Comparing Label-Free Biosensors for Pharmacological Screening With Cell-Based Functional Assays
Bibliographic record
Abstract
The diversity and impact of label-free technologies continues to expand in drug discovery. Two classes of label-free instruments, using either an electrical impedance-based or an optical-based biosensor, are now available for investigating the effects of ligands on cellular targets. Studies of GPCR function have been especially prominent with these instruments due to the importance of this target class in drug discovery. Although both classes of biosensors share similar high sensitivity to changes in cell shape and structure, it is unknown whether these biosensors yield similar results when comparing the same GPCR response. Furthermore, since cell morphology changes induced by GPCRs differ depending on which G-protein is activated, there is potential for these instruments to have differential sensitivities to G-protein signaling. Here 1 impedance (CellKey)- and 2 optical-based instruments (BIND and Epic) are compared using Gi-coupled (ACh M2), Gq-coupled (ACh M1), and Gs-coupled (CRF1) receptors. All 3 instruments were robust in agonist and antagonist modes yielding comparable potencies and assay variance. Both the impedance and optical biosensors showed similar high sensitivity for detecting an endogenous D1/D5 receptor response and a melanocortin-4 receptor inverse agonist (agouti-related protein). The impedance-based biosensor was uniquely able to qualitatively distinguish G-protein coupling and reveal dual signaling by CRF1. Finally, responses with a ligand-gated ion channel, TRPV1, were similarly detectable in each instrument. Thus, despite some differences, both impedance- and optical-based platforms offer robust live-cell, label-free assays well suited to drug discovery and typically yield similar pharmacological profiles for GPCR ligands.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".