LENACAPAVIR: A NOVEL CAPSID INHIBITOR TRANSFORMING HIV MANAGEMENT
Bibliographic record
Abstract
Lenacapavir (GS‑6207, brand Sunlenca/Yeztugo) is an anti-Retroviral drug developed by Gilead Sciences as one of very first class of HIV capsid inhibitor. Its discovery has been built upon decades of NIH (National Institute of health)-funded structural biology research showing the HIV capsid (p24) as a critical, druggable target. Structure-based design in the 2010s led to GS‑CA1 and later lenacapavir, which binds between capsid subunits and “molecularly glues” them together. Researchers found lenacapavir disrupts the capsid by binding CA proteins, preventing the virus from entering the nucleus or replicating. In 2019 the FDA granted Breakthrough Therapy designation. The pivotal Phase 2/3 CAPELLA trial in heavily treatment-experienced (HTE) patients with multi-drug-resistant HIV was launched (NCT04150068) and later supported global regulatory filings. By Aug 2022 the EMA granted marketing authorization (EU) for lenacapavir in multi-drug-resistant HIV, followed by US FDA approval (Sunlenca) in Dec 2022 and Canadian approval soon after. On June 18, 2025, the U.S. Food and Drug Administration (FDA) approved Yeztugo (lenacapavir) for use as PrEP to reduce the risk of sexually-acquired HIV-1 infection in adults and adolescents weighing ≥ 35 kg. The drug is given via subcutaneous injection every 6 months (twice-yearly), making it the first FDA-approved HIV prevention option with that dosing frequency.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".