Should the concomitant use of erlotinib and acid-reducing agents be avoided? The drug interaction between erlotinib and acid-reducing agents
Bibliographic record
Abstract
CONTEXT: Erlotinib, an epidermal growth factor receptor tyrosine kinase inhibitor, exhibits a drug interaction with proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs). The manufacturer recommends avoidance of the combination however, the extent of the drug interaction is not clearly understood. Evidence acquisition. A literature search was performed and the pharmacokinetics and pharmacology of acid-reducing agents were reviewed. RESULTS: Acid-reducing agents reduce the solubility, and subsequent absorption, of erlotinib by raising gastric pH. Our literature search was unable to identify any published studies or case reports that address this issue. Until more information is available, the clinical relevance of this interaction, and whether it actually leads to failure of therapy, is unknown. PPIs would all be expected to exhibit a similar effect on erlotinib. Of the H2RAs, co-administration appears to have a greater impact than administering them separately. Ranitidine, famotidine, and nizatidine should likely have similar effects on erlotinib absorption. Cimetidine has a shorter duration of action, but should be used with caution because of its effects on cytochrome P450 3A4, a pathway also utilized by erlotinib. Antacids are not expected to have a significant effect. CONCLUSIONS: The clinical relevance of this drug interaction is unknown. Until more information is available, decision making regarding this interaction should be on a patient-by-patient basis. The indication for acid-reducing therapy should be reevaluated and stopping therapy or changing therapy can be considered. However, there may be occasions where any benefit of such actions will be exceeded by its risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".