Abstract 2602: The cardiotoxicity and myocyte damage caused by small molecule anticancer tyrosine kinase inhibitors is correlated with lack of target specificity
Bibliographic record
Abstract
Abstract The use of the new anticancer tyrosine kinase inhibitors (TKI) has revolutionized the treatment of certain cancers. However, the use of some of these results in cardiotoxicity. Large scale profiling data recently made available for the binding of 7 of the 9 FDA-approved tyrosine kinase inhibitors to a panel of 317 kinases has allowed us to correlate kinase inhibitor binding selectivity scores with TKI-induced damage to neonatal rat cardiac myocytes. The 7 TKIs that were the subject of this study were: imatinib (Gleevec), sunitinib (Sutent), dasatinib (Sprycel), sorafenib (Nexavar), lapatinib (Tykerb), gefitinib (Iressa), and erlotinib (Tarceva). The tyrosine kinase selectivity scores, but not the serine-threonine kinase scores, were highly correlated with the myocyte damaging effects of the TKIs. Additionally, we showed that damage to myocytes gave a good rank order correlation with clinical cardiotoxicity. Finally, strength of TKI binding to colony stimulating factor 1 receptor (CSF1R) was highly correlated with myocyte damage, thus possibly implicating this kinase in contributing to TKI-induced cardiotoxicity. Support: CIHR and a Canada Research Chair in Drug Development to BBH. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2602.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".