Physiologically Based Pharmacokinetic Modeling and Simulation to Support a Change in the FDA‐Labeled Dosing Frequency of RHB‐105 Low‐Dose Rifabutin Triple Therapy for <i>Helicobacter pylori</i> Eradication
Bibliographic record
Abstract
Abstract Patient adherence is vital for Helicobacter pylori eradication. Simplifying therapy dosing schedules may promote patient adherence, enhance treatment success rates, and help mitigate the development of antibiotic resistance. We aimed to assess plasma and intragastric rifabutin, amoxicillin, and omeprazole concentrations comparing two dosing schedules of RHB‐105 (every 8 h and a more flexible three‐times daily schedule, at 8 a.m., 12 p.m., and 6 p.m.) using a validated physiologically based pharmacokinetic (PBPK) model. Leveraging in vitro and in vivo information on the pharmacokinetics of the three components of RHB‐105, we developed mechanistic absorption PBPK models to predict plasma and intragastric concentration–time profiles for each component. There were only negligible differences in the area under the concentration–time curves (AUC) for plasma and the intragastric compartment, and maximal concentration (Cmax) with only up to a 1.1‐fold difference for rifabutin, amoxicillin, and omeprazole between dosing schedules. Overlapping 90% confidence intervals for both AUC and Cmax support that overall exposures are comparable regardless of dosing every 8 h or three‐times daily for all three drugs. Drug exposure was highly similar for rifabutin, amoxicillin, and omeprazole with each dosing schedule. Novel mechanistic absorption PBPK modeling supports the approval and use of the more flexible dosing schedule for RHB‐105, simplifying patient experience and potentially increasing adherence.
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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) | 0.002 | 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".