Disentangling the fibrous microenvironment: designer culture models for improved drug discovery
Bibliographic record
Abstract
INTRODUCTION: Standard high-throughput screening (HTS) assays rarely identify clinically viable 'hits', likely because cells do not experience physiologically realistic culture conditions. The biophysical nature of the extracellular matrix has emerged as a critical driver of cell function and response and recreating these factors could be critically important in streamlining the drug discovery pipeline. AREAS COVERED: The authors review recent design strategies to understand and manipulate biophysical features of three-dimensional fibrous tissues. The effects of architectural parameters of the extracellular matrix and their resulting mechanical behaviors are deconstructed; and their individual and combined impact on cell behavior is examined. The authors then illustrate the potential impact of these physical features on designing next-generation platforms to identify drugs effective against breast cancer. EXPERT OPINION: Progression toward increased culture complexity must be balanced against the demanding technical requirements for high-throughput screening; and strategies to identify the minimal set of microenvironmental parameters needed to recreate disease-relevant responses must be specifically tailored to the disease stage and organ system being studied. Although challenging, this can be achieved through integrative and multidisciplinary technologies that span microfabrication, cell biology, and tissue engineering.
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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 | 0.001 | 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".