Application of Fluorescence- and Bioluminescence-Based Biosensors in Cancer Drug Discovery
Bibliographic record
Abstract
Recent advances in drug discovery have established biosensors as indispensable tools, particularly valued for their precision, sensitivity, and real-time monitoring capabilities. The review begins with a brief overview of cancer drug discovery, underscoring the pivotal role of biosensors in advancing cancer research. Various types of biosensors employed in cancer drug discovery are then explored, with particular emphasis on fluorescence- and bioluminescence-based technologies such as FRET, TR-FRET, BRET, NanoBRET, and NanoBiT. These biosensors have enabled breakthrough discoveries, including the identification of Celastrol as a novel YAP-TEAD inhibitor through NanoBiT-based screening, and the development of TR-FRET assays that successfully identified Ro-31-8220 as a SMAD4R361H/SMAD3 interaction inducer. The integration of biosensors in high throughput screening and validation for cancer drug compounds is examined, highlighting successful applications such as the development of LATS biosensors that revealed VEGFR as an upstream regulator of the Hippo signaling pathway. Real-time monitoring of cellular responses through biosensors has yielded invaluable insights into cancer cell signaling pathways, as demonstrated by NanoBRET assays detecting RAF dimerization and HiBiT systems monitoring protein degradation dynamics. The review addresses challenges linked to biosensor applications, such as maintaining stability in complex tumor microenvironments and achieving consistent sensitivity in HTS applications. Emerging trends are discussed, including integrating artificial intelligence and advanced nanomaterials for enhanced biosensor performance. In conclusion, this review offers a comprehensive analysis of fluorescence- and bioluminescence-based biosensor applications in the dynamic cancer drug discovery field, presenting quantitative evidence of their impact and highlighting their potential to revolutionize targeted cancer treatments.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".