An update on neuropsychiatric adverse effects with second-generation integrase inhibitors and nonnucleoside reverse transcriptase inhibitors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Neuropsychiatric adverse effects (NPAE) associated with integrase strand transfer inhibitors (INSTIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs) are a growing concern, with higher rates in the real-world compared to phase III trials. This paper reviews the incidence, risk factors, and management of NPAE with second-generation INSTIs, INSTI/rilpivirine dual therapy, and doravirine. RECENT FINDINGS: Recent cohort data confirm up to 8% NPAE-associated discontinuations for dolutegravir; NPAE with dolutegravir/rilpivirine therapy are higher than with dolutegravir alone, whereas bictegravir appears similar to dolutegravir. In contrast, NPAE with cabotegravir alone or with rilpivirine appears to be low. Doravirine has NPAE rates similar to rilpivirine and lower than efavirenz. Risk factors for NPAE include female gender, concurrent abacavir use, Sub-Saharan African descent, and age, whereas underlying psychiatric conditions do not appear to increase risk. Strategies to manage NPAE include changing administration time, therapeutic drug monitoring, or regimen modification including within-class INSTI changes. People experiencing NPAE with dolutegravir may tolerate bictegravir. SUMMARY: Overall, mild to moderate NPAE are associated with INSTIs and newer NNRTIs. Rarely, more severe symptoms may occur and lead to treatment discontinuation. Clinicians should be aware of NPAE to identify and manage drug-related adverse effects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".