Non-steroidal mineralocorticoid receptor antagonists in cardiorenal disease
Bibliographic record
Abstract
Despite existing treatments, patients with heart failure and chronic kidney disease (CKD) remain at high risk for adverse outcomes and progression to end-stage disease. Steroidal mineralocorticoid receptor antagonists (MRAs) such as spironolactone and eplerenone reduce mortality but remain under-prescribed due to the perceived risk of hyperkalaemia and hormonal side effects. The discovery of non-steroidal MRAs represents a major new dimension in cardiorenal disease therapy. Non-steroidal MRAs have high affinity and specificity for the mineralocorticoid receptor (MR) and differ from both steroidal agents and each other with respect to important physiochemical, pharmacodynamic, and pharmacokinetic parameters. Similar to their steroidal counterparts, they have beneficial anti-inflammatory, anti-remodelling, and anti-fibrotic properties in the kidneys, heart, and vasculature. There are several non-steroidal MRAs under development and clinical assessment; of these, only esaxerenone and finerenone are approved for treatment globally. In Japan, esaxerenone is approved for essential hypertension and has been studied in diabetic nephropathy. Compared with steroidal MRAs, finerenone more potently inhibits MR co-regulator recruitment and fibrosis and distributes more evenly between the heart and kidneys. The landmark Phase III trials FIGARO-DKD and FIDELIO-DKD demonstrated that finerenone-reduced major kidney and cardiovascular events on top of maximally tolerated renin-angiotensin-aldosterone system inhibition in patients with CKD associated with Type 2 diabetes. Non-steroidal MRAs are currently under evaluation in heart failure and for synergistic treatment with sodium-glucose contransporter 2 inhibitors. These ground-breaking agents could become an important therapy across the spectrum of cardiorenal disease.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".