flow field-flow fractionationlight scattering detectionmulti-attribute characterizationnanoparticle drug delivery systems

Identifiers

PMID37241911
PMCPMC10224342

What OpenQuestion holds

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LicenceCC BY
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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.