Novel Dolutegravir and Lenacapavir Resistance Patterns in Human Immunodeficiency Virus Type 2 Infection: A Case Report
Bibliographic record
Abstract
Background: The treatment management of human immunodeficiency virus (HIV)-2 infection presents greater challenges compared to HIV-1 infection, primarily because of inherent resistance against non-nucleoside reverse transcriptase inhibitors. Integrase strand transfer inhibitors, particularly dolutegravir, have improved treatment outcomes for people with HIV-2. Lenacapavir, a novel and potent antiretroviral capsid inhibitor, offers additional therapeutic options. However, limited knowledge exists regarding HIV-2 resistance against dolutegravir and lenacapavir. Methods: structural modeling were performed. Results: Lenacapavir was added to a failing regimen of boosted darunavir, twice daily dolutegravir, and 2 nucleoside reverse transcriptase inhibitors. Initially, this addition led to a decline in the viral load and increase in CD4+ T-cell count, despite the identification of a previously unreported combination of integrase resistance mutations. However, virological suppression was not achieved and viral load, although reduced, resumed increasing. This rebound was associated with the development of an N73D capsid substitution in HIV-2, which conferred resistance against lenacapavir. Based on cell-based assays predicting hypersusceptibility to bictegravir, the regimen was adjusted to oral lenacapavir plus bictegravir/emtricitabine/tenofovir alafenamide, resulting in a resumption in viral load decline. Conclusions: Although lenacapavir demonstrated therapeutic potential, our case underscores the critical need to combine it with other fully active antiretroviral agents to prevent the rapid emergence of resistance and achieve long-term virological control in treatment-experienced individuals with HIV-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".