A new paradigm for drug-induced torsadogenic risk assessment using human iPS cell-derived cardiomyocytes
Bibliographic record
Abstract
INTRODUCTION: Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are anticipated to be a useful tool for conducting proarrhythmia risk assessments of drug candidates. However, a torsadogenic risk prediction paradigm using hiPSC-CMs has not yet been fully established. METHODS: Extracellular field potentials (FPs) were recorded from hiPSC-CMs using the multi-electrode array (MEA) system. The effects on FPs were evaluated with 60 drugs, including 57 with various clinical torsadogenic risks. Actual drug concentrations in medium were measured using the equilibrium dialysis method with a Rapid Equilibrium Dialysis device. Relative torsade de pointes (TdP) scores were determined for each drug according to the degree of FP duration prolongation and early afterdepolarization occurrence. The margins were calculated from the free concentration in medium and free effective therapeutic plasma concentration. Each drug's results were plotted on a two-dimensional map of relative TdP risk scores versus margins. RESULTS: Each drug was categorised as high, intermediate, or low risk based on its location within predefined areas of the two-dimensional map. We categorised 19 drugs as high risk; 18 as intermediate risk; and 17 as low risk. We examined the concordance between our categorisation of high and low risk drugs against the torsadogenic risk categorisation in CredibleMeds®. Our system demonstrated high concordance, as reflected in a sensitivity of 81%, specificity of 87%, and accuracy of 83%. DISCUSSION: These results indicate that our torsadogenic risk assessment is reliable and has a potential to replace the hERG assay for torsadogenic risk prediction, however, this system needs to be improved for the accurate of prediction of clinical TdP risk. Here, we propose a novel drug induced torsadogenic risk categorising system using hiPSC-CMs and the MEA system.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".