Targeting NMDA receptors in neuropsychiatric disorders by drug screening on human neurons derived from pluripotent stem cells
Bibliographic record
Abstract
NMDA receptors (NMDARs), a prominent subtype of glutamatergic receptors, are implicated in the pathogenesis and development of neuropsychiatric disorders such as epilepsy, intellectual disability, autism spectrum disorder, and schizophrenia, and are therefore a potential therapeutic target in treating these disorders. Neurons derived from induced pluripotent stem cells (iPSCs) have provided the opportunity to investigate human NMDARs in their native environment. In this review, we describe the expression, function, and regulation of NMDARs in human iPSC-derived neurons and discuss approaches for utilizing human neurons for identifying potential drugs that target NMDARs in the treatment of neuropsychiatric disorders. A challenge in studying NMDARs in human iPSC-derived neurons is a predominance of those receptors containing the GluN2B subunit and low synaptic expression, suggesting a relatively immature phenotype of these neurons and delayed development of functional NMDARs. We outline potential approaches for improving neuronal maturation of human iPSC-derived neurons and accelerating the functional expression of NMDARs. Acceleration of functional expression of NMDARs in human iPSC-derived neurons will improve the modeling of neuropsychiatric disorders and facilitate the discovery and development of novel therapeutics targeting NMDARs for the treatment of these disorders.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".