Computational modeling for antiarrhythmic drugs for atrial fibrillation according to the genotypes

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was supported by a grant [HI19C0114] from the Ministry of Health and Welfare. Additionally, the work was funded by grants [NRF-2019R1C1C100907512], and [NRF-2020R1A2B01001695] from the Basic Science Research Program run by the National Research Foundation of Korea (NRF) under the Ministry of Science, ICT & Future Planning (MSIP). Background The efficacy of antiarrhythmic drugs (AAD) can vary in patients with atrial fibrillation (AF) and the PITX2 gene affects the responsiveness of AADs. We explored the virtual AAD (V-AAD) responses between wild-type and PITX2+/- deficient AF conditions by realistic in-silico AF modeling. Methods We tested the V-AADs in AF modeling integrated with patients’ 3D-computed tomography and 3D-electroanatomical mapping, acquired in 25 patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal type). The ion currents for the PITX2+/- deficiency and each AAD (amiodarone, sotalol, dronedarone, flecainide, and propafenone) were defined based on previous publications. Results We compared the wild-type and PITX2+/- deficiency in terms of the action potential duration (APD90), conduction velocity (CV), maximal slope of restitution (Smax), and wave-dynamic parameters, such as the dominant frequency (DF), phase singularities (PS), and AF termination rates according to the V-AADs. The PITX2+/- deficient model exhibited a shorter APD90 (p < 0.001), a lower Smax (p < 0.001), mean DF (p = 0.012), PS number (p < 0.001), and a longer AF cycle length (AFCL, p = 0.011). Five V-AADs changed the electrophysiology in a dose dependent manner. AAD-induced AFCL lengthening (p < 0.001) and reductions in the CV (p = 0.033), peak DF (p < 0.001) and PS number (p < 0.001) were more significant in PITX2+/- deficient than wild-type AF. PITX2+/- deficient AF was easier to terminate with class IC AADs than the wild-type AF (p = 0.018). Conclusions The computational modeling-guided AAD test was feasible for evaluating the efficacy of multiple AADs in patients with AF. AF wave-dynamics and electrophysiological characteristics are different among the PITX2 deficient and the wild-type genotype models. BaselineChanges after AADClass ICClass IIIWild-typePITX2+/-p-valueWild-typePITX2+/-p-valueWild-typePITX2+/-p-valueWild-typePITX2+/-p-valueAPD90, (ms)243.7 ± 33.8184.4 ± 15.5<0.00138.2 ± 37.343.4 ± 56.20.223275.9 ± 43.5219.0 ± 39.2<0.001284.9 ± 32.8233.8 ± 71.4<0.001CV, (m/s)0.78 ± 0.320.70 ± 0.210.347-0.15 ± 0.18-0.20 ± 0.260.0330.63 ± 0.320.53 ± 0.300.0270.60 ± 0.360.43 ± 0.33<0.001Mean Smax0.787 ± 0.280.531 ± 0.18<0.0010.005 ± 0.260.115 ± 0.24<0.0010.828 ± 0.310.694 ± 0.320.0030.768 ± 0.320.608 ± 0.27<0.001Mean AFCL, (ms)146.96 ± 24.61164.78 ± 22.730.01122.62 ± 24.5537.92 ± 32.72<0.001165.44 ± 36.96190.85 ± 35.61<0.001169.05 ± 25.26203.35 ± 34.78<0.001Peak DF, (Hz)10.68 ± 2.9711.82 ± 3.340.211-2.98 ± 4.94-5.46 ± 4.66<0.00110.01 ± 4.397.23 ± 4.20<0.0016.30 ± 4.325.80 ± 4.070.301Mean DF, (Hz)6.80 ± 0.886.22 ± 0.710.012-1.95 ± 2.44-2.20 ± 1.990.2065.75 ± 1.784.53 ± 2.00<0.0014.14 ± 2.393.69 ± 2.000.077PS Number, (N)101086 ± 9608814150 ± 24778<0.001-59322 ± 99288-7409 ± 27856<0.00150579 ± 6523611568 ± 21868<0.00132951 ± 558643524 ± 8302<0.001PS Life Span, (ms)109.36 ± 113.90102.24 ± 226.640.889-24.87 ± 72.06-41.38 ± 126.350.073103.36 ± 180.6868.05 ± 162.790.14871.91 ± 141.8655.99 ± 217.970.454Table. Effects of AADs in the Wild-type and PITX2+/- Deficiency groupAbstract Figure. Wild-type vs. PITX2+/- baseline model