Evidence map›Paper›PMID 26317071›Full record

ArticleJournal of advanced pharmaceutical technology & research

Application of quality by design approach to optimize process and formulation parameters of rizatriptan loaded chitosan nanoparticles.

Ajinath Eknath Shirsat, Sohan S Chitlange

Abstract read
In one paragraph

Article in Journal of advanced pharmaceutical technology & research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. ZnO Nanoparticles ofMolecules (Basel, Switzerland) · 2022
    Article
  3. Investigating the Impact of OptimizedInternational journal of nanomedicine · 2022
    Article
  4. Article
  5. Article
  6. Review
  7. 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.

Ajinath Eknath ShirsatDepartment of Pharmacy, JJTU, Rajasthan, India.
Sohan S ChitlangeDepartment of Quality Assurance, Padm. Dr. D. Y. Patil Institute of Pharmaceutical Science and Research, Pimpri, Pune, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of present study was to optimize rizatriptan (RZT) chitosan (CS) nanoparticles using ionic gelation method by application of quality by design (QbD) approach. Based on risk assessment, effect of three variables, that is CS %, tripolyphosphate % and stirring speed were studied on critical quality attributes (CQAs); particle size and entrapment efficiency. Central composite design (CCD) was implemented for design of experimentation with 20 runs. RZT CS nanoparticles were characterized for particle size, polydispersity index, entrapment efficiency, in-vitro release study, differential scanning calorimetric, X-ray diffraction, scanning electron microscopy (SEM). Based on QbD approach, design space (DS) was optimized with a combination of selected variables with entrapment efficiency > 50% w/w and a particle size between 400 and 600 nm. Validation of model was performed with 3 representative formulations from DS for which standard error of - 0.70-3.29 was observed between experimental and predicted values. In-vitro drug release followed initial burst release 20.26 ± 2.34% in 3-4 h with sustained drug release of 98.43 ± 2.45% in 60 h. Lower magnitude of standard error for CQAs confirms the validation of selected CCD model for optimization of RZT CS nanoparticles. In-vitro drug release followed dual mechanism via, diffusion and polymer erosion. RZT CS nanoparticles were prepared successfully using QbD approach with the understanding of the high risk process and formulation parameters involved and optimized DS with a multifactorial combination of critical parameters to obtain predetermined RZT loaded CS nanoparticle specifications.

Indexed as

Central composite designchitosandesign spaceionic gelationquality by designrizatriptan

Identifiers

PMID26317071
PMCPMC4542404

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Registered trials

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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.