Evidence map›Paper›PMID 30081536›Full record

ReviewPharmaceutics2018

Tailoring Formulations for Intranasal Nose-to-Brain Delivery: A Review on Architecture, Physico-Chemical Characteristics and Mucociliary Clearance of the Nasal Olfactory Mucosa.

Stella Gänger, Katharina Schindowski

Open access · goldAbstract readReview
In one paragraph

Review in Pharmaceutics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 180 papers.

0numbers the graph read from it
0cells of the map it votes in
180citing papers in PubMed
13.6field-weighted citation impact, top 1% 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

180 citing papers in PubMed, 406 citations in OpenAlex.

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120 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

2 authors at 2 institutions in 1 country.

Stella GängerInstitute of Applied Biotechnology, University of Applied Sciences Biberach, Hubertus-Liebrecht-Strasse 35, 88400 Biberach, Germany. stella.gaenger@gmx.de.
Katharina SchindowskiInstitute of Applied Biotechnology, University of Applied Sciences Biberach, Hubertus-Liebrecht-Strasse 35, 88400 Biberach, Germany. schindowski@hochschule-bc.de.ORCID 0000-0003-2514-1654
Universität Ulm · DEUniversity of Applied Sciences Biberach · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The blood-brain barrier and the blood-cerebrospinal fluid barrier are major obstacles in central nervous system (CNS) drug delivery, since they block most molecules from entering the brain. Alternative drug delivery routes like intraparenchymal or intrathecal are invasive methods with a remaining risk of infections. In contrast, nose-to-brain delivery is a minimally invasive drug administration pathway, which bypasses the blood-brain barrier as the drug is directed from the nasal cavity to the brain. In particular, the skull base located at the roof of the nasal cavity is in close vicinity to the CNS. This area is covered with olfactory mucosa. To design and tailor suitable formulations for nose-to-brain drug delivery, the architecture, structure and physico-chemical characteristics of the mucosa are important criteria. Hence, here we review the state-of-the-art knowledge about the characteristics of the nasal and, in particular, the olfactory mucosa needed for a rational design of intranasal formulations and dosage forms. Also, the information is suitable for the development of systemic or local intranasal drug delivery as well as for intranasal vaccinations.

Indexed as

biopharmaceuticalsCNS drug deliverydosage formmedical deviceNALTnanoparticlesolfactory epitheliumrespiratory epitheliumsemisolid

Identifiers

PMID30081536
PMCPMC6161189
OpenAlexW2887204204

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.