Development and Validation of HPLC-MS/MS Method for Busereline Quantitation in Animal Blood Plasma
Bibliographic record
Abstract
Introduction. Busereline, being a synthetic gonadotropin-releasing hormone analog, is widely used for hormone-dependent cancer treatment (e.g. prostate cancer and breast cancer). Based on the accumulated scientific data for busereline quantitation in biosamples, the main analytical method that is used for this purpose is high-performance liquid chromatography (HPLC) with fluorescence detection, combined with protein precipitation (TCA 10%) for sample preparation. However, due to several limitations of this method resulting in low sensitivity (at the µg/mL level of concentrations), the HPLC-MS/MS analytical method was chosen for peptide determination in biosamples. The HPLC-MS/MS method is considered to have higher accuracy and specificity. The main sample preparation method for gonadotropin-releasing hormone analogs is solid-phase extraction. In our work, we’ve chosen protein precipitation as an alternative – easier and less laborious biosamples preparation process. Aim. The main objective of this study was the development and validation of HPLC-MS/MS method for busereline quantitation in animal (mini pigs) plasma samples and its further application to pharmacokinetic studies. Materials and methods. Busereline quantitative determination in plasma samples was performed using HPLC-MS/MS method. A protein precipitation procedure (methanol, 1:2, v/v) was used for busereline extraction from pig plasma. Results and discussion. The developed analytical method was validated for selectivity, linearity, matrix effect, accuracy (intra-day, inter-day), precision (intra-day, inter-day), LLOQ, carryover and stability. Conclusion. A new HPLC-MS/MS method for busereline quantitation in blood plasma was developed and successfully validated. The developed method showed linearity over the quantitation range from 1 to 20 ng/mL. The developed method can be successfully applied to busereline pharmacokinetic studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".