Evidence map›Paper›PMID 36043758›Full record

ReviewPharmaceutical nanotechnology2022

Functional Excipients and Novel Drug Delivery Scenario in Self-nanoemulsifying Drug Delivery System: A Critical Note.

Russel Tonjan, Dilpreet Singh

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmaceutical nanotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 59% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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 1 institution in 1 country.

Russel TonjanDepartment of Pharmaceutics, ISF College of Pharmacy, GT Road (NH-95), Ghal Kalan, Moga, Punjab 142001, India.
Dilpreet SinghDepartment of Pharmaceutics, ISF College of Pharmacy, GT Road (NH-95), Ghal Kalan, Moga, Punjab 142001, India.
Friendship College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lipid-based formulations have emerged as prospective dosage forms for extracting the therapeutic effects of existing lipophilic compounds and novel chemical entities more efficiently. Compared to other excipients, lipids have the added benefit of enhancing the bioavailability of lipophilic and highly metabolizable drugs due to their unique physicochemical features and similarities to in vivo components. Furthermore, lipids can minimize the needed dose and even the toxicity of drugs with poor aqueous solubility when employed as the primary excipient. Hence, the aim of the present review is to highlight the functional behavior of lipid excipients used in SNEDD formulation along with the stability aspects of the formulation in vivo. Moreover, this review also covered the importance of SNEDDS in drug delivery, the therapeutic and manufacturing benefits of lipids as excipients, and the technological advances made so far to convert liquid to solid SNEDDS like melt granulation, adsorption on a solid support, spray cooling, melt extrusion/ spheronization has also highlighted. The mechanistic understanding of SNEDD absorption in vivo is highly complex, which was discussed very critically in this review. An emphasis on their application and success on an industrial scale was presented, as supported by case studies and patent surveys.

Indexed as

Drug Delivery SystemsExcipientsLipidsNanoparticle Drug Delivery SystemNanoparticlesAnimalsBiological AvailabilityEmulsionsHumansSolubilityEmulsionsExcipientsLipidsNanoparticle Drug Delivery Systemabsorptionexcipientlipidlipophilicphysicochemical.poor solubilitySNEDDS

Identifiers

PMID36043758
OpenAlexW4293545353

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.