Bibliographic record
Abstract
Inhibiting the HIV-1 entry process offer a new therapeutic target and the hope to potentialize our current treatments against wild-type or drug-resistant viruses. Several inhibitors of CD4, co-receptor CCR5 or CXCR4 and fusion are at various levels of clinical development. How best to use this class of drugs in our therapeutic arsenal remains to be defined. It is likely that these compounds will not be used as monotherapy. Therefore, it is important to evaluate how these drugs will interact among themselves as well as with antiretrovirals from other classes. Drug interactions can range from synergy to antagonism depending on factors including binding affinity, drug concentrations, and pharmacokinetics. In the case of entry inhibitors, one must also consider that the entry of HIV-1 into the cell is a multi-step process that involve cumulative events which are interdependent. Furthermore, polymorphism both in the coreceptors and in gp120, the density of coreceptors, and the binding site of the drug may also affect efficacy. Therefore it is difficult to predict how blocking one step of the process will affect the subsequent one without carefully studying interactions of each potential combination in an in vitro system. So far, studies of interactions between fusion inhibitors and coreceptor inhibitors have shown a high level of synergy. Similar studies performed with two co-receptor inhibitors have shown results varying from synergy to high antagonism depending on the viral isolate and the compounds used. In the following chapter, we will review some concepts of mechanisms that may affect these interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".