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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".