๐Ÿ“’ Shim 2017

Mechanistic systems modeling to improve understanding and prediction of cardiotoxicity caused by targeted cancer therapeutics1



  • Tyrosine kinase inhibitors (TKIs), anticancer durgs, have cardiotoxicity: heart failure, cardiomyopathy, conduction abnormalities, QT prolongation, and myocardial injury
  • cardio-oncology: antineoplastic medication causing cardiotoxicity
  • on-target toxicity (TKs crucial to cardiomyocytes) & off-target toxicity (other kinases)
  • To research:
    • TKIs affect signaling networks in cardiomyocytes
    • Find interventions that can reverse and/or mitigate any associated cardiotoxicity
  • quantitative systems pharmacology (QSP) approach
  • chemical classes of TKIs
    1. humanized monoclonal antibodies (mAbs)
    2. Small molecule TKIs

Mechanisms underlying cardiotoxicity caused by TKIs

Survival signaling

  • ERBB2, target of trastuzumab, plays an important role in maintaining cardiomyocyte health
  • ERBB1, target of erlotinib, has also been implicated in cardioprotection and myocyte survival
  • The PI3K-Akt axis, downstream of ERBB, is critical in survival signaling
  • Raf-1 (serine/threonine kinases): cardiotoxicity caused by sorafenib inhibition of Raf-1 and then disinhibition of pro-apoptotic kinases

Sarcoplasmic reticulum and mitochondrial homeostasis

  • mitochondrial dysfunction resulting from TKI treatment can lead to membrane permeabilization and the release of reactive oxidative species (ROS) to the cytoplasm
  • then SR dysfunction through both altered Ca2+ release
  • e.g. imatinib => dilated SR structures, ฮ”ฮจ collapse
  • sunitinib => off-target inhibition of AMPK, ATP depletion

Excitation and contraction

  • direct or indirect modulation of cardiac ionic currents, resulting in pro-arrhythmic electrical activity

    • Direct decrease in hERG K+ (repolarizing) currents => QTc prolong
    • Inhibition of Src also induce the above
  • structural remodeling that leads to altered myocyte contraction

    • Src inhibition
    • Src is critival in organization of sarcomeres and the formation of focal adhesions that connect adjacent cell

Complexities of TKI-induced cardiotoxicity and the need for a systems approach

  • it would be beneficial to develop a systematic strategy to
    1. evaluate the potential cardiotoxicity of new TKIs;
    2. predict the mostly likely mechanisms involved
    3. suggest strategies for mitigating and/or reversing toxicity.
  • cell based, high-throughput drug screening assay
    • Using induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs)
    • limited insight into the dynamics of toxicity development (one cannot measure all the things!). The mechanistic details often remain hidden even the results are clear.

Mechanistic mathematical modeling (QSP models) to improve toxicity testing

  • QSP models have been exploited to understand cardiotoxicity caused by anthracyclines (de Oliveira et al, 2016)
  • An example of the success of QSP models: DILIsym for hepatotoxicity

Initial efforts to use QSP approaches to understand TKI-induced cardiotoxicity

  • tyrosine kinase signaling encompasses large, complex networks with numerous feedback loops,
  • โ€œtoxicity indexโ€ 2: effects of TKIs in hiPSC-CMs through several assays. QSP is complementary to this data-driven model.
  • ฮฒ-adrenergic receptor agonist isoproterenol => apoptosis3. simulations often generate novel experimentally-testable predictions
  • DtoxSโ€”Drug Toxicity Signature Generation Centerโ€”SOP,
  • mRNA levels => model parameters
  • CMC signaling model4
    • many known TKI targets (e.g., ERBB2, Raf-1) and critical nodes in cardiac survival signaling (e.g., PI3K, Akt, and ERK) are included
  • BNP (Brain Natriuretic Peptide) as heart failure biomarker. Nilotinib increased BNP level of CMCs when in stimuli
  • In myocytes that are not exposed to a physiological stimulus, TKIs and non-TKIs cause similar changes in hypertrophy biomarkers. Under stimulations from isoproterenol or endothelin-1, the pro-hypertrophic response is exaggerated in TKI-treated cells, compared with non-TKI-treated ones.

Benefits of simulations

  1. Making predictions from existing data
  2. Quantitative (dose-response) results to prioritize experimental tests and use resources efficiently
  3. predict the mechanisms responsible for the differences between indivisuals and/or drug classes

Future directions

  • describing direct inhibition of kinase activity by drugs (RNA expr only for now)
  • additional biological processes potentially involved in cardiotoxicity
    • e.g. models describing mitochondrial function, including the production of reactive oxygen species


The initial results presented here show how mechanistic models can be integrated with โ€œomicsโ€ measurements such as mRNA-seq, generating simulations that can suggest underlying mechanisms and help in prioritizing costly experiments.


  1. Shim JV, Chun B, van Hasselt JGC, Birtwistle MR, Saucerman JJ, Sobie EA. Mechanistic systems modeling to improve understanding and prediction of cardiotoxicity caused by targeted cancer therapeutics. Front. Physiol. 2017;8:651. doi:10.3389/fphys.2017.00651. PMC5599787 ↩︎

  2. Sharma A, Burridge PW, McKeithan WL, et al. High-throughput screening of tyrosine kinase inhibitor cardiotoxicity with human induced pluripotent stem cells. Sci Transl Med. 2017;9(377):eaaf2584. PMC5409837 ↩︎

  3. Shin SY, Kim T, Lee HS, et al. The switching role of ฮฒ-adrenergic receptor signalling in cell survival or death decision of cardiomyocytes. Nat Commun. 2014;5:5777. Published 2014 Dec 17. doi:10.1038/ncomms6777 PMC4284638 ↩︎

  4. Ryall KA, Holland DO, Delaney KA, Kraeutler MJ, Parker AJ, Saucerman JJ. Network reconstruction and systems analysis of cardiac myocyte hypertrophy signaling. J Biol Chem. 2012;287(50):42259-68. PMC3516769 ↩︎