Pitavastatin Calcium Confers Fungicidal Properties to Fluconazole by Inhibiting Ubiquinone Biosynthesis and Generating Reactive Oxygen Species
Bibliographic record
Abstract
Fluconazole (FLC) is extensively employed for the prophylaxis and treatment of invasive fungal infections (IFIs). However, the fungistatic nature of FLC renders pathogenic fungi capable of developing tolerance towards it. Consequently, converting FLC into a fungicidal agent using adjuvants assumes significance to circumvent FLC resistance and the perpetuation of fungal infections. This drug repurposing study has successfully identified pitavastatin calcium (PIT) as a promising adjuvant for enhancing the fungicidal activity of FLC from a comprehensive library of 2372 FDA-approved drugs. PIT could render FLC fungicidal even at concentrations as low as 1 μM. The median lethal dose (LD50) of PIT was determined to be 103.6 mg/kg. We have discovered that PIT achieves its synergistic effect by inhibiting the activity of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, thereby impeding ubiquinone biosynthesis, inducing reactive oxygen species (ROS) generation, triggering apoptosis, and disrupting Golgi function. We employed a Candida albicans strain that demonstrated a notable tolerance to FLC to infect mice and found that PIT effectively augmented the antifungal efficacy of FLC against IFIs. This study is an illustrative example of how FDA-approved drugs can effectively eliminate fungal tolerance to FLC.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".