Highlights on the Development, Related Patents, and Prospects of Lenacapavir: The First-in-Class HIV-1 Capsid Inhibitor for the Treatment of Multi-Drug-Resistant HIV-1 Infection
Bibliographic record
Abstract
The multidrug-resistant (MDR) human immunodeficiency virus 1 (HIV-1) infection is an unmet medical need. HIV-1 capsid plays an important role at different stages of the HIV-1 replication cycle and is an attractive drug target for developing therapies against MDR HIV-1 infection. Lenacapavir (LEN) is the first-in-class HIV-1 capsid inhibitor approved by the USFDA, EMA, and Health Canada for treating MDR HIV-1 infection. This article highlights the development, pharmaceutical aspects, clinical studies, patent literature, and future directions on LEN-based therapies. The literature for this review was collected from PubMed, authentic websites (USFDA, EMA, Health Canada, Gilead, and NIH), and the free patent database (Espacenet, USPTO, and Patent scope). LEN has been developed by Gilead and is marketed as Sunlenca (tablet and subcutaneous injection). The long-acting and patient-compliant LEN demonstrated a low level of drug-related mutations, is active against MDR HIV-1 infection, and does not reveal cross-resistance to other anti-HIV drugs. LEN is also an excellent drug for patients having difficult or limited access to healthcare facilities. The literature has established additive/synergistic effects of combining LEN with rilpivirine, cabotegravir, islatravir, bictegravir, and tenofovir. HIV-1 infection may be accompanied by opportunistic infections such as tuberculosis (TB). The associated diseases make HIV treatment complex and warrant drug interaction studies (drug-drug, drug-food, and drug-disease interaction). Many inventions on different aspects of LEN have been claimed in patent literature. However, there is a great scope for developing more inventions related to the drug combination of LEN with anti-HIV/anti-TB drugs in a single dosage form, new formulations, and methods of treating HIV and TB co-infection. Additional research may provide more LEN-based treatments with favorable pharmacokinetic parameters for MDR HIV-1 infections and associated opportunistic infections such as TB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".