Evidence map›Paper›PMID 28080053›Full record

ReviewMolecular pharmaceutics2017

Lipid-Drug Conjugate for Enhancing Drug Delivery.

Danielle Irby, Chengan Du, Feng Li

Abstract readReview
In one paragraph

Review in Molecular pharmaceutics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing 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

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

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41 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

3 authors.

Danielle IrbyDepartment of Pharmaceutical Sciences, School of Pharmacy, Hampton University , Hampton, Virginia 23668, United States.
Chengan DuDepartment of Pharmaceutical Sciences, School of Pharmacy, Hampton University , Hampton, Virginia 23668, United States.
Feng LiDepartment of Pharmaceutical Sciences, School of Pharmacy, Hampton University , Hampton, Virginia 23668, United States.ORCID 0000-0002-5953-2592

Funding

Polymer-drug Conjugate Micelles for Treating Resistant Prostate CancersSC3GM109332 · NIGMS · HAMPTON UNIVERSITY · PI LI, FENG · 2014 to 2016
$333k
NIGMS NIH HHS SC3 GM109332
6 · The paper itself

Abstract

Lipid-drug conjugates (LDCs) are drug molecules that have been covalently modified with lipids. The conjugation of lipids to drug molecules increases lipophilicity and also changes other properties of drugs. The conjugates demonstrate several advantages including improved oral bioavailability, improved targeting to the lymphatic system, enhanced tumor targeting, and reduced toxicity. Based on the chemical nature of drugs and lipids, various conjugation strategies and chemical linkers can be utilized to synthesize LDCs. Linkers and/or conjugation methods determine how drugs are released from LDCs and are critical for the optimal performance of LDCs. In this review, different lipids used for preparing LDCs and various conjugation strategies are summarized. Although LDCs can be administered without a delivery carrier, most of them are loaded into appropriate delivery systems. The lipid moiety in the conjugates can significantly enhance drug loading into hydrophobic components of delivery carriers and thus generate formulations with high drug loading and superior stability. Different delivery carriers such as emulsions, liposomes, micelles, lipid nanoparticles, and polymer nanoparticles are also discussed in this review.

Indexed as

Drug CarriersDrug Delivery SystemsHydrophobic and Hydrophilic InteractionsLipidsPolymersProdrugsDrug CarriersLipidsPolymersProdrugschemical bondsconjugationdrug deliverylipidprodrug

Identifiers

PMID28080053
PMCPMC5477224

What OpenQuestion holds

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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.