Development and validation of a sensitive liquid‐chromatography tandem mass spectrometry assay for mycophenolic acid and metabolites in HepaRG cell culture: Characterization of metabolism interactions between <i>p</i>‐cresol and mycophenolic acid
Bibliographic record
Abstract
Abstract Mycophenolic acid (MPA), a frequently used immunosuppressant, exhibits large inter‐patient pharmacokinetic variability. This study (a) developed and validated a sensitive liquid chromatography–tandem mass spectrometry (LC–MS/MS) assay for MPA and metabolites [MPA glucuronide (MPAG) and acyl‐glucuronide (AcMPAG)] in the culture medium of HepaRG cells; and (b) characterized the metabolism interaction between MPA and p‐cresol (a common uremic toxin) in this in vitro model as a potential mechanism of pharmacokinetic variability. Chromatographic separation was achieved with a C18 column (4.6 × 250 mm,5 μm) using a gradient elution with water and methanol (with 0.1% formic acid and 2 mm ammonium acetate). A dual ion source ionization mode with positive multiple reaction monitoring was utilized. Multiple reaction monitoring mass transitions (m/z) were: MPA (320.95 → 207.05), MPAG (514.10 → 303.20) and AcMPAG (514.10 → 207.05). MPA‐d3 (323.95 → 210.15) and MPAG‐d3 (517.00 → 306.10) were utilized as internal standards. The calibration curves were linear from 0.00467 to 3.2 μg/mL for MPA/MPAG and from 0.00467 to 0.1 μg/mL for AcMPAG. The assay was validated based on industry standards. p‐Cresol inhibited MPA glucuronidation (IC50 ≈ 55 μm) and increased MPA concentration (up to >2‐fold) at physiologically relevant substrate‐inhibitor concentrations (n = 3). Our findings suggested that fluctuations in p‐cresol concentrations might be in part responsible for the large pharmacokinetic variability observed for MPA in the clinic.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".