Maraviroc and Other HIV-1 Entry Inhibitors Exhibit a Class-Specific Redistribution Effect That Results in Increased Extracellular Viral Load
Bibliographic record
Abstract
HIV entry inhibitors, such as maraviroc (MVC), prevent cell-free viruses from entering the cells. In clinical trials, patients who were treated with MVC often displayed viral loads that were above the limit of conventional viral load detection compared to efavirenz-based regimens. We hypothesize that viruses blocked by entry inhibitors may be redistributed to plasma, where they artificially increase viral load measurements compared to those with the use of antiretroviral drugs (ARVs) that act intracellularly. We infected PM-1 cells with CCR5-tropic HIV-1 BaL or CXCR4-tropic HIV-1 NL4-3 in the presence of inhibitory concentrations of efavirenz, raltegravir, enfuvirtide, maraviroc, and AMD3100, the latter three being entry inhibitors. Supernatant viral load, reverse transcriptase enzyme activity, and intracellular nucleic acid levels were measured at times up to 24 h postinfection. Infectivity of redistributed dual-tropic HIV-1 was assessed using TZM-bl cells. Extracellular viral load analysis revealed that entry inhibitor-treated cells had higher levels of virus in the supernatant than the cells treated with other ARVs at 8 h postinfection. By 24 h, the supernatant viral load was still higher for entry inhibitors than other ARVs. We observed a correlation between viral load and the step of entry inhibition. Dual-tropic virus infectivity was undiminished utilizing the CCR5 coreceptor following redistribution by CXCR4 entry inhibition. This in vitro model indicates that entry inhibitors exhibit a redistribution effect unseen with intracellular ARV drugs. Based on these results, the effectiveness of some entry inhibitors may be underestimated if plasma viral load is used as a sole indicator of clinical success.
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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 | 0.001 | 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".