3D<i>in vitro</i>tissue models and their potential for drug screening
Bibliographic record
Abstract
INTRODUCTION: The development of one standard, simplified in vitro three-dimensional tissue model suitable to biological and pathological investigation and drug-discovery may not yet be feasible, but standardized models for individual tissues or organs are a possibility. Tissue bioengineering, while concerned with finding methods of restoring functionality in disease, is developing technology that can be miniaturized for high throughput screening (HTS) of putative drugs. Through collaboration between biologists, physicists and engineers, cell-based assays are expanding into the realm of tissue analysis. Accordingly, three-dimensional (3D) micro-organoid systems will play an increasing role in drug testing and therapeutics over the next decade. Nevertheless, important hurdles remain before these models are fully developed for HTS. AREAS COVERED: We highlight advances in the field of tissue bioengineering aimed at enhancing the success of drug candidates through pre-clinical optimization. We discuss models that are most amenable to high throughput screening with emphasis on detection platforms and data modeling. EXPERT OPINION: Modeling 3D tissues to mimic in-vivo architecture remains a major challenge. As technology advances to provide novel methods of HTS analysis, so do potential pitfalls associated with such models and methods. We remain hopeful that integration of biofabrication with HTS will significantly reduce attrition rates in drug development.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".