S25.2: Correcting the QT interval for changes in HR in pre‐clinical drug development
Bibliographic record
Abstract
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.Two well-known formulae (Bazett, 1920;Fridericia, 1920) are frequently used and have been proven useful with data from human beings.However, researchers have become aware of the fact that this does not hold for animal experiments.Different corrections have been proposed recently (e.g.Malik et al., 2002; Sarma et al., 1984).We investigate some of the models by comparing the outcomes of the analyses.The data is derived from telemetry measurements on Labrador dogs.Previous comparisons often stress only the fit of the model or the correlation between the corrected QT interval and heart rate.We do not think that this is sufficient to make a profound decision about which model to use.Instead, using control animals only, we propose the use of a measure of predictive performance.As a sufficiently large number of observations was available, the data was subdivided into a training and a test set.The first one serves to estimate the respective parameters while the second one is used to determine the performance of the model.Here, a kind of PRESS statistic is used.Next, the models were considered on treated animals, using the estimated parameters.Both positive and negative controls were considered.A reasonable correction should lead to a correct identification of possibly problematic prolongation of QT.In fact, only a few models under consideration were able to do so.Namely, these are the linear, the parabolic and the logarithmic model.The next steps in identifying the best correction will be to consider additional compounds as well as other species to validate our hitherto results.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".