Organotypic and Microphysiological Human Tissue Models for Drug Discovery and Development—Current State-of-the-Art and Future Perspectives
Bibliographic record
Abstract
The number of successful drug development projects has been stagnant for decades despite major breakthroughs in chemistry, molecular biology, and genetics. Unreliable target identification and poor translatability of preclinical models have been identified as major causes of failure. To improve predictions of clinical efficacy and safety, interest has shifted to three-dimensional culture methods in which human cells can retain many physiologically and functionally relevant phenotypes for extended periods of time. Here, we review the state of the art of available organotypic culture techniques and critically review emerging models of human tissues with key importance for pharmacokinetics, pharmacodynamics, and toxicity. In addition, developments in bioprinting and microfluidic multiorgan cultures to emulate systemic drug disposition are summarized. We close by highlighting important trends regarding the fabrication of organotypic culture platforms and the choice of platform material to limit drug absorption and polymer leaching while supporting the phenotypic maintenance of cultured cells and allowing for scalable device fabrication. We conclude that organotypic and microphysiological human tissue models constitute promising systems to promote drug discovery and development by facilitating drug target identification and improving the preclinical evaluation of drug toxicity and pharmacokinetics. There is, however, a critical need for further validation, benchmarking, and consolidation efforts ideally conducted in intersectoral multicenter settings to accelerate acceptance of these novel models as reliable tools for translational pharmacology and toxicology. SIGNIFICANCE STATEMENT: Organotypic and microphysiological culture of human cells has emerged as a promising tool for preclinical drug discovery and development that might be able to narrow the translation gap. This review discusses recent technological and methodological advancements and the use of these systems for hit discovery and the evaluation of toxicity, clearance, and absorption of lead compounds.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".