Unraveling the Disruptive Mechanism of Local Anesthetics on Raft-Like Ordered Membranes: Simulation Studies
Bibliographic record
Abstract
The membrane perturbation of local anesthetics (LAs) with distinct steric differences─dibucaine (Dib), tetracaine (Tet), and lidocaine (Lid)─as well as their behavior in different regions of the raft-like ordered (Lo) membrane, were investigated using umbrella sampling molecular dynamics (MD) simulations. Both uncharged (-u) and protonated (-p) forms were considered. Our findings from potential mean force (PMF), z -axis diffusion, and area per lipid (APL) confirmed that Dib-u preferentially located at the hydrophobic core, whereas Lid-u shows no specific localization preference and exhibits rapid diffusion across the Lo membrane. The steric effect drives the distinct translocation behavior of LAs-u but has no significant impact on LAs-p. These two factors─bulky properties and high affinity for the hydrophobic core, as observed in Dib-u─contribute to the membrane disruption. This observation is evidenced by the significantly lower PMF profile at the deep hydrophobic core, the greatest lipid packing disorder, and high steric bulk, which is in reasonable agreement with experimental observations. Three-dimensional reference interaction-site model (3D-RISM) analysis further supports the amphiphilic nature of LAs-u and an increase in hydrophilicity of LAs-p. This difference is a key factor modulating the action of LAs-u and LAs-p forms in different membrane environments. Our findings on the distinct behavior governing the Lo membrane translocation of LAs-u are influenced by preferred localization within the Lo membrane and the steric effect. These insights offer valuable insights for the anesthetic design and membrane-based biosensor development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".