Novel treatments for autism spectrum disorder based on genomics and systems biology
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder with a complex underlying genetic architecture. There are currently no known pharmacologic treatments for the core ASD symptoms of social deficits and restricted/ repetitive behavior. However, there are dozens of clinical trials currently underway that are testing the impact of novel and existing agents on core and associated symptoms in ASD. METHODS: We present a narrative synthesis of the historical and contemporary challenges to drug discovery in ASD. We then provide an overview of novel treatments currently under investigation from a genomics and systems biology perspective. RESULTS: Data driven network and cluster analyses suggest alterations in transcriptional regulation, chromatin remodelling, synaptic transmission, neuropeptide signalling, and/or immunological mechanisms may contribute to or underlie the development of ASD. Agents and upcoming trials targeting each of the above listed systems are reviewed. CONCLUSION: Identifying effective pharmacologic treatments for the core and associated symptom domains in ASD will require further collaboration and innovation in the areas of outcome measurement, biomarker research, and genomics, as well as systematic efforts to identify and treat subgroups of individuals with ASD who may be differentially responsive to specific treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".