Letter: thiopurines during pregnancy and intrauterine exposure to metabolites – author's reply
Bibliographic record
Abstract
We thank Dr. de Boer and colleagues for their comments, and for bringing attention to the issue of thiopurine metabolism in the placenta and foetus.1 Many previous review articles and guidelines have commented on the fact that the foetal liver lacks the enzyme ‘inosinate pyrophosphorylase’.2-5 Our use of the term ‘inosinate phosphorylase’ may have led to some confusion.6 The articles quoted by de Boer et al.7, 8 provide case reports where measurements of thiopurine metabolites were taken from the red blood cells of mother and infant, specifically 6-MMP and 6-TGN. However, they do not mention measurement of 6-MP levels. It has been shown that low levels of 6-MP can be found in foetal blood.9 It is therefore theoretically possible that the foetus is metabolising 6-MP into 6-TGN. This ‘inosinate phosphorylase’ may in fact refer to the ‘inosine triphosphate pyrophosphatase’ enzyme which is involved in the metabolism of 6-methyl-thioinosine-monophosphate (6-MTIMP) back to 6-thioinoosine-monophosphate (6-TIMP), one of the metabolites of 6-MP.10 If the foetus lacks this enzyme, then presumably the 6-TIMP that is phosphorylated to 6-TIDP and then 6-TITP will not be phosphorylated back to 6-TIMP, and hence, the 6-TIMP that is available to continue metabolism to 6-TGMP is lower. More studies are needed to determine the metabolic pathways involved in the metabolism of azathioprine and mercaptopurine from the mother, in the placenta, and in the foetus. However, the most important point to take away from this discussion is that the studies agree that even if low levels of metabolites of azathioprine are found in the foetus, maintenance thiopurine therapy during pregnancy is considered safe. Declaration of personal interests: F. Habal served on the advisory board for Abbott Pharmaceutical (January 2012). Declaration of funding interests: None.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.038 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".