QGC606: A Best-in-Class Orally Active Centrally Acting Aminopeptidase A Inhibitor Prodrug for Treating Heart Failure Following Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Blockade of brain renin-angiotensin system (RAS) overactivity by firibastat, the first centrally acting aminopeptidase A (APA) inhibitor prodrug, has already demonstrated its effectiveness in improving cardiac function after myocardial infarction (MI). We developed QGC606, a more potent and more selective APA inhibitor prodrug and studied its effects after long-term oral administration in mice post-MI. METHODS: Two days after MI induced by the left anterior descending artery ligation, adult male mice were randomized into 4 groups to receive oral treatment during 4 weeks with vehicle; QGC606; firibastat; or the angiotensin-I converting enzyme inhibitor ramipril, used as positive control. RESULTS: Four weeks post-MI, brain APA was overactivated in vehicle-treated MI mice. QGC606 treatment normalized brain APA hyperactivity to control values measured in sham-operated mice. Four weeks post-MI, QGC606-treated mice had higher left ventricular (LV) ejection fractions, significantly smaller LV end-systolic diameter and volume, significantly lower HF biomarkers mRNA expression (Myh7 and Anf) and plasma N-terminal pro B-type natriuretic peptide (NT-pro-BNP) and noradrenaline levels than saline-treated mice. QGC606 treatment significantly improved the dP/dt max and min, LV end-diastolic pressure without affecting blood pressure (BP), whereas we observed a decrease in BP in ramipril-treated mice. We observed also a reduction of cardiac fibrosis, highlighted by lower connective tissue growth factor mRNA levels and a reduction of both the fibrotic area and MI size in QGC606-treated mice. CONCLUSIONS: Chronic oral QGC606 administration in post-MI mice showed beneficial effects in improving cardiac function and reducing cardiac remodeling and fibrosis but, unlike ramipril, without lowering BP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".