Antiretroviral drugs efavirenz, dolutegravir and bictegravir dysregulate blood-brain barrier integrity and function
Bibliographic record
Abstract
The implementation of combined antiretroviral therapy (cART) significantly reduces the mortality associated with human immunodeficiency virus (HIV) infection. However, complications such as HIV-associated neurocognitive disorders (HAND) remain a major health concern. We hypothesized that the toxicity of antiretroviral drugs (ARVs) may contribute to the pathogenesis of HAND in addition to cerebral viral infection. To address this question, we evaluated the impact of HIV integrase strand transfer inhibitors (dolutegravir and bictegravir), and a non-nucleoside reverse transcriptase inhibitor (efavirenz) on the integrity and permeability of various human and mouse blood-brain barrier (BBB) models, in vitro, ex vivo and in vivo. We observed a significant downregulation of tight junction proteins (TJP1/Tjp1, OCLN/Ocln and CLDN5/Cldn5), upregulation of proinflammatory cytokines (IL6/Il6, IL8/Il8, IL1β/Il1β) and NOS2/Nos2, and alteration of membrane-associated transporters (ABCB1/Abcb1a, ABCG2/Abcg2 and SLC2A1/Slc2a1) mRNA expression, in vitro, in human (hCMEC/D3) and primary cultures of mouse microvascular endothelial cells, and ex vivo in isolated mouse brain capillaries treated with efavirenz, dolutegravir, and/or bictegravir. We also observed a significant increase in BBB permeability in vivo following treatment with the selected ARVs in mice applying NaF permeability assay. Taken together, these results suggest that clinically recommended integrase strand transfer inhibitors such as dolutegravir may exacerbate HIV-associated cerebrovascular pathology, which may contribute to the associated short-term neuropsychiatric side effects and the high incidence of mild forms of HAND reported in the clinical setting.
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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 | 0.000 |
| 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".