The influence of affinity, efficacy, and slope factor on the estimates obtained by the receptorial responsiveness method (RRM): a computer simulation study
Bibliographic record
Abstract
The receptorial responsiveness method (RRM) was proposed to characterize changes in the concentration of degradable agonists in the microenvironment of their receptors. The characterization is done by providing concentrations of a stable agonist for the same receptor that is equieffective with the change in concentration to be characterized. RRM is based on the analysis of concentration-effect (E/c) curves reflecting the simultaneous action of the degradable and the stable agonist. In the present study, we investigated whether dissimilar affinity and (or) efficacy of the coacting agonists as well as the steepness of the E/c curves influence the reliability of RRM. E/c curves were simulated based on the operational model and then analyzed with RRM. We found that dissimilarity in affinity of the coacting agonists did not affect the accuracy of RRM estimates. In contrast, accuracy of the estimation depended on the magnitude of the concentration to be assessed, the operational slope factor, and the operational efficacy ratio of the coacting agonists. However, our results suggest that proper choice of a stable agonist for a degradable one can help to ensure reliable results, since information about the change in concentration of a degradable agonist is otherwise difficult to obtain.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".