Evidence map›Paper›PMID 35323794›Full record

ArticleMembranes2022

Versatile Encapsulation and Synthesis of Potent Liposomes by Thermal Equilibration.

Steven A Roberts, Chaebin Lee, Shrishti Singh, Nitin Agrawal

Open access · goldAbstract read
In one paragraph

Article in Membranes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.5field-weighted citation impact, top 20% of its field
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

14 citing papers in PubMed, 20 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Steven A RobertsDepartment of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA.
Chaebin LeeCenter for Cancer and Immunology Research, Children's National Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Shrishti SinghDepartment of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0001-5180-3936
Nitin AgrawalCenter for Cancer and Immunology Research, Children's National Research Institute, Children's National Hospital, Washington, DC 20010, USA.ORCID 0000-0003-3216-2349
Children's National · USGeorge Mason University · US

Funding

National Science Foundation 1645195National Science Foundation 2013952
6 · The paper itself

Abstract

The wide-scale use of liposomal delivery systems is challenged by difficulties in obtaining potent liposomal suspensions. Passive and active loading strategies have been proposed to formulate drug encapsulated liposomes but are limited by low efficiencies (passive) or high drug specificities (active). Here, we present an efficient and universal loading strategy for synthesizing therapeutic liposomes. Integrating a thermal equilibration technique with our unique liposome synthesis approach, co-loaded targeting nanovesicles can be engineered in a scalable manner with potencies 200-fold higher than typical passive encapsulation techniques. We demonstrate this capability through simultaneous co-loading of hydrophilic and hydrophobic small molecules and targeted delivery of liposomal Doxorubicin to metastatic breast cancer cell line MDA-MB-231. Molecular dynamic simulations are used to explain interactions between Doxorubicin and liposome membrane during thermal equilibration. By addressing the existing challenges, we have developed an unparalleled approach that will facilitate the formulation of novel theranostic and pharmaceutical strategies.

Indexed as

drug deliverylipid bilayerliposomemolecular dynamicsnanoparticlesvesicles

Identifiers

PMID35323794
PMCPMC8954264
OpenAlexW4220982579

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.