Validation and application of a liquid chromatography–tandem mass spectrometric method for the determination of GDC‐0834 and its metabolite in human plasma using semi‐automated 96‐well protein precipitation
Bibliographic record
Abstract
A liquid chromatographic-tandem mass spectrometric (LC-MS/MS) method was developed and validated for the determination of GDC-0834 and its amide hydrolysis metabolite (M1) in human plasma to support clinical development. The method consisted of semi-automated 96-well protein precipitation extraction for sample preparation and LC-MS/MS analysis in positive ion mode using TurboIonSpray® for analysis. D6-GDC-0834 and D6-M1 metabolite were used as internal standards. A linear regression (weighted 1/concentration(2) ) was used to fit calibration curves over the concentration range of 1 - 500 ng/mL for both GDC-0834 and M1 metabolite. The accuracy (percentage bias) at the lower limit of quantitation (LLOQ) was 5.20 and 0.100% for GDC-0834 and M1 metabolite, respectively. The precision (CV) for samples at the LLOQ was 3.13-8.84 and 5.20-8.93% for GDC-0834 and M1 metabolite, respectively. For quality control samples at 3, 200 and 400 ng/mL, the between-run CV was ≤ 7.38% for GDC-0834 and ≤ 8.20% for M1 metabolite. Between run percentage bias ranged from -2.76 to 6.98% for GDC-0834 and from -6.73 to 2.21% for M1 metabolite. GDC-0834 and M1 metabolite were stable in human plasma for 31 days at -20 and -70°C. This method was successfully applied to support a GDC-0834 human pharmacokinetic-based study.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 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".