Direct quantification of intact FIM in monkey plasma using a selective chromatography–tandem mass spectrometry method: Application in a pharmacokinetic study
Bibliographic record
Abstract
Abstract FIM protein, which consists of 155 amino acids, was developed as a novel GLP‐1 analog to reduce blood glucose, and pharmacodynamic results showed that it had a certain effect when used in treating Alzheimer's disease. The molecular weight of FIM is 16,304 Da. In theory, the concentration of FIM in biological samples should be determined by the ligand binding assay method or indirectly quantified using LC–MS/MS instrumentation. However, the above methods are complex and time‐consuming. In this study, we successfully developed a simpler LC–MS/MS method for directly quantifying the intact FIM protein in monkey plasma for the first time. The chromatographic separation of FIM was achieved using an InertSustain Bio C 18 column with a mobile phase of acetonitrile containing 0.1% formic acid (A)–water containing 0.1% formic acid (B) at a flow rate of 0.3 ml/min. Good linearity was observed in the concentration range of 5–500 ng/ml ( r 2 > 0.99). The intra‐ and inter‐day precisions (expressed as relative standard deviation, RSD) of FIM were 2.30–12.8 and 7.30–13.2%, respectively. The intra‐ and inter‐day accuracies (expressed as a relative error, RE) were −12.7–6.55 and − 10.1–0.892%, respectively. This method was successfully applied for a pharmacokinetic study of the FIM protein in four monkeys after subcutaneous administration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".