Personalised therapy during preconception and gestation in SLE: usefulness of 6-mercaptopurine metabolite levelswith azathioprine
Bibliographic record
Abstract
Although azathioprine (AZA) is the immunosuppressive of choice in SLE pregnancies, no one has evaluated 6-mercaptopurine (6-MP) metabolite levels in this population. Even outside pregnancy, the use of metabolite testing has not been widely applied in SLE.1 AZA is a prodrug that is cleaved to 6-MP, which is converted to the active nucleotides 6-thioguanine (6-TG) and via the enzyme thiopurine methyltransferase (TPMT) to 6-methylmercaptopurine (6-MMP) (figure 1).1 Studies in inflammatory bowel diseases (IBD) established the therapeutic range for 6-TG concentrations between 235 and 450 pmol/8×108 red blood cells (RBC), as higher concentrations are associated with higher risk of myelotoxicity without increased efficacy.1 6-MMP levels >5700 pmol/8×108 RBC are associated with a higher risk of hepatotoxicity.1 Additionally, a subgroup of patients resistant to AZA shunts 6-MP towards the overproduction of 6-MMP, which is reflected in an inability to achieve therapeutic 6-TG levels despite dose escalation.1 In one study, 31% of patients on AZA were identified as ‘shunters’.2 Figure 1 Azathioprine metabolism. AZA, azathioprine; 6-MP, 6-mercaptopurine; 6-TG, 6-thioguanine; 6-MMP, 6-methylmercaptopurine; TPMT, thiopurine methyltransferase. Identifying patients as non-adherent, treatment-refractory or undertreated, as well as identifying drug toxicity, could improve the efficacy and safety of clinical decision-making. As pregnancy is a particularly critical period to optimise disease control and minimise drug toxicity, we evaluated …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".