Ion Channel Screening
Bibliographic record
Abstract
Ion channels are attractive targets for drug discovery with recent estimates indicating that voltage and ligand-gated channels account for the third and fourth largest gene families represented in company portfolios after the G protein coupled and nuclear hormone receptor families. A historical limitation on ion channel targeted drug discovery in the form of the extremely low throughput nature of the gold standard assay for assessing functional activity, patch clamp electrophysiology in mammalian cells, has been overcome by the implementation of multi-well plate format cell-based screening strategies for ion channels. These have taken advantage of various approaches to monitor ion flux or membrane potential using radioactive, non-radioactive, spectroscopic and fluorescence measurements and have significantly impacted both high-throughput screening and lead optimization efforts. In addition, major advances have been made in the development of automated electrophysiological platforms to increase capacity for cell-based screening using formats aimed at recapitulating the gold standard assay. This review addresses the options available for cell-based screening of ion channels with examples of their utility and presents case studies on the successful implementation of high-throughput screening campaigns for a ligand-gated ion channel using a fluorescent calcium indicator, and a voltage-gated ion channel using a fluorescent membrane potential sensitive dye.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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".