Acemetacin–phosphatidylcholine interactions are determined by the drug ionization state
Bibliographic record
Abstract
Gastrointestinal (GI) toxicity is a major drawback of the chronic use of nonsteroidal anti-inflammatory drugs (NSAIDs). The NSAIDs topical actions on the protective phospholipid layers of the GI mucosa seem to be a central toxicity mechanism of these pharmaceuticals. This work describes the interactions of acemetacin, a commercialized NSAID, with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) bilayers at pH 3.0, 5.0, and 7.4. This pH range was chosen to mimic the pH gradient found in the gastric mucosa, and to ultimately gain insights into the mechanisms underlying the acemetacin-induced gastric toxicity. Various experimental techniques were combined to characterize the partitioning of acemetacin in DMPC bilayers, and its effects on the phase transition behavior, as well as the structure and dynamics of DMPC bilayers. The acemetacin-DMPC interactions were clearly pH-dependent. The neutral (protonated) form of acemetacin had more affinity for the DMPC bilayer than the negatively charged form. Due to the higher affinity of neutral acemetacin, the drug effects on the phase transition and the structure and dynamics of the DMPC bilayer were more pronounced at lower pH values. In general, acemetacin decreased the temperature and the cooperativity of the lipid phase transition and induced changes in the packing and dynamics of the DMPC bilayer. These results support the hypothesis that acemetacin-induced gastric toxicity may be related to its effects on the protective phospholipid layers of the mucosal barrier.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".