Pharmacodynamics of ATI-2307 in a rabbit model of cryptococcal meningoencephalitis
Bibliographic record
Abstract
ABSTRACT Cryptococcal meningoencephalitis (CM) is a devastating fungal disease with high morbidity and mortality. The current regimen that is standard-of-care involves a combination of three different drugs administered for up to one year. There is a critical need for new therapies due to both toxicity and inadequate fungicidal activity of the currently available antifungal drugs. ATI-2307 is a novel aryl amidine that disrupts the mitochondrial membrane potential and inhibits the respiratory chain complexes of fungi—it thus represents a new mechanism for direct antifungal action. Furthermore, ATI-2307 selectively targets fungal mitochondria via a fungal-specific transporter that is not present in mammalian cells. It has very potent in vitro anticryptococcal activity. In this study, the efficacy of ATI-2307 was tested in a rabbit model of CM. ATI-2307 demonstrated significant fungicidal activity at dosages between 1 and 2 mg/kg/d, and these results were superior to fluconazole and similar to amphotericin B treatment. When ATI-2307 was combined with fluconazole, the antifungal effect was greater than either therapy alone. While ATI-2307 has potent anticryptococcal activity in the subarachnoid space, its ability to reduce yeasts in the brain parenchyma was relatively less over the same study period. This new drug, with its unique mechanism of fungicidal action and ability to positively interact with an azole, has demonstrated sufficient anticryptococcal potential in this experimental setting to be further evaluated in clinical studies.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".