A translational pharmacokinetic/pharmacodynamic approach supports optimal vonoprazan dosing for erosive oesophagitis and <i>Helicobacter pylori</i> infection
Bibliographic record
Abstract
BACKGROUND: Treatment of acid-related disorders relies on gastric acid suppression. The percentage of time intragastric pH is >4 (pH >4 holding time ratio [HTR]) is important for healing erosive oesophagitis; and the pH >6 HTR is critical for eradication of Helicobacter pylori infection, as bacterial replication is active and antibiotic effectiveness is optimised. Vonoprazan, a potassium-competitive acid blocker approved in the USA and other countries, suppresses gastric acid secretion in a predictable, rapid and consistent manner, extended over prolonged periods. AIM: To explore the relationship between vonoprazan exposure and pH HTR through a pharmacokinetic/pharmacodynamic (PK/PD) model. METHODS: We pooled data from Phase 1 studies with intragastric pH measurements. Pharmacokinetic profiles were predicted for study participants using an existing population pharmacokinetic model. Pharmacokinetic and pharmacodynamic data were merged, and three direct-link PK/PD models were derived and used to simulate pH HTRs with between-participant variability for pH >4, >5 and >6, for vonoprazan doses of 20 mg once and twice daily. RESULTS: We used data from five Phase 1 studies to derive the PK/PD model. These included 245 participants (95.1% male, 50.6% Japanese and 49.4% non-Asian). Pre-dose, the mean pH >4 HTR was 6.4%, pH >5 3.2% and pH >6 1.2%. After 7 days of dosing, simulations predicted pH >4 HTRs of 89.7% and 98.1%, and pH >6 HTRs of 53.1% and 75.3%, for vonoprazan 20 mg once and twice daily, respectively. CONCLUSIONS: Vonoprazan 20 mg once- and twice-daily dosing demonstrated high, dose-dependent, 24-hour intragastric acid control in this PK/PD model, supporting clinical efficacy data in patients with acid-related disorders.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".