Development of an Analytical Method for the Rapid Quantitation of Peptides Used in Microbicide Formulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, a growing number of macromolecules such as peptides and proteins have been formulated into various microbicide formulations for the prevention of sexually transmitted infections. However, a fast and reliable high-throughput method for quantitating peptide/protein in polymer-based microbicide formulations is still lacking. As a result, we developed and validated a reversed-phase high-performance liquid chromatography method for the quantitation of gp120 fragment and LL-37 simultaneously in various microbicide gel formulations. This method was capable of detecting a limit of linearity (regression coefficient of 0.999) for gp120 fragment and LL-37 within a range of 0.625–80 and 1.25–80 µg mL −1 , respectively. The lower limit of quantification for gp120 fragment and LL-37 was 1.14 and 0.31 µg mL −1 , respectively. Method validation demonstrated acceptable intra- and inter-day RSD % (<5 %) and accuracy (95.67–100.5 %). Formulating both peptides into polymeric pharmaceutical gel formulations showed high extraction efficiency (in the range of 95.90 ± 3.03 to 111.45 ± 2.51 %). Using this method, we were able to separate and identify the forced degraded products from both peptides simultaneously without affecting the quantitation of both peptides in the polymeric dosage forms. Furthermore, this method was able to detect and separate degradants that were unable to be revealed using gel eletrophoresis.
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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 | 0.001 | 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) | 0.000 | 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 it