A Cysteine Protease Inhibitor Rescues Mice from a Lethal Cryptosporidium parvum Infection
Bibliographic record
Abstract
Cryptosporidiosis, caused by the protozoan parasite Cryptosporidium parvum, can stunt infant growth and can be lethal in immunocompromised individuals. The most widely used drugs for treating cryptosporidiosis are nitazoxanide and paromomycin, although both exhibit limited efficacy. To investigate an alternative approach to therapy, we demonstrate that the clan CA cysteine protease inhibitor N-methyl piperazine-Phe-homoPhe-vinylsulfone phenyl (K11777) inhibits C. parvum growth in mammalian cell lines in a concentration-dependent manner. Further, using the C57BL/6 gamma interferon receptor knockout (IFN-γR-KO) mouse model, which is highly susceptible to C. parvum, oral or intraperitoneal treatment with K11777 for 10 days rescued mice from otherwise lethal infections. Histologic examination of untreated mice showed intestinal inflammation, villous blunting, and abundant intracellular parasite stages. In contrast, K11777-treated mice (210 mg/kg of body weight/day) showed only minimal inflammation and no epithelial changes. Three putative protease targets (termed cryptopains 1 to 3, or CpaCATL-1, -2, and -3) were identified in the C. parvum genome, but only two are transcribed in infected mammals. A homology model predicted that K11777 would bind to cryptopain 1. Recombinant enzymatically active cryptopain 1 was successfully targeted by K11777 in a competition assay with a labeled active-site-directed probe. K11777 exhibited no toxicity in vitro and in vivo, and surviving animals remained free of parasites 3 weeks after treatment. The discovery that a cysteine protease inhibitor provides potent anticryptosporidial activity in an animal model of infection encourages the investigation and development of this biocide class as a new, and urgently needed, chemotherapy for cryptosporidiosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".