Drug-induced arrhythmogenesis. Prediction and Mechanisms Blanca Rodriguez and Computational Biology Group University of Oxford Feb 3 rd, 29
Work done by Esther Pueyo, Lucia Romero, Alberto Corrias, Martin Fink, David Gavaghan, Kevin Burrage Chaste Team www.comlab.ox.ac.uk/chaste/theteam) Funded by
Drug cardiotoxicity The Problem 2 million, 13 years Drug market approval (DiMasi et al., 23). Cardiotoxicity: unwanted side effects that can result in arrhythmia Torsades de Pointes Preclinical Assessment of Cardiotoxicity Recordings of the HERG current Action potential duration (at 1Hz) In vivo QT interval
Our goal To identify new biomarkers of drug-induced cardiotoxicity using computational techniques To develop the computational techniques and models required for the assessment of drug cardiotoxicity Collaboration Academic: University of Oxford, Universidad Politecnica de Valencia, CRS4 in Sardinia, University of Szeged Industrial: Fujitsu, Aureus, GSK, Novartis, Roche, Pfizer, Astrazeneca http://www.vph-predict.eu
V (mv) Drug-induced arrhythmia Early afterdepolarizations Dispersion of repolarization Repolarization reserve I CaL 6 4 2-2 -4-6 -8-1 17.4 17.7 18 18.3 18.6 time (s) I Ks I Kr Natural APD/QT variability (acquired and congenital) due to: variability in a particular ion channel expression variability in complex interplay during repolarization How can variability explain the probabilistic nature of drug-induced arrhythmias?
{Romero L, Pueyo E, Fink M, Rodriguez B, AJP, under review} Impact of Biological Variability on Biomarkers I CaL I Kr I Ks I K1 I NaK I NaCa Steady-state properties Restitution properties Sensitivity to ±3% changes Normalized Sensitivity APD rate adaptation Na and Ca rate dependence
{Romero L, Pueyo E, Fink M, Rodriguez B, AJP, under review} Impact of Biological Variability on Biomarkers I CaL I Kr I Ks I K1 I NaK I NaCa Steady-state properties Restitution properties Sensitivity to ±3% changes Normalized Sensitivity APD rate adaptation Na and Ca rate dependence
Impact of Biological Variability on Biomarkers Action potential duration Restitution curve Experimental range Experimental range
APD (ms) APD heart rate adaptation BCL=1ms I Kr I CaL I I Kr Ks I K1 I NaK I Ks NaCa NaCa BCL=6ms Simulation Experiments 3 Control 3% INaK Control.2 mm strophantin G 29 28 12 24 36 48 6 72 84 96 18 12 time (s) Control.6 mm strophantin G {Pueyo E, Baczko I, Laguna P, Varro A, Rodriguez B. Mechanisms of ventricular rate adaptation as a predictor of arrhythmic risk.}
Iks (pa/pf) IKr block and IKs stochastic behaviour Human ventricular model I CaL I Ks variability varies with location in the ventricles ( midmyocardial cells long APD) increases during 24h exposure to I Kr blocker {Xiao et al., 28} I Ks I Kr.4.2 Stochastic differential equation 92 93 94 95 96 time (s) Iks Small number of channels ion channel stochastic behaviour manifests itself at macroscopic ionic current level {Krogh-Madsen, 24}. How does noise in I Ks kinetics due to ion channel stochastic behaviour affect repolarization reserve?
V (mv) V (mv) I Kr Block and I Ks stochastic properties 6 4 2-2 -4-6 -8 Deterministic Control IKr block 6 4 2-2 -4-6 -8-1 -1 17.4 17.7 18 18.3 18.6 17.4 17.7 18 18.3 18.6 time (s) time (s) Stochastic IKs I Kr block APD prolongation Increased dispersion in APD
V (mv) V (mv) V (mv) V (mv) V (mv) V (mv) I Kr Block and I Ks stochastic properties Control LQT2 6 4 2-2 -4-6 -8 4 2-2 -4-6 -8 4 2-2 -4-6 -8 Determ. Stoch. -1 2 4 6 8 1 time (ms) 6-1 2 4 6 8 1 time (ms) 6-1 2 4 6 8 1 time (ms) Under HERG block conditions, IKs stochastic behaviour might contribute to: EADs generation (probability=.2) Increased dispersion of APD 6 4 2-2 -4-6 -8-1 2 4 6 8 1 time (ms) 6 4 2-2 -4-6 -8-1 2 4 6 8 1 time (ms) 6 4 2-2 -4-6 -8 Determ. Stoch. -1 2 4 6 8 1 time (ms)
V (mv) Multi-scale Modelling of Drug-induced Effects Drug/Ion Channel model Cellular AP model 6 4 2-2 -4-6 -8 EAD/APD dispersion -1 17.4 17.7 18 18.3 18.6 time (s) Ventricular model Torsades de Pointes
Conclusions Systematic investigation into how changes in specific ionic current properties can modulate electrophysiological properties and contribute to biological variability Comparison to experimental data demonstrates that the sensitivity analysis is a powerful method for the systematic and in depth validation of action potential models. It also highlights the need for additional experimental data to further our understanding of human ventricular electrophysiology. Stochastic gating behaviour might contribute to repolarization instabilities and arrhythmias under drug induced block.