Multidimensional LC-MS/MS Enables Simultaneous Quantification of Intact Human Insulin and Five Recombinant Analogs in Human Plasma
Bibliographic record
Abstract
This work provides a multidimensional method for the simultaneous, direct quantification of intact human insulin and five insulin analogs in human plasma. This investigation solves both the selectivity and sensitivity problems encountered for accurate quantification of insulins in plasma since the former is not possible with conventional assays and the latter with conventional LC-MS/MS. The method uses a mixed-mode SPE and a multidimensional LC method including a solid-core particle column containing an anion exchange stationary phase. Matrix factors for all analogs were calculated in 6 sources of human plasma and CVs of the matrix factors were <15% in all cases supporting the selectivity of the method, while achieving LLOQs of 50-200 pg/mL (1.4-5.6 μIU/mL) for each insulin from 250 μL of human plasma. The average accuracy for the standard curve points in extracted human plasma was 99-100%. Average inter- and intraday accuracies for QC samples were 98% and 94%, respectively. Average inter- and intraday precisions for QC samples were 7.5 and 5.3%, respectively. Patient samples were analyzed in a blind study and results concurred with their diabetes multidosing regimes. The study also demonstrated that the presence of high levels of human insulin and bovine insulin does not interfere with quantification of any of the analyzed analogs. We propose this method for the accurate pharmacokinetic monitoring of diabetic patients, for sport antidoping and forensic toxicology analysis.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".