Stage-Dependent Inhibition of HIV-1 Replication by Antiretroviral Drugs in Cell Culture
Bibliographic record
Abstract
Recent clinical trials have shown that the use of the HIV-1 integrase (IN) inhibitor raltegravir (RAL) results in drops in the viral load that are more rapid than those achieved by use of the reverse transcriptase (RT) inhibitor efavirenz. Previously, mathematical modeling of viral load decay that takes into account the stage of viral replication targeted by a drug has yielded data that closely approximate the clinical trial results. This model predicts greater inhibition of viral replication by drugs that act later in the viral replication cycle. In the present study, we have added drugs that target entry, reverse transcription, integration, or proteolytic processing to acutely infected cells and have shown modest viral inhibition by entry inhibitors, intermediate levels of inhibition by RT and IN inhibitors, and high levels of inhibition by protease inhibitors relative to the levels of growth for the no-drug controls. When dual or triple combinations of these drugs were added to acutely infected cells, we found that the levels of inhibition achieved by any given combination were comparable to those achieved by the latest-acting drug in the combination. In single-round infections in which the kinetics of reverse transcription and integration had been determined by quantitative PCR, addition of IN inhibitors at various times postinfection resulted in levels of inhibition equal to or greater than those achieved by addition of RT inhibitors. Collectively, our data provide in vitro evidence of the stage-dependent inhibition of HIV-1 by clinically relevant drugs. We discuss how stage-dependent inhibition helps to explain the unique viral load decay dynamics observed clinically with RAL.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".