Understanding how long‐acting β<sub>2</sub>‐adrenoceptor agonists enhance the clinical efficacy of inhaled corticosteroids in asthma – an update
Bibliographic record
Abstract
In moderate‐to‐severe asthma, adding an inhaled long‐acting β2‐adenoceptor agonist (LABA) to an inhaled corticosteroid (ICS) provides better disease control than simply increasing the dose of ICS. Acting on the glucocorticoid receptor (GR, gene NR3C1), ICSs promote anti‐inflammatory/anti‐asthma gene expression.In vitro, LABAs synergistically enhance the maximal expression of many glucocorticoid‐induced genes. Other genes, including dual‐specificity phosphatase 1(DUSP1) in human airways smooth muscle (ASM) and epithelial cells, are up‐regulated additively by both drug classes. Synergy may also occur for LABA‐induced genes, as illustrated by the bronchoprotective gene, regulator of G‐protein signalling 2 (RGS2) in ASM. Such effects cannot be produced by either drug alone and may explain the therapeutic efficacy of ICS/LABA combination therapies. While the molecular basis of synergy remains unclear, mechanistic interpretations must accommodate gene‐specific regulation. We explore the concept that each glucocorticoid‐induced gene is an independent signal transducer optimally activated by a specific, ligand‐directed, GR conformation. In addition to explaining partial agonism, this realization provides opportunities to identify novel GR ligands that exhibit gene expression bias. Translating this into improved therapeutic ratios requires consideration of GR density in target tissues and further understanding of gene function. Similarly, the ability of a LABA to interact with a glucocorticoid may be suboptimal due to low β2‐adrenoceptor density or biased β2‐adrenoceptor signalling. Strategies to overcome these limitations include adding‐on a phosphodiesterase inhibitor and using agonists of other Gs‐coupled receptors. In all cases, the rational design of ICS/LABA, and derivative, combination therapies requires functional knowledge of induced (and repressed) genes for therapeutic benefit to be maximized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".