Should You Run a Dedicated TQT Study? Sponsor and Regulatory Considerations on Substitution Pathways to Assess QT Liability
Bibliographic record
Abstract
Cardiac safety regulatory guidance for drug development has undergone several monumental shifts over the past decade as technological advancements, analysis models and study best practices have transformed this landscape. Once, clinical proarrhythmic risk assessment of a new chemical entity (NCE) was nearly exclusively evaluated in a dedicated thorough QT (TQT) study. However, since the introduction of the International Council for Harmonisation (ICH) E14/S7B Q&A 5.1 and 6.1 TQT substitutions, drug developers are offered an alternative pathway to evaluate proarrhythmic risk during an ascending dose study in healthy volunteers or during a powered patient study, respectively. In addition, the findings as well as the manner in which nonclinical studies are conducted (i.e., utilizing best practices) can dictate the need for a positive control in the clinical study and/or affect the labeling outcome. Drug sponsors are now faced with the option of pursuing a dedicated TQT study or requesting a TQT substitution. Potential factors influencing the choice of pathway include the NCE mechanism of action, pharmacokinetic properties, and safety profile, as well as business considerations. This tutorial will highlight the regulatory framework for integrated arrhythmia risk prediction models to outline drug safety, delineate potential reasons why a TQT substitution request may be rejected and discuss when a standalone TQT is recommended.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".