Cellular models for discovering prion disease therapeutics: Progress and challenges
Bibliographic record
Abstract
Prions, which cause fatal neurodegenerative disorders such as Creutzfeldt-Jakob disease, are misfolded and infectious protein aggregates. Currently, there are no treatments available to halt or even delay the progression of prion disease in the brain. The infectious nature of prions has resulted in animal paradigms that accurately recapitulate all aspects of prion disease, and these have proven to be instrumental for testing the efficacy of candidate therapeutics. Nonetheless, infection of cultured cells with prions provides a much more powerful system for identifying molecules capable of interfering with prion propagation. Certain lines of cultured cells can be chronically infected with various types of mouse prions, and these models have been used to unearth candidate anti-prion drugs that are at least partially efficacious when administered to prion-infected rodents. However, these studies have also revealed that not all types of prions are equal, and that drugs active against mouse prions are not necessarily effective against prions from other species. Despite some recent progress, the number of cellular models available for studying non-mouse prions remains limited. In particular, human prions have proven to be particularly challenging to propagate in cultured cells, which has severely hindered the discovery of drugs for Creutzfeldt-Jakob disease. In this review, we summarize the cellular models that are presently available for discovering and testing drugs capable of blocking the propagation of prions and highlight challenges that remain on the path towards developing therapies for prion disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".