Galectin-1-Specific Inhibitors as a New Class of Compounds To Treat HIV-1 Infection
Bibliographic record
Abstract
Despite significant improvements, antiretroviral therapies against HIV-1 are plagued by a high frequency of therapeutic failures that have been associated with acquisition of drug resistance. We recently reported that HIV-1 exploits a host glycan binding protein, galectin-1, to increase its attachment to host cells, thereby increasing its overall infectivity in susceptible cells. This finding suggests that host molecules such as galectin-1 could reduce the expected efficiency of HIV-1 drugs targeting early steps of the replicative cycle, such as attachment and entry processes. Thus, new classes of drugs that would interfere with galectin-1/HIV-1 interactions could benefit the current antiretroviral therapy. To further explore this possibility, experiments were conducted to discover leading compounds showing specific inhibition of galectin-1 activity in a cellular model of HIV-1 infection. Three lactoside compounds were found to modestly inhibit the interaction of galectin-1 with primary human CD4(+) T cells. Interestingly, these same inhibitors reduced the galectin-1-mediated increase in HIV-1 attachment to target cells in a much more efficient manner. More important, the tested lactoside derivatives also significantly decreased the galectin-1-dependent enhancement of HIV-1 infection. These observations deserve further attention when considering that the development of new drugs to prevent and treat HIV-1 infection remains a priority.
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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) | 0.000 | 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".