Evidence map›Paper›PMID 31171738›Full record

ReviewIET nanobiotechnology2019

Brain targeted delivery of anticancer drugs: prospective approach using solid lipid nanoparticles.

Anupriya Anand, Abimanyu Sugumaran, Damodharan Narayanasamy

Abstract readReview
In one paragraph

Review in IET nanobiotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

3 authors.

Anupriya AnandDepartment of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.
Abimanyu SugumaranDepartment of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India. abipharmastar@gmail.com.
Damodharan NarayanasamyDepartment of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A brain tumour is amongst most devastating and challenging condition to overcome with suitable treatment as the drug has to cross the blood-brain barrier (BBB) with several physiological barriers like opsonisation by the reticuloendothelial system. Presently various techniques such as surgical, chemotherapeutic agents, and radiotherapy techniques have performed to extend the lifespan of patients diagnosed with glioblastoma, which did not maximise the overall survival of patients with a tumour. Nanotechnology is relied upon to diminish the requirement for intrusive methods for conveyance of therapeutics to the central nervous system. Colloidal nanocarriers sizing range 1-1000 nm have been utilised to cross BBB delivers the drug at cell levels with enhanced bioavailability and reduced toxicity. However, solid lipid nanoparticles (SLNs) are considered a highly flexible carrier for more successful remedially in brain tumour. The treatment of a brain tumour via SLNs is gaining greater potency due to its inimitable size and lipidic nature. This review focuses and represents the current strategies of SLNs in the brain tumour treatment with appropriate techniques adopted are highlighted. Based on this review, the authors concluded that SLNs embrace exclusive promising lipidic nanocarrier that could be utilised to target a brain tumour effectively.

Indexed as

Antineoplastic AgentsDrug Delivery SystemsLipidsNanoparticlesAnimalsBrainBrain NeoplasmsHumansLiposomesMiceMicellesNanomedicineAntineoplastic AgentsLipidsLiposomesMicelles

Identifiers

PMID31171738
PMCPMC8676006

What OpenQuestion holds

Textmetadata
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.