Physiologically‐based pharmacokinetic modelling to predict intragastric rifabutin concentrations in the treatment of <i>Helicobacter pylori</i> infection
Bibliographic record
Abstract
Summary Background Sustained intragastric antibiotic exposure is important for Helicobacter pylori eradication, yet little is known about gastric pharmacology of commonly used H. pylori regimens. For rifabutin, differing intragastric concentrations based on dosing regimen may account for differences in reported eradication rates. Aim To compare intragastric rifabutin concentrations between low‐dose rifabutin (50 mg three time daily; as in RHB‐105) and generically dosed rifabutin 150 mg once daily, 150 mg twice daily, and 300 mg once daily using a validated Physiologically‐based pharmacokinetic (PBPK) model. Methods We obtained plasma pharmacokinetic data from the RHB‐105 clinical development programs and used it to develop and validate a whole‐body PBPK model using PK‐SIM software. We modified the existing rifabutin model to include the impact of omeprazole on gastric pH and emptying time. Modelled intragastric rifabutin exposure was expressed as the time that each regimen maintained its concentration ≥MIC90. Results Rifabutin 50 mg three times daily achieved significantly longer times with intragastric concentration above MIC90 (22.3 ± 1.1 h) than 150 mg once daily (8.3 ± 1.7 h), 150 mg twice daily (16.3 ± 2.3 h), or 300 mg once daily (8.5 ± 1.9 h) while providing the lowest mean maximal plasma concentration and mean area under the plasma concentration–time curve of all regimens studied. Conclusions PBPK modelling showed rifabutin 50 mg three times daily had higher intragastric exposure times than 150 mg once daily or twice daily, or 300 mg once daily. This low‐dose rifabutin regimen provides the highest potential for H. pylori eradication while minimising systemic rifabutin exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".