Insights into the broad cellular effects of nelfinavir and the HIV protease inhibitors supporting their role in cancer treatment and prevention
Bibliographic record
Abstract
PURPOSE OF REVIEW: The development of HIV protease inhibitors more than two decades ago heralded a new era in HIV care, changing the infection from universally fatal to chronic but controllable. With the widespread use of protease inhibitors, there was a reduction in the incidence and mortality of HIV-associated malignancies. Studies later found these drugs to have promising direct antitumor effects. RECENT FINDINGS: Protease inhibitors have a wide range of effects on several cellular pathways that are important for tumorigenesis and independent of inhibition of the HIV protease, including reducing angiogenesis and cell invasion, inhibition of the Akt pathway, induction of autophagy, and promotion of apoptosis. Among protease inhibitors, nelfinavir appears to have the most potent and broad antineoplastic activities, and also affects replication of the oncogenic herpesviruses Kaposi sarcoma-associated herpesvirus and Epstein-Barr virus. Nelfinavir is being studied for the prevention and treatment of a wide range of malignancies in persons with and without HIV infection. SUMMARY: Nelfinavir and other protease inhibitors are well tolerated, oral drugs that have promising antitumor properties, and may prove to play an important role in the prevention and treatment of several cancers. Additional insights into protease inhibitors' mechanisms of action may lead to the development of novel cancer chemotherapy agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".