High-Throughput Screening for Kv1.3 Channel Blockers Using an Improved FLIPR-Based Membrane-Potential Assay
Bibliographic record
Abstract
Voltage-gated K(+) channels are potential drug targets for an increasing number of disease indications. Searching for compounds that modulate K(+) channel activities by high-throughput screening (HTS) is becoming a standard approach in the drug discovery effort. Here the authors report an improved fluorometric imaging plate reader (FLIPR) membrane potential assay for Kv1.3 K(+) channel HTS. They have found that the Chinese hamster ovary (CHO) cells have endogenous membrane electrogenic transporters that contribute to maintaining membrane potential. Blocking the recombinant K(+) channels in the overexpressing CHO cell line hardly changed the membrane potential. Inhibition of the endogenous transporters is essential to achieve the required assay robustness. The authors identified the optimal assay conditions and designed a simple assay format. After an HTS campaign using this assay, various chemical series of Kv1.3 channel blockers have been identified and confirmed by the automated electrophysiological IonWorks assay. The correlation in dose response between FLIPR and IonWorks was established by biophysical modeling and experimental data. After characterization using patch-clamp recording, both use-dependent and use-independent compounds were identified. Some compounds possess nanomolar potency, indicating that the FLIPR assay is effective for successfully identifying K(+) channel blockers as novel drug candidates.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".