Liquid Chromatography–Tandem Mass Spectrometry Analysis of Erythrocyte Thiopurine Nucleotides and Effect of Thiopurine Methyltransferase Gene Variants on These Metabolites in Patients Receiving Azathioprine/6-Mercaptopurine Therapy
Bibliographic record
Abstract
BACKGROUND: Polymorphic thiopurine S-methyltransferase (TPMT) is a major determinant of thiopurine toxicity. METHODS: We extracted 6-thioguanine nucleotides (6-TGNs) and 6-methylmercaptopurine nucleotides (6-MMPNs) from erythrocytes with perchloric acid and converted them to 6-thioguanine (6-TG) and a 6-methylmercaptopurine (6-MMP) derivative during a 60-min acid hydrolysis step. The liquid chromatography system consisted of a C(18) column with an ammonium acetate-formic acid-acetonitrile buffer. 8-Bromoadenine was the internal standard. Analytes were measured with positive ionization and multiple reaction monitoring mode. With PCR-restriction fragment length polymorphism analysis and TaqMan allelic discrimination, common TPMT alleles (*1, *2, *3A, *3B, *3C) were determined in 31 792 individuals. We used perchloric acid extraction, acid hydrolysis, and HPLC with ultraviolet detection to measure erythrocyte 6-TG and 6-MMP nucleotide concentrations in 6189 patients with inflammatory bowel disease receiving azathioprine/6-mercaptopurine therapy. RESULTS: Intra- and interday imprecision were <10% at low and high analyte concentrations. The conversion of 6-TG and 6-MMP nucleoside mono-, di-, and triphosphates was complete after hydrolysis. Allelic frequency for TPMT variant alleles ranged from 0.0063% (*3B) to 3.61% (*3A). Compared with wild types, TPMT heterozygotes had an 8.3-fold higher risk for 6-TGNs >450 pmol/8 x 10(8) erythrocytes (concentration associated with increased risk for leukopenia), but an 8.2-fold lower risk for 6-MMPNs >5700 pmol/8 x 10(8) erythrocytes (concentration associated with increased risk for hepatotoxicity). CONCLUSIONS: The liquid chromatography-tandem mass spectrometry method can be applied to the routine monitoring of thiopurine therapy. The association between TPMT genotype and metabolite concentrations illustrates the utility of pharmacogenetics in the management of patients undergoing treatment with thiopurines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".