Recent advances in 3D bioprinted neural models: A systematic review on the applications to drug discovery
Bibliographic record
Abstract
The design of neural tissue models with architectural and biochemical relevance to native tissues opens the way for the fundamental study and development of therapies for many disorders with limited treatment options. Here, we systematically review the most recent literature on 3D bioprinted neural models, including their potential for use in drug screening. Neural tissues that model the central nervous system (CNS) from the relevant literature are reviewed with comprehensive summaries of each study, and discussion of the model types, bioinks and additives, cell types used, bioprinted construct shapes and culture time, and the characterization methods used. In this review, we accentuate the lack of standardization among characterization methods to analyze the functionality (including chemical, metabolic and other pathways) and mechanical relevance of the 3D bioprinted constructs, and discuss this as a critical area for future exploration. These gaps must be addressed for this technology to be applied for effective drug screening applications, despite its enormous potential for rapid and efficient drug screening. The future of biomimetic, 3D printed neural tissues is promising and evaluation of the in vivo relevance on multiple levels should be sought to adequately compare model performance and develop viable treatment options for neurodegenerative diseases, or other conditions that affect the CNS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".