Toxicity testing and drug screening using iPSC-derived hepatocytes, cardiomyocytes, and neural cells
Bibliographic record
Abstract
Unexpected toxicity in areas such as cardiotoxicity, hepatotoxicity, and neurotoxicity is a serious complication of clinical therapy and one of the key causes for failure of promising drug candidates in development. Animal studies have been widely used for toxicology research to provide preclinical security evaluation of various therapeutic agents under development. Species differences in drug penetration of the blood-brain barrier, drug metabolism, and related toxicity contribute to failure of drug trials from animal models to human. The existing system for drug discovery has relied on immortalized cell lines, animal models of human disease, and clinical trials in humans. Moreover, drug candidates that are passed as being safe in the preclinical stage often show toxic effects during the clinical stage. Only around 16% drugs are approved for human use. Research on induced pluripotent stem cells (iPSCs) promises to enhance drug discovery and development by providing simple, reproducible, and economically effective tools for drug toxicity screening under development and, on the other hand, for studying the disease mechanism and pathways. In this review, we provide an overview of basic information about iPSCs, and discuss efforts aimed at the use of iPSC-derived hepatocytes, cardiomyocytes, and neural cells in drug discovery and toxicity 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".