Identification of novel myelin repair drugs by modulation of oligodendroglial differentiation competence
Bibliographic record
Abstract
BACKGROUND: In multiple sclerosis loss of myelin and oligodendrocytes impairs saltatory signal transduction and leads to neuronal loss and functional deficits. Limited capacity of oligodendroglial precursor cells to differentiate into mature cells is the main reason for inefficient myelin repair in the central nervous system. Drug repurposing constitutes a powerful approach for identification of pharmacological compounds promoting this process. METHODS: A phenotypic compound screening using the subcellular distribution of a potent inhibitor of oligodendroglial cell differentiation, namely p57kip2, as differentiation competence marker was conducted. Hit compounds were validated in terms of their impact on developmental cell differentiation and myelination using both rat and human primary cell cultures and organotypic cerebellar slice cultures, respectively. Their effect on spontaneous remyelination was then investigated following cuprizone-mediated demyelination of the corpus callosum. FINDINGS: A number of novel small molecules able to promote oligodendroglial cell differentiation were identified and a subset was found to foster human oligodendrogenesis as well as myelination ex vivo. Among them the steroid danazol and the anthelminthic parbendazole were found to increase myelin repair. INTERPRETATION: We provide evidence that early cellular processes involved in differentiation decisions are applicable for the identification of regeneration promoting drugs and we suggest danazol and parbendazole as potent therapeutic candidates for demyelinating diseases. FUNDING: This work was supported by the Jürgen Manchot Foundation, Düsseldorf; Research Commission of the Medical Faculty of Heinrich-Heine-University Düsseldorf; Christiane and Claudia Hempel Foundation; Stifterverband/Novartisstiftung; James and Elisabeth Cloppenburg, Peek and Cloppenburg Düsseldorf Stiftung and International Progressive MS Alliance (BRAVEinMS).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".