From optimized monovalent ligands to size-controlled dendrimers: an efficient strategy towards high-activity DC-SIGN antagonists
Bibliographic record
Abstract
This short review describes our work on the development of dendrimeric antagonists of DC-SIGN, a dendritic cells (DCs) receptor recognizing highly mannosylated structures and primarily involved in the recognition of viruses such as HIV. The structure of pseudo-di-mannoside and pseudo-tri-mannoside compounds was first finely modified to obtain DC-SIGN ligands that were more stable and selective than mannose. Their DC-SIGN affinity differences were amplified once presented on multivalent dendrimer-like scaffolds, including poly-alkyne terminated and phenylene-ethynylene rod-like ones. Libraries of mannosylated dendrimers were synthesized, improving their stability and maximizing their monodispersity. The effect of the dendrimers valency, structure, and size on DC-SIGN affinity and antiviral potency was investigated. Both the valency and the topology of the architectures were revealed as key parameters for activity optimization, together with the intrinsic affinity of the monovalent ligand. The stability, rigidity, and length of the scaffolds were also tuned. The design of geometrically adapted scaffolds afforded one of the most potent inhibitors of DC-SIGN mediated HIV infections to date. This monodispersed, not cytotoxic, and highly active compound was also tested with DCs; its internalization into endolysosomal compartments and its ability to induce the overexpression of signaling molecules makes it a good precursor to produce pathogen-entry inhibitors with immunomodulant properties.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".