Optimizing proton-pump inhibitor therapy in paediatric eosinophilic esophagitis through <i>CYP2C19</i> pharmacogenetic testing
Bibliographic record
Abstract
Abstract Background Eosinophilic esophagitis (EoE) is a chronic inflammatory disorder which can respond to proton-pump inhibitors (PPIs). Genetic variation in the CYP2C19 metabolism gene influences PPI efficacy and adverse effects. Pharmacogenetic testing (PGx) can predict PPI response by analyzing genetic variation, particularly identifying patients categorized as CYP2C19 rapid or ultra-rapid metabolizers who might benefit from PPI dosage increases or changes to pharmacotherapy. Although PGx clinical practice guidelines have been established for PPI use, routine clinical implementation has been slow. Methods We conducted a non-interventional prospective cohort study of patients followed by a paediatric EoE clinic between 2020 and 2023. Eligible patients underwent CYP2C19 PGx testing, with results correlated to PPI use and histological outcomes assessed via endoscopic biopsies. Results Sixty-nine patients underwent PGx testing; 20 (29%) and 5 (7%) were determined to be rapid and ultra-rapid metabolizers, respectively. PGx-based management changes were made in 44 (64%) patients. Forty-three (62%) patients completed reassessment endoscopy, of which 21 (49%) demonstrated histological remission; 17 (40%) of these patients achieved remission after PGx-guided drug changes. Conclusions This study demonstrates that PPI non-response in patients with EoE may partly be due to inadequate PPI dosing in those with rapid or ultra-rapid CYP2C19 metabolizer status. Identifying CYP2C19 metabolizer status in pediatric patients with EoE for first-generation PPIs leads to therapeutic management changes and can improve histological remission rates. Clinicians treating EoE patients should consider routine PGx testing in combination with monitoring clinical factors to guide individualized PPI therapy and optimize dosing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".