Inhibition of HCN1 currents by norquetiapine, an active metabolite of the atypical anti-psychotic drug quetiapine
Bibliographic record
Abstract
Quetiapine is a second-generation atypical antipsychotic drug that has been commonly prescribed for the treatment of schizophrenia, major depressive disorder (depression), and other psychological disorders. Targeted inhibition of hyperpolarization-activated cyclic-nucleotide gated (HCN) channels, which generate Ih, may provide effective resistance against schizophrenia and depression. We investigated if HCN channels could contribute to the therapeutic effect of quetiapine, and its major active metabolite norquetiapine. Two-electrode voltage clamp recordings were used to assess the effects of quetiapine and its active metabolites 7-hydroxyquetiapine and norquetiapine on currents from HCN1 channels expressed in Xenopus laevis oocytes. Norquetiapine, but not quetiapine nor 7-hydroxyquetiapine, has an inhibitory effect on HCN1 channels. Norquetiapine selectively inhibited HCN1 currents by shifting the voltage-dependence of activation to more hyperpolarized potentials in a concentration-dependent manner with an IC50 of 13.9 ± 0.8 μM for HCN1 and slowing channel opening, without changing the kinetics of closing. Inhibition by norquetiapine primarily occurs from in the closed state. Norquetiapine inhibition is not sensitive to the external potassium concentration, and therefore, likely does not block the pore. Norquetiapine inhibition also does not dependent on the cyclic-nucleotide binding domain. Norquetiapine also inhibited HCN4 channels with reduced efficacy than HCN1 and had no effect on HCN2 channels. Therefore, HCN channels are key targets of norquetiapine, the primary active metabolite of quetiapine. These data help to explain the therapeutic mechanisms by which quetiapine aids in the treatment of anxiety, major depressive disorder, bipolar disorder, and schizophrenia, and may represent a novel structure for future drug design of HCN inhibitors.
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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 | 0.000 | 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) | 0.001 | 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 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".