Evidence map›Paper›PMID 31072061›Full record

ReviewCancers2019

Active Targeting Strategies Using Biological Ligands for Nanoparticle Drug Delivery Systems.

Jihye Yoo, Changhee Park, Gawon Yi, Donghyun Lee, Heebeom Koo

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 231 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
231citing papers in PubMed, 1 pooled it
–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

231 citing papers in PubMed, 1 synthesis or guideline pooled it.

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171 more citing papers are in PubMed but not listed here.

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

5 authors.

Jihye YooDepartment of Medical Life Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea. yooji0498@catholic.ac.kr.
Changhee ParkDepartment of Medical Life Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea. parkch@catholic.ac.kr.
Gawon YiDepartment of Medical Life Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea. gawon4292@catholic.ac.kr.
Donghyun LeeDepartment of Medical Life Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea. a2168989@catholic.ac.kr.
Heebeom KooDepartment of Medical Life Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea. hbkoo@catholic.ac.kr.ORCID 0000-0002-2193-6583

Funding

National Research Foundation of Korea 2016R1C1B3013951
6 · The paper itself

Abstract

Targeting nanoparticle (NP) carriers to sites of disease is critical for their successful use as drug delivery systems. Along with optimization of physicochemical properties, researchers have focused on surface modification of NPs with biological ligands. Such ligands can bind specific receptors on the surface of target cells. Furthermore, biological ligands can facilitate uptake of modified NPs, which is referred to as 'active targeting' of NPs. In this review, we discuss recent applications of biological ligands including proteins, polysaccharides, aptamers, peptides, and small molecules for NP-mediated drug delivery. We prioritized studies that have demonstrated targeting in animals over in vitro studies. We expect that this review will assist biomedical researchers working with NPs for drug delivery and imaging.

Indexed as

active targetingbiodistributiondrug deliveryligandnanoparticletumor targeting

Identifiers

PMID31072061
PMCPMC6562917

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.