Update on Preclinical Models of Cancer Therapy-Related Cardiac Dysfunction: Challenges and Perspectives. A Scientific Statement of the Heart Failure Association (HFA) of the ESC, the ESC Council of Cardio-Oncology, and the ESC Working Group on Cellular Biology of the Heart
Bibliographic record
Abstract
New anticancer therapies with potential cardiovascular side effects are continuously being introduced into clinical practice, with new and often unexpected toxicities becoming apparent only after clinical introduction. These unknown toxicities should be identified and understood beforehand to better prepare patients and physicians, enabling the implementation of effective treatments. Therefore, there is a crucial need for appropriate preclinical models to understand the biological basis of their cardiotoxicity. This scientific statement summarizes the preclinical models hitherto used, from in vitro two- and three-dimensional human systems to small and large animals, to pinpoint the molecular mechanisms behind the cardiotoxicity of new-generation anticancer therapies, particularly immunotherapies, and to develop potential cardioprotective strategies. Furthermore, it discusses how preclinical models have contributed to the provocative concept of heart failure being potentially tumorigenic and how the discovery of drugs with both anticancer and cardioprotective actions has revealed a common mechanistic basis for heart failure and cancer. Finally, it discusses the existing gaps between preclinical models and clinical observations in patients, how these discrepancies affect regulatory pathways and the drug development process in cardio-oncology and provides recommendations for closing these gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".