Improved C5-amide bioisosteres for human neuraminidase 1 inhibitors based on 2-deoxy-2,3-didehydro-N-acetyl neuraminic acid (DANA)
Bibliographic record
Abstract
Neuraminidase enzymes (NEU) play a crucial role in many physiological and pathological conditions. Humans have four isoenzymes of NEU and their specific roles continue to be investigated. Isoenzyme-selective inhibitors are needed as research tools and may lead to future therapeutics. We tested a series of new candidate inhibitors by replacing the C5-amide of 2-deoxy-2,3-dididehydro-N-acetyl neuraminic acid (DANA) with amide bioisosteres. Design of candidate inhibitors was accomplished using substituents that were components of previously identified NEU inhibitors combined with alternative amide bioisosteres. Compounds were tested for inhibition of the four human NEU, and inhibitory activities were compared to reference amide compounds. We observed that 1,4-disubstituted-1,2,3-triazole was the best bioisostere for inhibitors of NEU1. Inhibitor 542 showed high potency for NEU1 (K¬i = 0.4 ± 0.1 μM) and gave significant improvement in selectivity compared to the reference amide compound 502. Additionally, compound 542 had improved lipophilic characteristics which could provide improved pharmacokinetic properties. Screening of these inhibitors also identified a selective NEU2 inhibitor 543 (Ki = 2.6 ± 0.6 μM), illustrating that amide bioisostere replacement can identify improved inhibitors for multiple NEU isoenzymes.
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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".