Potential Use of Microbial Surfactant in Microemulsion Drug Delivery System: A Systematic Review
Bibliographic record
Abstract
Mandana Ohadi,1 Arash Shahravan,2 Negar Dehghannoudeh,3 Touba Eslaminejad,1 Ibrahim M Banat,4 Gholamreza Dehghannoudeh1,5 1Pharmaceutics Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran; 2Endodontology Research Center, Kerman University of Medical Sciences, Kerman, Iran; 3Faculty of Arts and Science, University of Toronto, Toronto, Ontario, Canada; 4Faculty of Life & Health Sciences, University of Ulster, Coleraine BT52 1SA, N. Ireland, UK; 5Department of Pharmaceutics, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, IranCorrespondence: Gholamreza Dehghannoudeh Tel +98-34-31325015Fax +98-34-31325003Email ghr_dehghan@kmu.ac.irBackground: Microemulsions drug delivery systems (MDDS) have been known to increase the bioavailability of hydrophobic drugs. The main challenge of the MDDS is the development of an effective and safe system for drug carriage and delivery. Biosurfactants are preferred surface-active molecules because of their lower toxicity and safe characteristics when compared to synthetic surfactants. Glycolipid and lipopeptide are the most common biosurfactants that were tested for MDDS. The main goal of the present systematic review was to estimate the available evidence on the role of biosurfactant in the development of MDDS.Search Strategy: Literature searches involved the main scientific databases and were focused on the period from 2005 until 2017. The Search filter composed of two items: “Biosurfactant” and/or “Microemulsion.”Inclusion Criteria: Twenty-four studies evaluating the use of biosurfactant in MDDS were eligible for inclusion. Among these 14 were related to the use of glycolipid biosurfactants in the MDDS formulations, while four reported using lipopeptide biosurfactants and six other related review articles.Results: According to the output study parameters, biosurfactants acted as active stabilizers, hydrophilic or hydrophobic linkers and safety carriers in MDDS, and among them glycolipid biosurfactants had the most application in MDDS formulations.Conclusion: Synthetic surfactants could be replaced by biosurfactants as an effective bio-source for MDDS due to their excellent self-assembling and emulsifying activity properties.Keywords: microemulsion, drug delivery systems, biosurfactant, systematic review glycolipid, lipopeptide
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".