Comparing Label-Free Biosensors for Pharmacological Screening With Cell-Based Functional Assays
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it