Reverse phase high-performance liquid chromatography for quantification of hydroxymethylnitrofurazone in polymeric nanoparticles
Bibliographic record
Abstract
Hydroxymethylnitrofurazone (NFOH) is a new compound with potential leishmanicidal and trypanocidal activity. Despite its effectiveness, the formulators have to overcome its poor aqueous solubility. Recently, polymeric nano-scale drug delivery systems have proposed for the treatment of neglected diseases. As several studies have confirmed the advantages of such formulations, and this approach provides new analytical challenges, including the need to detect trace amounts of the drug. A suitable method was developed and validated for NFOH determination bound to poly (n-butylcyanoacrylate) (PBCA) nanoparticles. The chromatographic separation was achieved using a C18 column maintained at 25 ºC and an isocratic mobile phase consisting of water and acetonitrile: 80:20 (v/v) at a flow rate of 1.2 mL min-1 and UV-detection at 265 nm. Investigated validation parameters included selectivity, linearity, accuracy, precision and robustness (changes in column temperature, mobile phase composition and flow). The method was specific, the peak of NFOH had no interference with any nanoparticle excipients and no co-elution with main degradation product (nitrofurazone). Linearity was over the range of 0.94 13.11 μg mL-1 (r2=0.999). The method was accurate and precise, recovery of 100.7%, RSD of 0.4%; intra-day and inter-day RSD range 9.98-9.99 μg mL-1 and 0.3% to 0.5%, respectively. Robustness confirmed that method could resist the applied changes. Application of the optimized method revealed an encapsulation efficiency of 64.4% (n=3). Therefore, the method was successfully developed and validated for the determination of the encapsulation efficiency of NFOH-PBCA nanoparticles.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".