Correcting the QT Interval for Changes in HR in Pre-clinical Drug Development
Bibliographic record
Abstract
OBJECTIVES: Estimation of possible cardiovascular side effects belongs to the safety assessment of every drug candidate. Drug-induced prolongation of the QT interval can result in life-threatening ventricular arrhythmia. In pre-clinical drug development, animal experiments are used to study this possible effect. Researchers have become aware that correction formulae derived for human beings are not applicable to animal experiments. METHODS: We investigated some of the proposed models by comparing the outcomes of the analyses on the same data. The data was derived from telemetry measurements on Labrador dogs. We propose the use of both the correlation with heart rate (or RR interval) and a measure of predictive performance. As a sufficiently large number of observations were available, the data was subdivided into a training and a test set. The training set serves to estimate the respective parameters while the test set is used to determine the performance of the model. Here, a kind of PRESS statistic was used. Next, the models were considered for treated animals, using the estimated parameters. Both positive and negative controls were used. CONCLUSIONS: Most models under consideration performed quite well. These models eliminated the correlation for the most part and were reasonably predictive. Furthermore, they reliably differentiate between positive and negative controls. The next steps in identifying the best correction will be to consider additional compounds as well as other species to validate our current results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".