Predictive model of bosentan-induced liver toxicity in Japanese patients with pulmonary arterial hypertension
Bibliographic record
Abstract
Bosentan, an endothelin receptor antagonist, has been widely used as a first-line medication for the treatment of pulmonary arterial hypertension (PAH). It has been shown to improve symptoms of hypertension, exercise capacity, and hemodynamics and prolong time to clinical worsening. However, liver dysfunction is a major side effect of bosentan treatment that could hamper the optimal management of patients with PAH. Previously, we demonstrated, using drug metabolism enzymes and transporters analysis, that the carbohydrate sulfotransferase 3 (CHST3) and CHST13 alleles are significantly more frequent in patients with elevated aminotransferases during therapy with bosentan than they are in patients without liver toxicity. In addition, we constructed a pharmacogenomics model to predict bosentan-induced liver injury in patients with PAH using two single-nucleotide polymorphisms and two nongenetic factors. The purpose of the present study was to externally validate the predictive model of bosentan-induced liver toxicity in Japanese patients. We evaluated five cases of patients treated with bosentan, and one presented with liver dysfunction. We applied mutation alleles of CHST3 and CHST13, serum creatinine, and age to our model to predict liver dysfunction. The sensitivity and specificity were calculated as 100% and 50%, respectively. Considering that PAH is a rare disease, multicenter collaboration would be necessary to validate our model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".