Highly Potent Inhibitors of Proprotein Convertase Furin as Potential Drugs for Treatment of Infectious Diseases
Bibliographic record
Abstract
Optimization of our previously described peptidomimetic furin inhibitors was performed and yielded several analogs with a significantly improved activity. The most potent compounds containing an N-terminal 4- or 3-(guanidinomethyl)phenylacetyl residue inhibit furin with K(i) values of 16 and 8 pM, respectively. These analogs inhibit other proprotein convertases, such as PC1/3, PC4, PACE4, and PC5/6, with similar potency, whereas PC2, PC7, and trypsin-like serine proteases are poorly affected. Incubation of selected compounds with Madin-Darby canine kidney cells over a period of 96 h revealed that they exhibit great stability, making them suitable candidates for further studies in cell culture. Two of the most potent derivatives were used to inhibit the hemagglutinin cleavage and viral propagation of a highly pathogenic avian H7N1 influenza virus strain. The treatment with inhibitor 24 (4-(guanidinomethyl)phenylacetyl-Arg-Val-Arg-4-amidinobenzylamide) resulted in significantly delayed virus propagation compared with an inhibitor-free control. The same analog was also effective in inhibiting Shiga toxin activation in HEp-2 cells. This antiviral effect, as well as the protective effect against a bacterial toxin, suggests that inhibitors of furin or furin-like proprotein convertases could represent promising lead structures for future drug development, in particular for the treatment of infectious diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".