ArticleFrontiers in physiology2017
Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics.
Article in Frontiers in physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 30 citations in OpenAlex.
- Osimertinib induces reversible cardiac dysfunction through the GATA4-MYLK3-MYL2 axis.European heart journal · 2026Article
- SIRT7 upregulation protects against TKI-induced cardiotoxicity by inhibiting MCU-mediated mitochondrial calcium overload.Frontiers in pharmacology · 2026Article
- Insights and perspectives into the etiology of TKI-induced cardiotoxicity.ASPET discovery · 2026Article
- Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?BMC medical informatics and decision making · 2025Article
- Adverse effects of tyrosine kinase inhibitors in cancer therapy: pathophysiology, mechanisms and clinical management.Signal transduction and targeted therapy · 2023Review
- Association between Cardiovascular Response and Inflammatory Cytokines in Non-Small Cell Lung Cancer Patients.Journal of cardiovascular development and disease · 2023Article
- Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease.Journal of personalized medicine · 2023Review
- Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.Briefings in bioinformatics · 2023Article
- Successful Treatment with Low-dose Crizotinib in a Patient with ROS1-rearranged Lung Cancer Who Developed Crizotinib-induced Heart Failure.Internal medicine (Tokyo, Japan) · 2023Article
- Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors.Frontiers in pharmacology · 2023Article
- A novel cardiovascular systems model to quantify drugs effects on the inter-relationship between contractility and other hemodynamic variables.CPT: pharmacometrics & systems pharmacology · 2022Article
- Combination ofFrontiers in pharmacology · 2022Article
- Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity.Journal of molecular and cellular cardiology · 2021Article
- Network model-based screen for FDA-approved drugs affecting cardiac fibrosis.CPT: pharmacometrics & systems pharmacology · 2021Article
- Does the Heart Want What It Wants? A Case for Self-Adapting, Mechano-Sensitive Therapies After Infarction.Frontiers in cardiovascular medicine · 2021Article
- Adverse cardiac effects of cancer therapies: cardiotoxicity and arrhythmia.Nature reviews. Cardiology · 2020Review
- Cardiovascular disease models: A game changing paradigm in drug discovery and screening.Biomaterials · 2019Review
- A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows.PLoS computational biology · 2019Article
- Bridging the Data Gap FromFrontiers in public health · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation-contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.