Quantification of the bisphosphonate alendronate using capillary electrophoresis mass spectrometry with dynamic pH barrage junction focusing
Bibliographic record
Abstract
A quantitative method was developed for the direct identity confirmation and quantification of alendronate using CE-MS combined with a pH-assisted focusing technique, dynamic pH barrage junction focusing. A pH-induced variation in electrophoretic mobility led to online focusing of alendronate at the sample/pH barrage boundary, significantly improving the detection sensitivity. In addition, the use of a flow-through microvial CE electrospray interface and the multiple reaction monitoring mode of MS further improved the specificity and quantification capability of this technology. This quantitative method presented a wide linear dynamic range over 8-2000 ng/mL and an LOD of 2 ng/mL. A 460-fold improvement in sensitivity was obtained when pH barrage junction focusing was applied during the CE process, in comparison to when normal CE was conducted without online sample stacking. The superior detection sensitivity over previously reported methods enables direct analysis of bisphosphonate compounds, eliminating tedious pre-column sample enrichment and derivatization. Validation of alendronate content in a commercial drug tablet further proved the reliability and power of this method. This simple method with no sample derivatization, superior sensitivity, and short run time (<8 min) is a promising alternative for accurate quantification of alendronate and other types of bisphosphonate compounds in both drug formulations and plasma samples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".