Confounders and Pharmacological Characterization When Using the QT, JTp, and Tpe Intervals in Beagle Dogs
Bibliographic record
Abstract
INTRODUCTION: Corrected QT (QTc) interval is an essential proarrhythmic risk biomarker, but recent data have identified limitations to its use. The J to T-peak (JTp) interval is an alternative biomarker for evaluating drug-induced proarrhythmic risk. The aim of this study was to evaluate pharmacological effects using spatial magnitude leads and DII electrocardiogram (ECG) leads and common ECG confounders (ie, stress and body temperature changes) on covariate adjusted QT (QTca), covariate adjusted JTp (JTpca), and covariate adjusted T-peak to T-end (Tpeca) intervals. METHODS: Beagle dogs were exposed to body hyper- (42 °C) or hypothermic (33 °C) conditions or were administered epinephrine to assess confounding effects on heart rate corrected QTca, JTpca, and Tpeca intervals. Dofetilide (0.1, 0.3, 1.0 mg/kg), ranolazine (100, 140, 200 mg/kg), and verapamil (7, 15, 30, 43, 62.5 mg/kg) were administered to evaluate pharmacological effects. RESULTS: Covariate adjusted QT (slope -12.57 ms/°C) and JTpca (-14.79 ms/°C) were negatively correlated with body temperature but Tpeca was minimally affected. Epinephrine was associated with QTca and JTpca shortening, which could be related to undercorrection in the presence of tachycardia, while minimal effects were observed for Tpeca. There were no significant ECG change following ranolazine administration. Verapamil decreased QTca and JTpca intervals and increased Tpeca, whereas dofetilide increased QTca and JTpca intervals but had inconsistent effects on Tpeca. CONCLUSION: Results highlight potential confounders on QTc interval, but also on JTpca and Tpeca intervals in nonclinical studies. These potential confounding effects may be relevant to the interpretation of ECG data obtained from nonclinical drug safety studies with Beagle dogs.
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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) | 0.000 | 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".