Low-Surfactant Microemulsions for Enhanced Topical Delivery of Poorly Soluble Drugs
Bibliographic record
Abstract
PURPOSE: To develop and characterize low-surfactant microemulsion (ME) gels to enhance topical delivery of poorly soluble drugs. METHOD: Five low surfactant ME formulations were manufactured following the construction of pseudo-ternary phase diagrams. The MEs were screened for their ability to dissolve a poorly soluble new chemical entity (Model Drug X). Various viscosity imparting agents like Carbopol 934, Colloidal Silica, HPMC K100M, Lubrajel NP, and Xanthan Gum were evaluated for the manufacture of these ME gels. Each ME gel was then further evaluated for physical stability, including assessing rheological profiles. In vitro release profiles were also determined and compared to a conventional ointment. RESULTS: Three of the five low surfactant MEs developed (ME1, ME4 and ME5) were capable of dissolving Model Drug X up to 14 fold higher than the conventional ointment formulation. ME1 and ME4 gels comprising Xanthan gum or Carbopol 934 were physically stable, while ME5 gel was stable only with Colloidal Silica. The ME5 gel with Colloidal Silica showed an irreversible increase in its elastic modulus when exposed to high temperature, indicating that the formulation would be less suitable for commercial use. The Xanthan Gum and Colloidal Silica gels yielded significantly higher release rates (8 - 10 fold) compared to a conventional ointment and formulations containing Carbopol 934. The significant difference in drug release rates between Xanthan Gum and Carbopol 934 indicated that choice of viscosity imparting agent played an important role in governing drug release from ME gels. CONCLUSION: ME gels were developed with low surfactant concentrations and improved formulation characteristics, which increased the solubility and subsequent release of a poorly soluble drug. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".