Dutch pharmacogenetics working group guideline for the gene-drug interaction of ABCG2, HLA-B and Allopurinol, and MTHFR, folic acid and methotrexate
Bibliographic record
Abstract
The Dutch Pharmacogenetics Working Group (DPWG) aims to facilitate PGx implementation by developing evidence-based pharmacogenetics guidelines to optimize pharmacotherapy. This guideline describes the gene-drug interaction of ABCG2 with allopurinol, HLA-B with allopurinol, MTHFR with folic acid, and MTHFR with methotrexate, relevant for the treatment of gout, cancer, and rheumatoid arthritis. A systematic review was performed based on which pharmacotherapeutic recommendations were developed. Allopurinol is less effective in patients with the ABCG2 p.(Gln141Lys) variant. In HLA-B*58:01 carriers, the risk of severe cutaneous adverse events associated with allopurinol is strongly increased. The DPWG recommends using a higher allopurinol dose in patients with the ABCG2 p.(Gln141Lys) variant. For HLA-B*58:01 positive patients the DPWG recommends choosing an alternative (for instance febuxostat). The DPWG indicates that another option would be to precede treatment with allopurinol tolerance induction. Genotyping of ABCG2 in patients starting on allopurinol was judged to be 'potentially beneficial' for drug effectiveness, meaning genotyping can be considered on an individual patient basis. Genotyping for HLA-B*58:01 in patients starting on allopurinol was judged to be 'beneficial' for drug safety, meaning it is advised to consider genotyping the patient before (or directly after) drug therapy has been initiated. For MTHFR-folic acid there is evidence for a gene-drug interaction, but there is insufficient evidence for a clinical effect that makes therapy adjustment useful. Finally, for MTHFR-methotrexate there is insufficient evidence for a gene-drug interaction.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".