Evidence map›Paper›PMID 36114372›Full record

ArticleAAPS PharmSciTech2022

Understanding the Effects of Associated Factors in the Development of Microsponge-Based Drug Delivery: a Statistical Quality by Design (QbD) Approach Towards Optimization.

Shibam Karmakar, Sourav Poddar, Jasmina Khanam

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Article in AAPS PharmSciTech, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. 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

3 authors.

Shibam KarmakarDepartment of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
Sourav PoddarDepartment of Chemical Engineering, National Institute of Technology, Tiruchirappalli, India. souravpoddarsxc@gmail.com.ORCID http://orcid.org/0000-0001-7154-5436
Jasmina KhanamDepartment of Pharmaceutical Technology, Jadavpur University, Kolkata, India. jasminakhanam@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The investigation aims to prepare, identify, analyse, and understand the effects of different variables and constant factors associated with developing drug-loaded microsponge formulation by altering variables using the experiment's design. A series of drug formulations were prepared by alteration of variables using the design of experiment (DoE). Shape factors were kept constant. Response surface methodology (RSM) was utilized to optimize the preparation and analyse factors and variables. The RSM and QbD make the process easy to scale up and reproducible to minimize batch-to-batch variation. Critical process attributes (CPAs), such as particle size, yield, and drug entrapment, were analysed and comprehended in the development process to estimate the risk of microsponge-based formulation and method stability. Critical process parameters (CPPs) were identified by trial and error. QbD-driven quasi-emulsion solvent evaporation method was adopted to emulsify the dispersed phase within a continuous aqueous phase to develop the microsponges. The investigation confirmed that the effects of changes in controlling factors were most prominent on response variables. Each response was found in a broad range in terms of average particle diameter (337-461 µm), entrapment efficiency (77.69-94.38%) of the drug, and product yield (76.91-96.38%). The optimum results are a yield of ~ 96%, an average particle size of 347 µm, and entrapment efficiency of ~ 93.55%. An analysis of ANOVA (P value < 0.05) and models were validated. A constant rate of drug release from the optimized product was observed for a period (8 h) with a drug release of 75%. The developed method is reproducible, stable, and reliable.

Indexed as

Drug Delivery SystemsDrug LiberationEmulsionsParticle SizeSolventsEmulsionsSolventsCPPs and CPAsmicroparticlemicrospongeoptimizationQbDResponse Surface Methodology (RSM)statistical tooling

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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.