Actions of Novel Angiotensin Receptor Blocking Drugs, Bisartans, Relevant for COVID-19 Therapy: Biased Agonism at Angiotensin Receptors and the Beneficial Effects of Neprilysin in the Renin Angiotensin System
Bibliographic record
Abstract
Angiotensin receptor blockers (ARBs) used in the treatment of hypertension and potentially in SARS-CoV-2 infection exhibit inverse agonist effects at angiotensin AR1 receptors, suggesting the receptor may have evolved to accommodate naturally occurring angiotensin 'antipeptides'. Screening of the human genome has identified a peptide (EGVYVHPV) encoded by mRNA, complementary to that encoding ANG II itself, which is an inverse agonist. Thus, opposite strands of DNA encode peptides with opposite effects at AR1 receptors. Agonism and inverse agonism at AR1 receptors can be explained by a receptor 'switching' between an activated state invoking receptor dimerization/G protein coupling and an inverse agonist state mediated by an alternative/second messenger that is slow to reverse. Both receptor states appear to be driven by the formation of the ANG II charge-relay system involving TyrOH-His/imidazole-Carboxylate (analogous to serine proteases). In this system, tyrosinate species formed are essential for activating AT1 and AT2 receptors. ANGII is also known to bind to the zinc-coordinated metalloprotease angiotensin converting enzyme 2 (ACE2) used by the COVID-19 virus to enter cells. Here we report in silico results demonstrating the binding of a new class of anionic biphenyl-tetrazole sartans ('Bisartans') to the active site zinc atom of the endopeptidase Neprilysin (NEP) involved in regulating hypertension, by modulating humoral levels of beneficial vasoactive peptides in the RAS such as vasodilator angiotensin (1-7). In vivo and modeling evidence further suggest Bisartans can inhibit ANG II-induced pulmonary edema and may be useful in combatting SARS-CoV-2 infection by inhibiting ACE2-mediated viral entry to cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".