Onset–potency relationship of nondepolarizing muscle relaxants: a reexamination using simulations
Bibliographic record
Abstract
Nondepolarizing muscle relaxants (MRs) display an inverse onset-potency relationship, that is, less potent MRs display a more rapid onset. We have conducted the current investigation to estimate the impact of variable pharmacokinetic or pharmacodynamic properties of the MRs on potency and onset time, and on the onset-potency relationship. Using a model of neuromuscular transmission, we changed either the affinity of MRs for the postsynaptic receptors or the pharmacokinetic properties of the MRs. The elimination rate constant, k(10), which defines the systemic clearance, was assigned one of 9 values and the transport rate constant, k(12), one of 5 values. The transport rate constant into the effect compartment was constant (k(e1) = 0.2 min(-1)). Only one parameter was altered at a time. With constant pharmacokinetics, a 100-fold decrease in affinity caused a proportional decrease in potency, but little change (0.02 min) in onset time. With constant affinity, increasing the clearance from 1 to 250 mL x kg(-1) x min(-1) shortened the onset time from 7.2 to 0.7 min and decreased the potency 12-fold. In a double logarithmic plot, the onset-potency relationship was linear. Lesser affinities produce a nearly parallel rightward shift of the regression lines. The inverse onset-potency relationship may be explained by the pharmacokinetic factors producing changes in both the potency and onset times.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".