A Fluorescence-Based High-Throughput Screening Assay for the Identification of T-Type Calcium Channel Blockers
Bibliographic record
Abstract
T-type voltage-gated Ca(2+) channels have been implicated in contributing to a broad variety of human disorders, including pain, epilepsy, sleep disturbances, cardiac arrhythmias, and certain types of cancer. However, potent and selective T-type Ca(2+) channel modulators are not yet available for clinical use. This may in part be due to their unique biophysical properties that have delayed the development of high-throughput screening (HTS) assays for identifying blockers. One notable challenge is that at the normal resting membrane potential (V(m)) of cell lines commonly utilized for drug screening purposes, T-type Ca(2+) channels are largely inactivated and thus cannot be supported by typical formats of functional HTS assays to both evoke and quantify the Ca(2+) channel signal. Here we describe a simple method that can successfully support a fluorescence-based functional assay for compounds that modulate T-type Ca(2+)channels. The assay functions by exploiting the pore-forming properties of gramicidin to control the cellular V(m) in advance of T-type Ca(2+) channel activation. Using selected ionic conditions in the presence of gramicidin, T-type Ca(2+) channels are converted from the unavailable, inactivated state to the available, resting state, where they can be subsequently activated by application of extracellular K(+). The fidelity of the assay has been pharmacologically characterized with sample T-type Ca(2+) channel blockers whose potency has been determined by conventional manual patch-clamp techniques. This method has the potential for applications in high-throughput fluorometric imaging plate reader (FLIPR(R), Molecular Devices, Sunnyvale, CA) formats with cell lines expressing either recombinant or endogenous T-type Ca(2+) channels.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".