5. Inhibitory Potency of Tamiflu Oseltamivir and DANA and their Modified Derivatives on Lipopolysaccharide-Induced Neu1 Sialidase Activity in Live BMA Macrophage Cells
Bibliographic record
Abstract
Toll-like receptors (TLRs) are a group of ancient receptors found on the surface of cells in our innate immune system. They are responsible for detecting conserved molecules found on pathogenic microbes, called Pathogen Associated Molecular Patterns (PAMP), such as lipopolysaccharide (LPS) molecules on the cell surfaces of Gram-negative bacteria. The activation of TLRs leads to immune responses against the pathogen infection. Although the cell signalling follow the activation of TLRs is well characterized, the initial mechanisms for TLR activation upon detecting PAMPs are not well understood. For the TLR-2,-3 and-4 receptors, we reported that an enzyme called Neu1 sialidase forms a complex with the TLR receptors on the cell surface of naïve and activated macrophages (Amith et al, 2009). Activation of this Neu1 is induced by the binding of TLR ligands, such as LPS, to their respective receptors; a specific sialyl -2,3-linked β-galactosyl residue on the TLR is hydrolyzed by the activated Neu1 enzyme. Neuraminidase inhibitors such as BCX1827, DANA, zanamivir and oseltamivir carboxylate have a limited inhibition of this LPS-induced Neu1 activity in live macrophage cells. In contrast, Tamiflu (oseltamivir phosphate) completely blocked this Neu1 activity. Here, we tested the inhibitory potency of a series of DANA and modified Tamiflu derivatives against the activity of the Neu1 enzyme. The results suggest that the linear alkyl side chains of DANA derivatives may contribute to their increased inhibitory potency on LPS-induced Neu1 activity compared to the derivatives with methyl side chain branches and to the parent DANA compound.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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".