Calcineurin Inhibitors Synergize with Manogepix to Kill Diverse Human Fungal Pathogens
Bibliographic record
Abstract
Invasive fungal infections have mortality rates of 30–90%, depending on patient co-morbidities and the causative pathogen. The frequent emergence of drug resistance reduces the efficacy of currently approved treatment options, highlighting an urgent need for antifungals with new modes of action. Addressing this need, fosmanogepix (N-phosphonooxymethylene prodrug of manogepix; MGX) is the first in a new class of gepix drugs, and acts as a broad-spectrum, orally bioavailable inhibitor of the essential fungal glycosylphosphatidylinositol (GPI) acyltransferase Gwt1. MGX inhibits the growth of diverse fungal pathogens and causes accumulation of immature GPI-anchored proteins in the fungal endoplasmic reticulum. Relevant to the ongoing clinical development of fosmanogepix, we report a synergistic, fungicidal interaction between MGX and inhibitors of the protein phosphatase calcineurin against important human fungal pathogens. To investigate this synergy further, we evaluated a library of 124 conditional expression mutants covering 95% of the genes encoding proteins involved in GPI-anchor biosynthesis or proteins predicted to be GPI-anchored. Strong negative chemical-genetic interactions between the calcineurin inhibitor FK506 and eleven GPI-anchor biosynthesis genes were identified, indicating that calcineurin signalling is required for fungal tolerance to not only MGX, but to inhibition of the GPI-anchor biosynthesis pathway more broadly. Depletion of these GPI-anchor biosynthesis genes, like MGX treatment, also exposed fungal cell wall (1→3)-β-D-glucans. Taken together, these findings suggest the increased risk of invasive fungal infections associated with use of calcineurin inhibitors as immunosuppressants may be mitigated by their synergistic fungicidal interaction with (fos)manogepix and its ability to enhance exposure of immunostimulatory glucans.
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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".