Evidence map›Paper›PMID 32290458›Full record

ArticleMaterials (Basel, Switzerland)2020

Tunable Release of Curcumin with an In Silico-Supported Approach from Mixtures of Highly Porous PLGA Microparticles.

Concetta Di Natale, Valentina Onesto, Elena Lagreca, Raffaele Vecchione, Paolo Antonio Netti

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
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  10. Phthalate Esters Metabolic StrainMicroorganisms · 2022
    Article
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  12. Article
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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.

Concetta Di NataleCenter for Advanced Biomaterials for Health Care (CABHC), IstitutoItaliano di Tecnologia, Largo Barsanti Matteucci 53, 80125 Napoli, Italy.
Valentina OnestoCenter for Advanced Biomaterials for Health Care (CABHC), IstitutoItaliano di Tecnologia, Largo Barsanti Matteucci 53, 80125 Napoli, Italy.ORCID 0000-0002-2308-4126
Elena LagrecaCenter for Advanced Biomaterials for Health Care (CABHC), IstitutoItaliano di Tecnologia, Largo Barsanti Matteucci 53, 80125 Napoli, Italy.ORCID 0000-0003-0500-8610
Raffaele VecchioneCenter for Advanced Biomaterials for Health Care (CABHC), IstitutoItaliano di Tecnologia, Largo Barsanti Matteucci 53, 80125 Napoli, Italy.ORCID 0000-0002-8831-7891
Paolo Antonio NettiCenter for Advanced Biomaterials for Health Care (CABHC), IstitutoItaliano di Tecnologia, Largo Barsanti Matteucci 53, 80125 Napoli, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, drug delivery systems have become some of the main topics within the biomedical field. In this scenario, polymeric microparticles (MPs) are often used as carriers to improve drug stability and drug pharmacokinetics in agreement with this kind of treatment. To avoid a mere and time-consuming empirical approach for the optimization of the pharmacokinetics of an MP-based formulation, here, we propose a simple predictive in silico-supported approach. As an example, in this study, we report the ability to predict and tune the release of curcumin (CUR), used as a model drug, from a designed combination of different poly(d,l-lactide-co-glycolide) (PLGA) MPs kinds. In detail, all CUR-PLGA MPs were synthesized by double emulsion technique and their chemical-physical properties were characterized by Mastersizer and scanning electron microscopy (SEM). Moreover, for all the MPs, CUR encapsulation efficiency and kinetic release were investigated through the UV-vis spectroscopy. This approach, based on the combination of in silico and experimental methods, could be a promising platform in several biomedical applications such as vaccinations, cancer-treatment, diabetes therapy and so on.

Indexed as

curcumindrug deliveryfirst-order equationin silicoPLGAMPsrelease model

Identifiers

PMID32290458
PMCPMC7215757

What OpenQuestion holds

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LicenceCC BY
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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.