Biased agonism in psychopharmacology: an opportunity to improve efficacy and safety of treatments
Bibliographic record
Abstract
G protein-coupled receptors (GPCRs) are involved in many physiological and pathophysiological processes. Conventional pharmacological models categorize the typology of pharmacologic ligands as agonists or antagonists. Biased agonism is a relatively newer pharmacodynamic characteristic that has potential to optimize therapeutic efficacy while minimizing adverse effects in psychiatric and neurological treatments. We conducted a narrative literature review of articles obtained from PubMed, Embase, and MEDLINE from inception to April 2025, focusing on pharmacologic antagonism (i.e., competitive, noncompetitive, uncompetitive) and agonism (i.e., full, partial, inverse, superagonism, biased). Primary and secondary articles defining these concepts were included, provided they addressed pharmacologic (rather than chemical) antagonism and agonism. Distinct mechanisms of antagonism and agonism were identified, each contributing nuanced receptor modulation beyond the conventional models. Notably, biased agonism facilitates targeted intracellular signaling (e.g., G protein- versus β-arrestin-mediated). Use cases demonstrate relatively greater efficacy (e.g., incretin receptor agonist, tirzepatide) and improved safety (e.g., serotonergic psychedelics, opioids). Biased agonism provides a potential avenue for future drug development, with emerging preclinical evidence suggesting potential to differentially activate intracellular pathways and thereby improve efficacy and safety profiles of psychopharmacologic agents-pending clinical validation. Future research vistas should aim to rigorously assess the long-term outcomes of biased agonism, explicitly addressing individual variability in receptor signaling and therapeutic response.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".