Evidence map›Paper›PMID 38942802›Full record

ArticleScientific reports2024

Preparation of Erlotinib hydrochloride nanoparticles (anti-cancer drug) by RESS-C method and investigating the effective parameters.

Majid Bazaei, Bizhan Honarvar, Nadia Esfandiari, Seyed Ali Sajadian, Zahra Arab Aboosadi

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

5 authors.

Majid BazaeiDepartment of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
Bizhan HonarvarDepartment of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran. honarvar2@gmail.com.
Nadia EsfandiariDepartment of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
Seyed Ali SajadianDepartment of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran. seyedali.sajadian@gmail.com.
Zahra Arab AboosadiDepartment of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The size of the drug particles is one of the essential factors for the proper absorption of the drug compared to the dose of the drug. When particle size is decreased, drug uptake into the body increases. Recent studies have revealed that the rapid expansion of supercritical solution with cosolvent plays a significant role in preparing micron and submicron particles. This paper examines the preparation of Erlotinib hydrochloride nanoparticles using a supercritical solution through the cosolvent method for the first time. An examination of the parameters of temperature (318-338 K), pressures (15-25 MPa) and nozzle diameter (300-700 μm) was investigated by Box-Behnken design, and their respective effects on particle size revealed that the nozzle diameter has a more significant impact on particle size than the other parameters. The smallest particles were produced at temperature 338 K, pressure 20 MPa, and nozzle diameter 700 μm. Besides, the ERL nanoparticles were characterized using SEM, DLS, XRD, FTIR, and DSC analyses. Finally, the results showed that the average size of the ERL particles decreased from 31.6 μm to 200-1100 nm.

Indexed as

Antineoplastic AgentsErlotinib HydrochlorideNanoparticlesParticle SizeChromatography, Supercritical FluidDrug CompoundingPressureTemperatureAntineoplastic AgentsErlotinib HydrochlorideAnticancer drugErlotinib hydrochlorideParticle sizePharmaceutical particlesRESS-C method

Identifiers

PMID38942802
PMCPMC11213895

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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.