Development and Validation of HPLC-MS/MS Method for Busereline Quantitation in Animal Blood Plasma
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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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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