Liquid Chromatography–Coupled Tandem Mass Spectrometry Based Assay to Evaluate Inosine-5′-monophosphate Dehydrogenase Activity in Peripheral Blood Mononuclear Cells from Stem Cell Transplant Recipients
Bibliographic record
Abstract
Combinations of immunosuppressive drugs are routinely used post-transplantation to prevent rejection and/or other complications and optimize outcomes. The prodrug ester mycophenolate mofetil (MMF) is frequently used in solid-organ and stem cell transplantation settings. A growing body of evidence supports therapeutic monitoring of this immunosuppressant to optimize its efficacy and reduce toxicity. Thus, pharmacodynamic monitoring of MMF is a strategy that could potentially improve patient outcomes. Pharmacodynamic measurements require evaluation of inosine-5'-monophosphate dehydrogenase (IMPDH) activity, the target enzyme of the active moiety mycophenolic acid. Various nonradioactive methods using chromatographic separations have been used to quantify xanthosine monophosphate, the catalytic product of the enzyme, to indirectly evaluate IMPDH activity. However, no methods have used mass spectrometry based detection, which provides more specificity and sensitivity. Here, we describe a liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) method for the quantification of xanthosine monophosphate and adenosine monophosphate (for normalization) in lysates of peripheral blood mononuclear cells (PBMCs) from hematopoietic stem cell transplant (HSCT) recipients. Linearity, precision, and accuracy were validated over a large range of concentrations for each compound. The method could measure analytes with high sensitivity, accuracy (93-116%), and reproducibility (CV < 7.5%). Its clinical application was validated in PBMC lysates obtained from healthy individuals (n = 43) and HSCT recipients (n = 19). This reliable and validated LC-MS/MS method could be a useful tool for pharmacodynamic monitoring of patients treated with MMF.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".