Human pluripotent stem cell (hPSC)-derived models for autism spectrum disorder drug discovery
Bibliographic record
Abstract
INTRODUCTION: Autism spectrum disorder (ASD) is a prevalent and complex neurodevelopmental disorder (NDD) with genetic and environmental origins. Currently, there are no effective pharmacological treatments targeting core ASD features. This leads to unmet medical needs of individuals with ASD and requires relevant human disease models recapitulating genetic and clinical heterogeneity to better understand underlying mechanisms and identify potential pharmacological therapies. Recent advancements in stem cell technology have enabled the generation of human pluripotent stem cell (hPSC)-derived two-dimensional (2D) and three-dimensional (3D) neural models, which serve as powerful tools for ASD modeling and drug discovery. AREAS COVERED: This article reviews the applications of hPSC-derived 2D and 3D neural models in studying various forms of ASD using pharmacological perturbation and drug screenings, highlighting the potential use of these models to develop novel pharmacological treatment strategies for ASD. EXPERT OPINION: hPSC-derived models recapitulate early human brain development spatiotemporally and have allowed patient-specific mechanistic investigation and therapeutic development using advanced molecular technologies, which will contribute to precision medicine for ASD therapy. Improvements are still required in hPSC-based models to further enhance their physiological relevance, clinical translation, and scalability for ASD drug discovery.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".