Development of an Analytical Method for the Rapid Quantitation of Peptides Used in Microbicide Formulations
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
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".