Ritonavir’s Evolving Role: A Journey from Antiretroviral Therapy to Broader Medical Applications
Bibliographic record
Abstract
Ritonavir is a protease inhibitor initially developed for HIV treatment that is now used as a pharmacokinetic booster for other antiretrovirals due to it being a cytochrome P450 3A4 enzyme and P-glycoprotein inhibitor. Consequently, ritonavir is of special interest for repurposing in other diseases. It had an important role in battling the COVID-19 pandemic as a part of the developed drug Paxlovid® in association with nirmatrelvir and has shown effects in hepatitis and other pathogenic diseases. Ritonavir has also shown promising results in overcoming drug resistance and enhancing the efficacy of existing chemotherapeutic agents in oncology. Evidence of cancer repurposing potential was demonstrated in cancers such as ovarian, prostate, lung, myeloma, breast, and bladder cancer, with several mechanisms of action presented. In vitro studies indicate that ritonavir alone can inhibit key pathways involved in cancer cell survival and proliferation, causing apoptosis, cell cycle arrest, endoplasmic reticulum stress, and metabolic stress due to the inhibition of molecules like heat shock protein 90 and cyclin-dependent kinases. Ritonavir also causes resistant cells to become sensitized to anticancer drugs like gemcitabine or docetaxel. These findings indicate that repurposing ritonavir, either on its own or in combination with other medications, could be a promising approach for treating various diseases. This is particularly relevant in cancer therapy, where ritonavir repurposing is the central focus of this review.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".