The Anti-Cancer Properties of the HIV Protease Inhibitor Nelfinavir
Bibliographic record
Abstract
Traditional cancer treatments may lose efficacy following the emergence of novel mutations or the development of chemoradiotherapy resistance. Late diagnosis, high-cost of treatment, and the requirement of highly efficient infrastructure to dispense cancer therapies hinder the availability of adequate treatment in low-income and resource-limited settings. Repositioning approved drugs as cancer therapeutics may reduce the cost and timeline for novel drug development and expedite the availability of newer, efficacious options for patients in need. Nelfinavir is a human immunodeficiency virus (HIV) protease inhibitor that has been approved and is extensively used as an anti-infective agent to treat acquired immunodeficiency syndrome (AIDS). Yet nelfinavir has also shown anti-cancer effects in in vitro and in vivo studies. The anti-cancer mechanism of nelfinavir includes modulation of different cellular conditions, such as unfolded protein response, cell cycle, apoptosis, autophagy, the proteasome pathway, oxidative stress, the tumor microenvironment, and multidrug efflux pumps. Multiple clinical trials indicated tolerable and reversible toxicities during nelfinavir treatment in cancer patients, either as a monotherapy or in combination with chemo- or radiotherapy. Since orally available nelfinavir has been a safe drug of choice for both adult and pediatric HIV-infected patients for over two decades, exploiting its anti-cancer off-target effects will enable fast-tracking this newer option into the existing repertoire of cancer chemotherapeutics.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".