Screening for Inhibitors of Microglia to Reduce Neuroinflammation
Bibliographic record
Abstract
BACKGROUND: Despite the significant role microglia play in the pathology of multiple sclerosis (MS), medications that act within the central nervous system (CNS) to inhibit microglia have not yet been identified as treatment options. OBJECTIVE: We screened 1040 compounds with the aim of identifying inhibitors of microglia to reduce neuroinflammation. METHODS: The NINDs collection of 1040 compounds, where most are therapeutic medications, was tested at 10 µM final concentration on lipopolysaccharide (LPS)-activated human microglia. An ELISA was run on the media to measure the level of TNF-α as an indicator of microglia activity. For compounds that reduce LPS-activated TNF-α levels by over 50%, considered as a potential inhibitor of interest, toxicity tests were conducted to exclude non-specific cytotoxicity. Promising compounds were subjected to further analyses, including toxicity to other CNS cell types, and multiplex assays. RESULTS: Of 1040 compounds tested, 123 reduced TNF-α levels of LPS-activated microglia by over 50%. However, most of these were cytotoxic to microglia at the concentration tested while 54 were assessed to be non-toxic. Of the latter, spironolactone was selected for further analyses. Spironolactone reduced TNF-α levels of activated microglia by 50-60% at 10 µM, and this concentration did not kill microglia, neurons or astrocytes. In multiplex assays, spironolactone reduced several molecules in activated microglia. Finally, during the screening, we identified 9 compounds that elevated further the TNF-α levels in LPS-activated microglia. CONCLUSION: Many of the non-toxic compounds identified in this screen as inhibitors of microglia, including spironolactone, may be explored as viable therapeutic options in MS.
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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 | 0.002 |
| 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 | 0.001 | 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".