iPSC-cardiomyocytes in the preclinical prediction of candidate pharmaceutical toxicity
Bibliographic record
Abstract
Many challenges remain in the preclinical evaluation, adjudication, and prioritization of novel compounds in therapeutic discovery pipelines. These obstacles are evident by the large number of candidate or lead compounds failing to reach clinical trials, significantly due to a lack of efficacy in the disease paradigm of interest and/or the presence of innate chemical toxicity. The consequential compound attrition in discovery pipelines results in added monetary and time costs, potential danger to patients, and a slowed discovery of true therapeutics. The low rate of successful translation calls for improved models that can recapitulate in vivo function in preclinical testing to ensure the removal of toxic compounds earlier in the discovery process, in particular for the assessment of cardiotoxicity, the leading cause of post-market drug withdrawal. With recent advances in the development of human Inducible pluripotent stem cell derived cardiomyocytes (iPSC-CMs), novel compounds can be assessed with better disease relevance while more accurately assessing human safety. In this review, we discuss the utility of iPSC-CMs in preclinical testing by taking advantage of the inherent ability to mimic CMs in vivo. We explore the similarities and differences in electrophysiology, calcium handling, cellular signaling, contractile machinery, and metabolism between iPSC-CMs and adult CMs as these complex coordinated functions directly relate to toxicity evaluation. We will highlight considerations when using iPSC-CMs, such as maturation protocols, to ensure a more representative phenotype of the adult human CM, and how different populations of CMs can affect results in compound testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".