Using QSP to predict cardiotoxicity caused by cancer drugs

Tyrosine kinase inhibitor drugs, or TKIs, have been highly effective at treating several types of cancer, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood.
We are exploring potential mechanisms of TKI-induced cardiotoxicity using a strategy that integrates several complementary approaches. The pipeline involves: (1) transcriptomics to quantify drug-induced changes in gene expression in stem cell-derived myocytes (iPSC-CMs); (2) mechanistic QSP modeling to predict subsequent changes in physiological dynamics; and (3) physiological measurements to confirm or refute model predictions. This QSP approach successfully predicted individual-specific TKI susceptibility whereby particular drugs were tolerated in one cell line but disrupted dynamics in another cell line.
Overall, the work offers new insight into cardiotoxicity caused by TKIs and illustrates a novel approach for integrating transcriptomic measurements and QSP models to generate experimentally testable, individual-specific predictions.