Biased agonism of clinically approved μ-opioid receptor agonists and TRV130 is not controlled by binding and signaling kinetics
Bibliographic record
Abstract
Binding and signaling kinetics have previously proven important in validation of biased agonism at GPCRs. Here we provide a comprehensive kinetic pharmacological comparison of clinically relevant μ-opioid receptor agonists, including the novel biased agonist oliceridine (TRV130) which is in clinical trial for pain management. We demonstrate that the bias profile observed for the selected agonists is not time-dependent and that agonists with dramatic differences in their binding kinetic properties can display the same degree of bias. Binding kinetics analyses demonstrate that buprenorphine has 18-fold higher receptor residence time than oliceridine. This is thus the largest pharmacodynamic difference between the clinically approved drug buprenorphine and the clinical candidate oliceridine, since their bias profiles are similar. Further, we provide the first pharmacological characterization of (S)-TRV130 demonstrating that it has a similar pharmacological profile as the (R)-form, oliceridine, but displays 90-fold lower potency than the (R)-form. This difference is driven by a significantly slower association rate. Finally, we show that the selected agonists are differentially affected by G protein-coupled receptor kinase 2 and 5 (GRK2 and GRK5) expression. GRK2 and GRK5 overexpression greatly increased μ-opioid receptor internalization induced by morphine, but only had modest effects on buprenorphine and oliceridine-induced internalization. Overall, our data reveal that the clinically available drug buprenorphine displays a similar pharmacological bias profile in vitro compared to the clinical candidate drug oliceridine and that this bias is independent of binding kinetics suggesting a mechanism driven by receptor-conformations. This article is part of the Special Issue entitled 'New Vistas in Opioid Pharmacology'.
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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".