Buparvaquone Nanostructured Lipid Carrier: Development of an Affordable Delivery System for the Treatment of Leishmaniases
Bibliographic record
Abstract
Buparvaquone (BPQ), a veterinary drug, was formulated as nanostructured lipid carriers (NLC) for leishmaniases treatment. The formulation design addressed poor water solubility of BPQ and lack of human drug delivery system. The DSC/TG and microscopy methods were used for solid lipids screening. Softisan® 154 showed highest BPQ solubility in both methods. The BPQ solubility in liquid lipids using HPLC revealed Miglyol® 812 as the best option. Response surface methodology (RSM) was used to identify the optimal Softisan154 : Miglyol 812 ratios (7 : 10 to 2 : 1) and Kolliphor® P188 and Tween® 80 concentration (>3.0% w/w) aiming for z -average in the range of 100–300 nm for macrophage delivery. The NLC obtained by high-pressure homogenization showed low z -averages (<350 nm), polydispersity (<0.3), and encapsulation efficiency close to 100%. DSC/TG and microscopy in combination proved to be a powerful tool to select the solid lipid. The relationship among the variables, demonstrated by a linear mathematical model using RSM, allowed generating a design space. This design space showed the limits in which changes in the variables influenced the z -average. Therefore, these drug delivery systems have the potential to improve the availability of affordable medicines due to the low cost of raw materials, using well established, reliable, and feasible scale-up technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".