Evidence map›Paper›PMID 35745796›Full record

ArticlePharmaceutics2022

Preparation of Drug-Loaded Liposomes with Multi-Inlet Vortex Mixers.

Huangliang Zheng, Hai Tao, Jinzhao Wan, Kei Yan Lee, Zhanying Zheng, Sharon Shui Yee Leung

Abstract read
In one paragraph

Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

6 authors.

Huangliang ZhengSchool of Pharmacy, The Chinese University of Hong Kong, Shatin, Hong Kong.
Hai TaoCenter for Turbulence Control, Harbin Institute of Technology, Shenzhen 518055, China.
Jinzhao WanCenter for Turbulence Control, Harbin Institute of Technology, Shenzhen 518055, China.
Kei Yan LeeSchool of Pharmacy, The Chinese University of Hong Kong, Shatin, Hong Kong.
Zhanying ZhengCenter for Turbulence Control, Harbin Institute of Technology, Shenzhen 518055, China.ORCID 0000-0001-5686-7197
Sharon Shui Yee LeungSchool of Pharmacy, The Chinese University of Hong Kong, Shatin, Hong Kong.ORCID 0000-0002-0194-7058

Funding

University Grants Committee 24300619
6 · The paper itself

Abstract

The multi-inlet vortex mixer (MIVM) has emerged as a novel bottom-up technology for solid nanoparticle preparation. However, its performance in liposome preparation remains unknown. Here, two key process parameters (aqueous/organic flow rate ratio (FRR) and total flow rate (TFR)) of MIVM were investigated for liposome preparation. For this study, two model drugs (lysozyme and erythromycin) were chosen for liposome encapsulation as the representative hydrophilic and hydrophobic drugs, respectively. In addition, two modified MIVMs, one with herringbone-patterned straight inlets and one with zigzag inlets, were designed to further improve the mixing efficiency, aiming to achieve better drug encapsulation. Data showed that FRR played an important role in liposome size control, and a size of <200 nm was achieved by FRR higher than 3:1. Moreover, increasing TFR (from 1 to 100 mL/min) could further decrease the size at a given FRR. However, similar regularities in controlling the encapsulation efficiency (EE%) were only noted in erythromycin-loaded liposomes. Modified MIVMs improved the EE% of lysozyme-loaded liposomes by 2~3 times at TFR = 40 mL/min and FRR = 3:1, which was consistent with computational fluid dynamics simulations. In summary, the good performance of MIVM in the control of particle size and EE% makes it a promising tool for liposome preparation, especially for hydrophobic drug loading, at flexible production scales.

Indexed as

computational fluid dynamicsliposomesmicromixingMIVMvortex mixing

Identifiers

PMID35745796
PMCPMC9227628

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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.