Biphasic dissolution combined with modified cylinder method—A new promising method for dissolution test in drug-loaded nanoemulsions
Bibliographic record
Abstract
Dissolution testing is important in assessing the in vitro drug release performance for oral administration dosage forms. However, currently, a simple and efficient in vitro test to investigate critical factors that may impact the drug release and bioavailability at the development stage of a drug-loaded nanoemulsion (NE) is lacking. Thus, in this study, we developed a new combined biphasic and modified cylinder (BP + MC) method to evaluate the dissolution profile of NEs. Flubendazole (FLZ), a Biopharmaceutical Classification System (BCS) Class II drug, offers a new prospective for drug repositioning for treating lung cancer and cryptococcal meningitis. We compared the drug release profiles of three different FLZ formulations (micronized as a suspension, loaded in NE, and solubilized in oil) by using three different methods (dialysis bag, modified cylinder method, and a new BP + MC method). The results showed potential higher drug release of FLZ from the suspension compared to FLZ-loaded NE at pH 1.2, and higher drug release from FLZ-loaded NE compared to other forms in octanol phase. These results correlate well with the in vivo test performed in mice carried out in our previous works. Furthermore, the partition mechanism of the drug released from the NE is discussed in-depth in this article, as well as the advantage of drug-loaded NEs over other preparations in creating supersaturable conditions. Based on the results, we provide new insights into how dissolution methods for a poorly water-solubility drug can be designed. Therefore, we present this new combined BP + MC method as a potential new discriminative dissolution test for future studies when developing drug-loaded NE and comparing with other dosage forms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".