📒 Lancaster 2016

Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms1



  • drug‐induced Torsades de Pointes (TdP) remains a critical issue in drug development.
    • blockade of the KCNH2, or hERG (IKr)
  • Reduction in IKr increases the action potential duration (APD), which appears as an increased QT interval in the EKG
  • A major shortcoming of the hERG assay is the failure to account for multichannel drug effects.
  • The goal of this study was twofold
    1. to develop a classifier for improved prediction of drug torsadogenicity.
    2. apply our classifier to identify key cellular physiological differences between torsadogenic and nontorsadogenic drugs
  • Quantitative Systems Pharmacology approach : combined modeling of physiological dynamics with statistical analysis and machine‐learning


Human ventricular cell models simulate drug response

  • simulated the application of 86 drugs at effective free therapeutic plasma concentration (EFTPC) in three recent, independently formulated human ventricular myocyte models (TP, GPB, ORd)
  • 13 metrics in the AP and Ca trasient curves

A novel classification method identifies torsadogenic drugs

  • principal components (PCs) from the metrics: first three PCs describe 88.2% of the variance
  • clear division between torsadogenic and nontorsadogenic drugs
  • The drug scores in the PC space were used to train a support vector machine (SVM) classifier, with accuarcy of 87.2%. Area under ROC curve (auROC) was 0.963.
  • substantial improvement over classifications based on either hERG block or APD at 90% repolarization (APD90)

Ca2+ dynamics augment action potential duration to identify torsadogenic drugs

  • PC scores are less clear in mechanisms explanation
  • Classification based on specific simulated metrics rather than PC scores provides physiological insight into differences between drugs
  • two to three metrics may allow for accurate drug classification (from PC analysis)
  • APD at 50% repolarization (APD50) and diastolic (Ca2+)i => auROC = 0.962
  • drugs with dramatic AP prolongation are torsadogenic. diastolic (Ca2+)i provides the additional information necessary to classify the drugs.

Risk prediction is robust across a large range of doses

  • repeated the classification analysis after simulating each drug at 0.1, 1, 10, and 100 times typical clinical concentration (EFTPC)
  • dose‐dependence of each drug’s distance from the decision boundary
  • Amiodarone, imipramine, and solifenacin were close to the origin at EFTPC, but higher concentrations of these drugs moved them onto the correct, torsadogenic side of the decision boundary.
  • The classification algorithm correctly predicted the torsadogenicity of donepezil, previously regarded safe.

A synthetic population stratifies drug risk

  • generated a synthetic population of 24 individuals by randomly varying ionic current parameters and calibrating the population variability to experimental data
  • Simulating drug effects in a synthetic population allows us to compute probabilities of TdP risk
  • Ibutilide, a class III antiarrhythmic with a well‐established risk of Torsades, is predicted to be dangerous in the majority (71%) of individuals
  • nitrendipine is a dihydropyridine Ca2+‐channel blocker with no known risk of Torsades that is predicted to be safe in all individuals in the population
  • nilotinib: safe in the majority of individuals, but torsadogenic in a subset (12.5%).

Off‐target interactions influence drug risk

  • we generated 100 hypothetical drugs that alter the activity of nine ion channels, pumps and transporters distinct from those targeted by the original drug set.
  • predict the influence of each parameter on drug risk
  • alterations to the Na+‐Ca2+ exchanger have the largest effect on TdP risk, with increased current leading to decreased risk. The Na+‐K+ ATPase, background Ca2+ current and the SERCA pump followed in significance
  • increases in the activity of the Na+‐K+ ATPase and the Na+‐Ca2+ exchanger were protective, whereas decreases were predicted to be torsadogenic
  • slow delayed rectifier (GKs) and the inward rectifier (GK1): blocking either K+ current is predicted to be torsadogenic, and enhancing either is protective


  • Quantitative Systems Pharmacology (QSP) strategy, running extensive mechanistic simulations based on results from a large drug dataset.
  • provide deeper insight into the physiological mechanisms of drug action than the “snapshot” one obtains using high‐throughput measurements, such as ion channel block or altered gene expression
  • the value of simulations to not only predict cellular drug responses, but to direct experimental studies
  • we do not assume the primacy of particular physiological responses, such as AP prolongation. Instead, we calculate from simulations a wide range of metrics, and then systematically determine which are most informative
  • drug‐induced changes in diastolic (Ca2+) were nearly as important as changes in APD to differentiate between torsadogenic and nontorsadogenic drugs
  • Na+‐K+ ATPase, Na+‐Ca2+ exchanger, background Ca2+ current, and the SERCA pump as the most significant potential modulators of TdP risk
  • peak INa inhibition does not account for specific effects on the late Na+ current (INaL)
  • Our method also does not include drug effects on channel trafficking, which plays a role in the torsadogenesis of some drugs


Models and simulations

  • (1) OVVR14; (2) ten Tusscher and Panfilov12; and (3) Grandi, Pasqualini, and Bers
  • stimulated at rates of 2 Hz, 1 Hz, and 0.5 Hz to steady state
  • under 24 different conditions: at 3 pacing rates × 8 cell types


  • reported half‐maximal inhibitory concentrations (IC50) for channel inhibition. We obtained these data for 86 drugs published in the studies by Kramer et al. and Mirams et al. as training set.
  • We defined the torsadogenicity of the drugs in our training set using the CredibleMeds database (

Classification of drugs

  • 331 metrics => PCs => training a SVM classifier15 with a linear kernel


  1. Lancaster MC, Sobie EA. Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms. Clin. Pharmacol. Ther. 2016;100(4):371-379. doi:10.1002/cpt.367. ASCPT ↩︎