Pharmacological and Mechanistic Interventions for Cognitive Impairment Associated With Schizophrenia: A Review of Registered Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is characterized by positive, negative, and cognitive symptoms. Current pharmacological treatments often fail to address cognitive deficits. In this review of clinical trials, we aim to identify studies that explore neurobiological (non-psychological) strategies to address Cognitive Impairment Associated with Schizophrenia (CIAS). METHODS: A search of clinical trial databases was conducted through US National Institutes of Health's ClinicalTrials.gov and the World Health Organization's International Clinical Trials Registry Platform (ICTRP) on August 2, 2024, with complementary searches performed on July 4 and 10, 2025, for each respective database to update the results. RESULTS: We identified 510 relevant interventional studies that objectively measured cognitive performance. Most trials were conducted in the United States (36.4%) and focused on treatment (79%), with randomized designs (88%), investigating drugs (56%), devices (33%), and dietary supplements (10%). Of these trials, 17% reported positive pro-cognitive evidence. Glutamate modulators were the most studied drug category (63 trials), with positive results for sarcosine, BI425809 (Iclepertin), d-serine, d-cycloserine, and minocycline in small-scale trials, although the results were not replicated in larger studies. Nicotinic receptor modulators like ABT-126 and encenicline also showed some cognitive benefits. Device-based interventions, particularly rTMS and iTBS, demonstrated improvements in global cognition, working memory, attention, and processing speed in a subset of trials. CONCLUSION: In this comprehensive overview of clinical trials on pro-cognitive agents in schizophrenia, we identify emerging opportunities but also acknowledge a lack of replicated evidence. Despite extensive attempts to address CIAS, it remains an undertreated domain, and future trials should explore better ways to treat this important condition.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".