Validation and application of a liquid chromatography‐tandem mass spectrometric method for the determination of GDC‐0152 in human plasma using solid‐phase extraction
Bibliographic record
Abstract
A liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was developed and validated for the determination of GDC-0152 in human plasma to support clinical development. The method consisted of a solid-phase extraction for sample preparation and LC-MS/MS analysis in the positive ion mode using TurboIonSpray(TM) for analysis. d(7) -GDC-0152 was used as the internal standard. A linear regression (weighted 1/concentration(2) ) was used to fit calibration curves over the concentration range of 0.02-10.0 ng/mL for GDC-0152. There were no endogenous interference components in the blank human plasma tested. The accuracy at the lower limit of quantitation was 99.3% with a precision (%CV) of 13.9%. For quality control samples at 0.0600, 2.00 and 8.00 ng/mL, the between-run %CV was ≤8.64. Between-run percentage accuracy ranged from 98.2 to 99.6%. GDC-0152 was stable in human plasma for 363 days at -20°C and for 659 days at -70°C storage. GDC-0152 was stable in human plasma at room temperature for up to 25 h and through three freeze-thaw cycles. In whole blood, GDC-0152 was stable for 12 h at 4°C and at ambient temperature. This validated LC-MS/MS method for determination of GDC-0152 was used to support clinical 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 | 0.004 | 0.004 |
| 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 | 0.001 |
| 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".