NS2B‐NS3 protease inhibitors as promising compounds in the development of antivirals against Zika virus: A systematic review
Bibliographic record
Abstract
Zika virus (ZIKV) infections are associated with severe neurological complications and are a global public health concern. There are no approved vaccines or antiviral drugs to inhibit ZIKV replication. NS2B-NS3 protease (NS2B-NS3 pro), which is essential for viral replication, is a promising molecular target for anti-ZIKV drugs. We conducted a systematic review to identify compounds with promising effects against ZIKV; we discussed their pharmacodynamic and pharmacophoric characteristics. The online search, performed using the PubMed/MEDLINE and SCOPUS databases, yielded 56 articles; seven relevant studies that reported nine promising compounds with inhibitory activity against ZIKV NS2B-NS3 pro were selected. Of these, five (niclosamide, nitazoxanide, bromocriptine, temoporfin, and novobiocin) are currently available on the market and have been tested for off-label use against ZIKV. The 50% inhibitory concentration values of these compounds for the inhibition of NS2B-NS3 pro ranged at 0.38-21.6 µM; most compounds exhibited noncompetitive inhibition (66%). All compounds that could inhibit the NS2B-NS3 pro complex showed potent in vitro anti-ZIKV activity with a 50% effective concentration ranging 0.024-50 µM. The 50% cytotoxic concentration of the compounds assayed using A549, Vero, and WRL-69 cell lines ranged at 0.6-1388.02 µM and the selectivity index was 3.07-1698. This review summarizes the most promising antiviral agents against ZIKV that have inhibitory activity against viral proteases.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".