A clinically relevant selective ERK-pathway inhibitor reverses core deficits in a mouse model of autism
Bibliographic record
Abstract
BACKGROUND: Extracellular signal-regulated kinase (ERK/MAPK) pathway in the brain is hypothesized to be a critical convergent node in the development of autism spectrum disorder. We reasoned that selectively targeting this pathway could reverse core autism-like phenotype in animal models. METHODS: Here we tested a clinically relevant, selective inhibitor of ERK pathway, PD325901 (Mirdametinib), in a mouse model of idiopathic autism, the BTBR mice. FINDINGS: We report that treating juvenile mice with PD325901 reduced ERK pathway activation, dose and duration-dependently reduced core disease-modeling deficits in sociability, vocalization and repetitive behavior, and reversed abnormal EEG signals. Further analysis revealed that subchronic treatment did not affect weight gain, locomotion, or neuronal density in the brain. Parallel treatment in the C57BL/6J mice did not alter their phenotype. INTERPRETATION: Our data indicate that selectively inhibiting ERK pathway using PD325901 is beneficial in the BTBR model, thus further support the notion that ERK pathway is critically involved in the pathophysiology of autism. These results suggest that a similar approach could be applied to animal models of syndromic autism with dysregulated ERK signaling, to further test selectively targeting ERK pathway as a new approach for treating autism. FUNDING: This has beenwork was supported by Alberta Children's Hospital Research Foundation (JMR & NC), University of Calgary Faculty of Veterinary Medicine (NC), Kids Brain Health Network (NC), and Natural Sciences and Engineering Research Council of Canada (NC).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".