Changes in nutrient balance, methane emissions, physiologic biomarkers, and production performance in goats fed different forage-to-concentrate ratios during lactation

[EN] The objective was to determine the effect forage-to-concentrate (F:C) ratio and stage of lactation on methane emissions, digestibility, nutrient balance, lactation performance, and metabolic responses in lactating goats. Twenty Murciano-Granadina dairy goats were used in an experiment divided into 3 periods: early (30 d), mid (100 d), and late (170 d) lactation. All goats were fed a diet with 35:65 F:C (FCL) during early-lactation. Then, 1 group (n = 10 goats) remained on FCL through mid- and late-lactation while the other group (n = 10 goats) was fed a diet with 50:50 F:C at mid-lactation (FCM) and 65:35 (FCH) at late lactation. A greater proportion of concentrate in the diet was associated with greater overall intake and digestibility (P < 0.05). Energy balance was negative in early-lactation (-77 kJ/kg of BW0.75, on average) and positive for FCL at mid- and late-lactation (13 and 35 kJ/kg of BW0.75, respectively). Goats fed FCM and FCH maintained negative energy balance throughout lactation. Plasma concentrations of non-esterified fatty acids at mid-lactation were greater for FCM than FCL (680 mEq/L), and at late-lactation concentrations were greater for FCH and FCL (856 mEq/L). A similar response was detected for plasma beta-hydroxybutyrate. Methane emission was greater (P < 0.05) for FCM than FCH (1.7 g CH4/d). This study demonstrated that differences in F:C across stages of lactation lead to distinct metabolic responses at the level of the rumen and tissues.This study was supported by LIFE Project, European Commission (ref. LIFE2016/CCM/ES/000088 LOW CARBON FEED).Fernández Martínez, CJ.; Hernández, A.; Gomis-Tena Dolz, J.; Loor, JJ. (2021). Changes in nutrient balance, methane emissions, physiologic biomarkers, and production performance in goats fed different forage-to-concentrate ratios during lactation. Journal of Animal Science. 99(7):1-13. https://doi.org/10.1093/jas/skab114S11399

Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study

[EN] Background: Assessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to de- velop a methodology to automatically generate Markovian models of the drug-hERG channel interactions. Methods: The training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, dis- tributed into 104 groups with different affinities and kinetics to the conformational states of the chan- nel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization. Results: We propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynam- ics and the onset of I Kr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accu- racy being 97.53%. Moreover, generation of the dynamic model of an I Kr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer. Conclusion: Our methodology could be very useful to model and simulate dynamic drug¿hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit I Kr and the efficacy of antiarrhythmic I Kr blockers.This work was the Spanish Ministerio de Ciencia, Innovacion y Universidades [grant "Formacion de Profesorado Universitario" FPU19/02200; grant PID2019-104356RB-C41 funded by MCIN/AEI/10.13039/50110 0 011033 ]; the European Union's Horizon 2020 research and innovation program [grant agreement No 101016496 (SimCardioTest)]; and the Direccion General de Politica Cientifica de la Generalitat Valenciana [grant PROMETEO/2020/043]. Patenting of the proposed system/software is under considerationEscobar-Ropero, F.; Gomis-Tena Dolz, J.; Saiz Rodríguez, FJ.; Romero Pérez, L. (2022). Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study. Computer Methods and Programs in Biomedicine. 226:1-10. https://doi.org/10.1016/j.cmpb.2022.10714811022

Inclusion of lemon leaves and rice straw into compound feed and its effect on nutrient balance, milk yield, and methane emissions in dairy goats

This study was supported by LIFE Project, Valencia, Spain (ref. LIFE2016/CCM/ES/000088 LOW CARBON FEED). The authors have not stated any conflicts of interestRomero Rueda, T.; Pérez Baena, I.; Larsen, T.; Gomis-Tena Dolz, J.; Loor, JJ.; Fernández Martínez, CJ. (2020). Inclusion of lemon leaves and rice straw into compound feed and its effect on nutrient balance, milk yield, and methane emissions in dairy goats. Journal of Dairy Science. 103(7):6178-6189. https://doi.org/10.3168/jds.2020-18168S61786189103

In silico assessment of drug safety in human heart applied to late sodium current blockers

Drug-induced action potential (AP) prolongation leading to Torsade de Pointes is a major concern for the development of anti-arrhythmic drugs. Nevertheless the development of improved anti-arrhythmic agents, some of which may block different channels, remains an important opportunity. Partial block of the late sodium current (INaL) has emerged as a novel anti-arrhythmic mechanism. It can be effective in the settings of free radical challenge or hypoxia. In addition, this approach can attenuate pro-arrhythmic effects of blocking the rapid delayed rectifying K+ current (IKr). The main goal of our computational work was to develop an in-silico tool for preclinical anti-arrhythmic drug safety assessment, by illustrating the impact of IKr/INaL ratio of steady-state block of drug candidates on “torsadogenic” biomarkers. The O’Hara et al. AP model for human ventricular myocytes was used. Biomarkers for arrhythmic risk, i.e., AP duration, triangulation, reverse rate-dependence, transmural dispersion of repolarization and electrocardiogram QT intervals, were calculated using single myocyte and one-dimensional strand simulations. Predetermined amounts of block of INaL and IKr were evaluated. “Safety plots” were developed to illustrate the value of the specific biomarker for selected combinations of IC50s for IKr and INaL of potential drugs. The reference biomarkers at baseline changed depending on the “drug” specificity for these two ion channel targets. Ranolazine and GS967 (a novel potent inhibitor of INaL) yielded a biomarker data set that is considered safe by standard regulatory criteria. This novel in-silico approach is useful for evaluating pro-arrhythmic potential of drugs and drug candidates in the human ventricle.This work was supported by (1) Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica, (2) Plan Avanza en el marco de la Accion Estrategica de Telecomunicaciones y Sociedad de la Informacion del Ministerio de Industria Turismo y Comercio of Spain (TSI-020100-2010-469), (3) Programa de Apoyo a la Investigacion y Desarrollo (PAID-06-11-2002) de la Universidad Politecnica de Valencia, (4) Programa Prometeo (PROMETEO/2012/030) de la Conselleria d'Educacio Formacio I Ocupacio, Generalitat Valenciana and (5) Gilead Sciences, Ltd.Trenor Gomis, BA.; Gomis-Tena Dolz, J.; Cardona Urrego, KE.; Romero Pérez, L.; Rajamani, S.; Belardinelli, L.; Giles, WR.... (2013). In silico assessment of drug safety in human heart applied to late sodium current blockers. Channels. 7(4):1-14. doi:10.4161/chan.24905S11474Maltsev, V. A., Silverman, N., Sabbah, H. N., & Undrovinas, A. I. (2007). Chronic heart failure slows late sodium current in human and canine ventricular myocytes: Implications for repolarization variability. European Journal of Heart Failure, 9(3), 219-227. doi:10.1016/j.ejheart.2006.08.007Zaza, A., Belardinelli, L., & Shryock, J. C. (2008). Pathophysiology and pharmacology of the cardiac «late sodium current». Pharmacology & Therapeutics, 119(3), 326-339. doi:10.1016/j.pharmthera.2008.06.001Song, Y., Shryock, J. C., Wagner, S., Maier, L. S., & Belardinelli, L. (2006). Blocking Late Sodium Current Reduces Hydrogen Peroxide-Induced Arrhythmogenic Activity and Contractile Dysfunction. Journal of Pharmacology and Experimental Therapeutics, 318(1), 214-222. doi:10.1124/jpet.106.101832Milberg, P., Pott, C., Fink, M., Frommeyer, G., Matsuda, T., Baba, A., … Eckardt, L. (2008). Inhibition of the Na+/Ca2+ exchanger suppresses torsades de pointes in an intact heart model of long QT syndrome-2 and long QT syndrome-3. Heart Rhythm, 5(10), 1444-1452. doi:10.1016/j.hrthm.2008.06.017Jia, S., Lian, J., Guo, D., Xue, X., Patel, C., Yang, L., … Yan, G.-X. (2011). Modulation of the late sodium current by ATX-II and ranolazine affects the reverse use-dependence and proarrhythmic liability of IKrblockade. British Journal of Pharmacology, 164(2), 308-316. doi:10.1111/j.1476-5381.2010.01181.xUNDROVINAS, A. I., BELARDINELLI, L., UNDROVINAS, N. A., & SABBAH, H. N. (2006). Ranolazine Improves Abnormal Repolarization and Contraction in Left Ventricular Myocytes of Dogs with Heart Failure by Inhibiting Late Sodium Current. Journal of Cardiovascular Electrophysiology, 17(s1), S169-S177. doi:10.1111/j.1540-8167.2006.00401.xWu, L., Shryock, J. C., Song, Y., Li, Y., Antzelevitch, C., & Belardinelli, L. (2004). Antiarrhythmic Effects of Ranolazine in a Guinea Pig in Vitro Model of Long-QT Syndrome. Journal of Pharmacology and Experimental Therapeutics, 310(2), 599-605. doi:10.1124/jpet.104.066100MOSS, A. J., ZAREBA, W., SCHWARZ, K. Q., ROSERO, S., MCNITT, S., & ROBINSON, J. L. (2008). Ranolazine Shortens Repolarization in Patients with Sustained Inward Sodium Current Due to Type-3 Long-QT Syndrome. Journal of Cardiovascular Electrophysiology, 19(12), 1289-1293. doi:10.1111/j.1540-8167.2008.01246.xSong, Y., Shryock, J. C., Wu, L., & Belardinelli, L. (2004). Antagonism by Ranolazine of the Pro-Arrhythmic Effects of Increasing Late INa in Guinea Pig Ventricular Myocytes. Journal of Cardiovascular Pharmacology, 44(2), 192-199. doi:10.1097/00005344-200408000-00008Belardinelli, L., Liu, G., Smith-Maxwell, C., Wang, W.-Q., El-Bizri, N., Hirakawa, R., … Shryock, J. C. (2012). A Novel, Potent, and Selective Inhibitor of Cardiac Late Sodium Current Suppresses Experimental Arrhythmias. Journal of Pharmacology and Experimental Therapeutics, 344(1), 23-32. doi:10.1124/jpet.112.198887Banyasz, T., Koncz, R., Fulop, L., Szentandrassy, N., Magyar, J., & Nanasi, P. (2004). Profile of IKs During the Action Potential Questions the Therapeutic Value of IKs Blockade. Current Medicinal Chemistry, 11(1), 45-60. doi:10.2174/0929867043456304Hopenfeld, B. (2006). A mathematical analysis of the action potential plateau duration of a human ventricular myocyte. Journal of Theoretical Biology, 240(2), 311-322. doi:10.1016/j.jtbi.2005.09.021Maleckar, M. M., Greenstein, J. L., Giles, W. R., & Trayanova, N. A. (2009). Electrotonic Coupling between Human Atrial Myocytes and Fibroblasts Alters Myocyte Excitability and Repolarization. Biophysical Journal, 97(8), 2179-2190. doi:10.1016/j.bpj.2009.07.054Nygren, A., Fiset, C., Firek, L., Clark, J. W., Lindblad, D. S., Clark, R. B., & Giles, W. R. (1998). Mathematical Model of an Adult Human Atrial Cell. Circulation Research, 82(1), 63-81. doi:10.1161/01.res.82.1.63Viswanathan, P. (1999). Pause induced early afterdepolarizations in the long QT syndrome: a simulation study. Cardiovascular Research, 42(2), 530-542. doi:10.1016/s0008-6363(99)00035-8Wu, R., & Patwardhan, A. (2007). Effects of rapid and slow potassium repolarization currents and calcium dynamics on hysteresis in restitution of action potential duration. Journal of Electrocardiology, 40(2), 188-199. doi:10.1016/j.jelectrocard.2006.01.001Zaniboni, M. (2011). 3D current–voltage–time surfaces unveil critical repolarization differences underlying similar cardiac action potentials: A model study. Mathematical Biosciences, 233(2), 98-110. doi:10.1016/j.mbs.2011.06.008Zaniboni, M. (2012). Late Phase of Repolarization is Autoregenerative and Scales Linearly with Action Potential Duration in Mammals Ventricular Myocytes: A Model Study. IEEE Transactions on Biomedical Engineering, 59(1), 226-233. doi:10.1109/tbme.2011.2170987Zaniboni, M., Riva, I., Cacciani, F., & Groppi, M. (2010). How different two almost identical action potentials can be: A model study on cardiac repolarization. Mathematical Biosciences, 228(1), 56-70. doi:10.1016/j.mbs.2010.08.007Belardinelli, L., Antzelevitch, C., & Vos, M. A. (2003). Assessing predictors of drug-induced torsade de pointes. Trends in Pharmacological Sciences, 24(12), 619-625. doi:10.1016/j.tips.2003.10.002Shah, R. R., & Hondeghem, L. M. (2005). Refining detection of drug-induced proarrhythmia: QT interval and TRIaD. Heart Rhythm, 2(7), 758-772. doi:10.1016/j.hrthm.2005.03.023Hondeghem, L. M., Carlsson, L., & Duker, G. (2001). Instability and Triangulation of the Action Potential Predict Serious Proarrhythmia, but Action Potential Duration Prolongation Is Antiarrhythmic. Circulation, 103(15), 2004-2013. doi:10.1161/01.cir.103.15.2004Mirams, G. R., Cui, Y., Sher, A., Fink, M., Cooper, J., Heath, B. M., … Noble, D. (2011). Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk. Cardiovascular Research, 91(1), 53-61. doi:10.1093/cvr/cvr044Obiol-Pardo, C., Gomis-Tena, J., Sanz, F., Saiz, J., & Pastor, M. (2011). A Multiscale Simulation System for the Prediction of Drug-Induced Cardiotoxicity. Journal of Chemical Information and Modeling, 51(2), 483-492. doi:10.1021/ci100423zSuzuki, S., Murakami, S., Tsujimae, K., Findlay, I., & Kurachi, Y. (2008). In silico risk assessment for drug-induction of cardiac arrhythmia. Progress in Biophysics and Molecular Biology, 98(1), 52-60. doi:10.1016/j.pbiomolbio.2008.05.003Mirams, G. R., Davies, M. R., Cui, Y., Kohl, P., & Noble, D. (2012). Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing. British Journal of Pharmacology, 167(5), 932-945. doi:10.1111/j.1476-5381.2012.02020.xSarkar, A. X., & Sobie, E. A. (2011). Quantification of repolarization reserve to understand interpatient variability in the response to proarrhythmic drugs: A computational analysis. Heart Rhythm, 8(11), 1749-1755. doi:10.1016/j.hrthm.2011.05.023CHANG, C., ACHARFI, S., WU, M., CHIANG, F., WANG, J., SUNG, T., & CHAHINE, M. (2004). A novel SCN5A mutation manifests as a malignant form of long QT syndrome with perinatal onset of tachycardia/bradycardia. Cardiovascular Research, 64(2), 268-278. doi:10.1016/j.cardiores.2004.07.007Weirich, J., & Antoni, H. (1998). Rate-dependence of antiarrhythmic and proarrhythmic properties of class I and class III antiarrhythmic drugs. Basic Research in Cardiology, 93(0), s125-s132. doi:10.1007/s003950050236O’Hara, T., Virág, L., Varró, A., & Rudy, Y. (2011). Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Computational Biology, 7(5), e1002061. doi:10.1371/journal.pcbi.1002061Wu, L., Ma, J., Li, H., Wang, C., Grandi, E., Zhang, P., … Belardinelli, L. (2011). Late Sodium Current Contributes to the Reverse Rate-Dependent Effect of I Kr Inhibition on Ventricular Repolarization. Circulation, 123(16), 1713-1720. doi:10.1161/circulationaha.110.000661Banyasz, T., Barandi, L., Harmati, G., Virag, L., Szentandrassy, N., Marton, I., … P. Nanasi, P. (2011). Mechanism of Reverse Rate-Dependent Action of Cardioactive Agents. Current Medicinal Chemistry, 18(24), 3597-3606. doi:10.2174/092986711796642355Noble D, Tsien RW. The repolarization process of heart cells. In: DeMello WC, ed. Electrical Phenomena in the Heart. New York: Academic Press, 1972:133-161.Fink, M., Noble, D., Virag, L., Varro, A., & Giles, W. R. (2008). Contributions of HERG K+ current to repolarization of the human ventricular action potential. Progress in Biophysics and Molecular Biology, 96(1-3), 357-376. doi:10.1016/j.pbiomolbio.2007.07.011Goineau, S., Castagné, V., Guillaume, P., & Froget, G. (2012). The comparative sensitivity of three in vitro safety pharmacology models for the detection of lidocaine-induced cardiac effects. Journal of Pharmacological and Toxicological Methods, 66(1), 52-58. doi:10.1016/j.vascn.2012.06.001Wu, L., Guo, D., Li, H., Hackett, J., Yan, G.-X., Jiao, Z., … Belardinelli, L. (2008). Role of late sodium current in modulating the proarrhythmic and antiarrhythmic effects of quinidine. Heart Rhythm, 5(12), 1726-1734. doi:10.1016/j.hrthm.2008.09.008Diness, J. G., Hansen, R. S., Nissen, J. D., Jespersen, T., & Grunnet, M. (2009). Antiarrhythmic effect of IKr activation in a cellular model of LQT3. Heart Rhythm, 6(1), 100-106. doi:10.1016/j.hrthm.2008.10.020Laursen, M., Olesen, S.-P., Grunnet, M., Mow, T., & Jespersen, T. (2011). Characterization of cardiac repolarization in the Göttingen minipig. Journal of Pharmacological and Toxicological Methods, 63(2), 186-195. doi:10.1016/j.vascn.2010.10.001HONDEGHEM, L. M. (2006). Thorough QT/QTc Not So Thorough: Removes Torsadogenic Predictors from the T-Wave, Incriminates Safe Drugs, and Misses Profibrillatory Drugs. Journal of Cardiovascular Electrophysiology, 17(3), 337-340. doi:10.1111/j.1540-8167.2006.00347.xHondeghem, L. M., & Snyders, D. J. (1990). Class III antiarrhythmic agents have a lot of potential but a long way to go. Reduced effectiveness and dangers of reverse use dependence. Circulation, 81(2), 686-690. doi:10.1161/01.cir.81.2.686Bril, A. (1998). Combined potassium and calcium channel antagonistic activities as a basis for neutral frequency dependent increase in action potential duration: comparison between BRL-32872 and azimilide. Cardiovascular Research, 37(1), 130-140. doi:10.1016/s0008-6363(97)00216-2Jurkiewicz, N. K., & Sanguinetti, M. C. (1993). Rate-dependent prolongation of cardiac action potentials by a methanesulfonanilide class III antiarrhythmic agent. Specific block of rapidly activating delayed rectifier K+ current by dofetilide. Circulation Research, 72(1), 75-83. doi:10.1161/01.res.72.1.75O’Hara, T., & Rudy, Y. (2012). Quantitative comparison of cardiac ventricular myocyte electrophysiology and response to drugs in human and nonhuman species. American Journal of Physiology-Heart and Circulatory Physiology, 302(5), H1023-H1030. doi:10.1152/ajpheart.00785.2011Stengl, M., Volders, P. G. A., Thomsen, M. B., Spatjens, R. L. H. M. G., Sipido, K. R., & Vos, M. A. (2003). Accumulation of slowly activating delayed rectifier potassium current (IKs) in canine ventricular myocytes. The Journal of Physiology, 551(3), 777-786. doi:10.1113/jphysiol.2003.044040Shah, R. R. (2002). The significance of QT interval in drug development. British Journal of Clinical Pharmacology, 54(2), 188-202. doi:10.1046/j.1365-2125.2002.01627.xNuyens, D., Stengl, M., Dugarmaa, S., Rossenbacker, T., Compernolle, V., Rudy, Y., … Carmeliet, P. (2001). Abrupt rate accelerations or premature beats cause life-threatening arrhythmias in mice with long-QT3 syndrome. Nature Medicine, 7(9), 1021-1027. doi:10.1038/nm0901-1021Gautier, M., Zhang, H., & Fearon, I. M. (2008). Peroxynitrite formation mediates LPC-induced augmentation of cardiac late sodium currents. Journal of Molecular and Cellular Cardiology, 44(2), 241-251. doi:10.1016/j.yjmcc.2007.09.007Fearon, I. M., & Brown, S. T. (2004). Acute and chronic hypoxic regulation of recombinant hNav1.5 α subunits. Biochemical and Biophysical Research Communications, 324(4), 1289-1295. doi:10.1016/j.bbrc.2004.09.188Ward, C. A., & Giles, W. R. (1997). Ionic mechanism of the effects of hydrogen peroxide in rat ventricular myocytes. The Journal of Physiology, 500(3), 631-642. doi:10.1113/jphysiol.1997.sp022048Saint, D. A. (2009). The cardiac persistent sodium current: an appealing therapeutic target? British Journal of Pharmacology, 153(6), 1133-1142. doi:10.1038/sj.bjp.0707492Zygmunt, A. C., Eddlestone, G. T., Thomas, G. P., Nesterenko, V. V., & Antzelevitch, C. (2001). Larger late sodium conductance in M cells contributes to electrical heterogeneity in canine ventricle. American Journal of Physiology-Heart and Circulatory Physiology, 281(2), H689-H697. doi:10.1152/ajpheart.2001.281.2.h689Trenor, B., Cardona, K., Gomez, J. F., Rajamani, S., Ferrero, J. M., Belardinelli, L., & Saiz, J. (2012). Simulation and Mechanistic Investigation of the Arrhythmogenic Role of the Late Sodium Current in Human Heart Failure. PLoS ONE, 7(3), e32659. doi:10.1371/journal.pone.0032659Martin, R. L., McDermott, J. S., Salmen, H. J., Palmatier, J., Cox, B. F., & Gintant, G. A. (2004). The Utility of hERG and Repolarization Assays in Evaluating Delayed Cardiac Repolarization: Influence of Multi-Channel Block. Journal of Cardiovascular Pharmacology, 43(3), 369-379. doi:10.1097/00005344-200403000-00007Antzelevitch, C., Belardinelli, L., Zygmunt, A. C., Burashnikov, A., Di Diego, J. M., Fish, J. M., … Thomas, G. (2004). Electrophysiological Effects of Ranolazine, a Novel Antianginal Agent With Antiarrhythmic Properties. Circulation, 110(8), 904-910. doi:10.1161/01.cir.0000139333.83620.5dNoble, D. (2006). Late sodium current in the pathophysiology of cardiovascular disease: consequences of sodium-calcium overload. Heart, 92(suppl_4), iv1-iv5. doi:10.1136/hrt.2005.078782Scirica, B. M., Morrow, D. A., Hod, H., Murphy, S. A., Belardinelli, L., Hedgepeth, C. M., … Braunwald, E. (2007). Effect of Ranolazine, an Antianginal Agent With Novel Electrophysiological Properties, on the Incidence of Arrhythmias in Patients With Non–ST-Segment–Elevation Acute Coronary Syndrome. Circulation, 116(15), 1647-1652. doi:10.1161/circulationaha.107.724880Hoefen, R., Reumann, M., Goldenberg, I., Moss, A. J., O-Uchi, J., Gu, Y., … Lopes, C. M. (2012). In Silico Cardiac Risk Assessment in Patients With Long QT Syndrome. Journal of the American College of Cardiology, 60(21), 2182-2191. doi:10.1016/j.jacc.2012.07.053Silva, J. R., Pan, H., Wu, D., Nekouzadeh, A., Decker, K. F., Cui, J., … Rudy, Y. (2009). A multiscale model linking ion-channel molecular dynamics and electrostatics to the cardiac action potential. Proceedings of the National Academy of Sciences, 106(27), 11102-11106. doi:10.1073/pnas.0904505106STARMER, C. (1991). Lidocaine blockade of continuously and transiently accessible sites in cardiac sodium channels*1. Journal of Molecular and Cellular Cardiology, 23, 73-83. doi:10.1016/0022-2828(91)90026-iRudy, Y., & Silva, J. R. (2006). Computational biology in the study of cardiac ion channels and cell electrophysiology. Quarterly Reviews of Biophysics, 39(1), 57-116. doi:10.1017/s0033583506004227Clancy, C. E., Zhu, Z. I., & Rudy, Y. (2007). Pharmacogenetics and anti-arrhythmic drug therapy: a theoretical investigation. American Journal of Physiology-Heart and Circulatory Physiology, 292(1), H66-H75. doi:10.1152/ajpheart.00312.2006Zygmunt, A. C., Nesterenko, V. V., Rajamani, S., Hu, D., Barajas-Martinez, H., Belardinelli, L., & Antzelevitch, C. (2011). Mechanisms of atrial-selective block of Na+ channels by ranolazine: I. Experimental analysis of the use-dependent block. American Journal of Physiology-Heart and Circulatory Physiology, 301(4), H1606-H1614. doi:10.1152/ajpheart.00242.2011Nesterenko, V. V., Zygmunt, A. C., Rajamani, S., Belardinelli, L., & Antzelevitch, C. (2011). Mechanisms of atrial-selective block of Na+ channels by ranolazine: II. Insights from a mathematical model. American Journal of Physiology-Heart and Circulatory Physiology, 301(4), H1615-H1624. doi:10.1152/ajpheart.00243.2011Zemzemi, N., Bernabeu, M. O., Saiz, J., Cooper, J., Pathmanathan, P., Mirams, G. R., … Rodriguez, B. (2013). Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials. British Journal of Pharmacology, 168(3), 718-733. doi:10.1111/j.1476-5381.2012.02200.xHille B. Ionic Channels of Excitable Membranes. 2nd ed. Sunderland, MA: Sinauer Associates, 1992.Rajamani, S., Shryock, J. C., & Belardinelli, L. (2008). Rapid Kinetic Interactions of Ranolazine With HERG K+ Current. Journal of Cardiovascular Pharmacology, 51(6), 581-589. doi:10.1097/fjc.0b013e3181799690Rajamani, S., El-Bizri, N., Shryock, J. C., Makielski, J. C., & Belardinelli, L. (2009). Use-dependent block of cardiac late Na+ current by ranolazine. Heart Rhythm, 6(11), 1625-1631. doi:10.1016/j.hrthm.2009.07.042Bassingthwaighte, J., Hunter, P., & Noble, D. (2009). The Cardiac Physiome: perspectives for the future. Experimental Physiology, 94(5), 597-605. doi:10.1113/expphysiol.2008.044099Quinn, T. A., Granite, S., Allessie, M. A., Antzelevitch, C., Bollensdorff, C., Bub, G., … Delmar, M. (2011). Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): Standardised reporting for model reproducibility, interoperability, and data sharing. Progress in Biophysics and Molecular Biology, 107(1), 4-10. doi:10.1016/j.pbiomolbio.2011.07.001Glukhov, A. V., Fedorov, V. V., Lou, Q., Ravikumar, V. K., Kalish, P. W., Schuessler, R. B., … Efimov, I. R. (2010). Transmural Dispersion of Repolarization in Failing and Nonfailing Human Ventricle. Circulation Research, 106(5), 981-991. doi:10.1161/circresaha.109.204891MALTSEV, V., & UNDROVINAS, A. (2006). A multi-modal composition of the late Na+ current in human ventricular cardiomyocytes. Cardiovascular Research, 69(1), 116-127. doi:10.1016/j.cardiores.2005.08.015Drouin, E., Charpentier, F., Gauthier, C., Laurent, K., & Le Marec, H. (1995). Electrophysiologic characteristics of cells spanning the left ventricular wall of human heart: Evidence for presence of M cells. Journal of the American College of Cardiology, 26(1), 185-192. doi:10.1016/0735-1097(95)00167-xBerecki, G., Zegers, J. G., Bhuiyan, Z. A., Verkerk, A. O., Wilders, R., & Van Ginneken, A. C. G. (2006). Long-QT syndrome-related sodium channel mutations probed by the dynamic action potential clamp technique. The Journal of Physiology, 570(2), 237-250. doi:10.1113/jphysiol.2005.096578Maltsev, V. A., Silverman, N., Sabbah, H. N., & Undrovinas, A. I. (2007). Chronic heart failure slows late sodium current in human and canine ventricular myocytes: Implications for repolarization variability. European Journal of Heart Failure, 9(3), 219-227. doi:10.1016/j.ejheart.2006.08.007Valdivia, C. R., Chu, W. W., Pu, J., Foell, J. D., Haworth, R. A., Wolff, M. R., … Makielski, J. C. (2005). Increased late sodium current in myocytes from a canine heart failure model and from failing human heart. Journal of Molecular and Cellular Cardiology, 38(3), 475-483. doi:10.1016/j.yjmcc.2004.12.012Belardinelli, L. (2006). Inhibition of the late sodium current as a potential cardioprotective principle:

Electrónica industrial: problemas resueltos

La Electrónica Industrial, o al menos así se entiende desde titulaciones como la de Ingeniero Industrial (I.I.) o la de Ingeniero en Automática y Electrónica Industrial (I.A.E.I.), versa sobre el estudio de las topologías que permiten la conversión electrónica de la potencia. Este enfoque permite el estudio de los convertidores sin entrar en las particularidades propias de cada dispositivo electrónico, es decir, considerándolos como "ideales" y sin entrar en disquisiciones tales como el control o la protección de los mismos.El hecho de no "entrar en el dispositivo" no significa que los análisis, desde el punto de vista de la topología,no sean elaborados, rigurosos y de cierta dificultad.Para dar ejemplos de cómo se pueden abordar dichos análisis, de una forma metódica, es por lo que se ha recopilado la presente colección de problemasGomis-Tena Dolz, J.; Figueres Amorós, E.; Garcerá Sanfeliú, G. (2019). Electrónica industrial: problemas resueltos. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/123421EDITORIA

Prediction of IKr Blocker Channel State Preference Based on Voltage Clamp Simulations Using Machine Learning Techniques

[EN] Assessment of drug cardiotoxicity is crucial in the development of new compounds and is typically addressed by evaluating the blockade they cause in the potassium human ether-à-go-go related gene (hERG) channels. Our objective is to develop a classifier to determine the preference for binding to the different states of a drug. We created a set of 2600 virtual blockers with different affinities and kinetics to the conformational states of the channel divided into 13 classes. Simulations were carried out using three stimulation protocols that enhance the probabilities of the channel to occupy a certain state. Three measurements were taken for each of the simulations: IC50, the recovery constant of the IKr potassium current and an estimation of the time required for the simulation to be stable. Therefore, we obtained 9 variables for each of the blockers studied. A two-step classifier was developed, trained and evaluated. First, we used support vector machines on the IC50 to separate the 13 classes into three groups with 4, 5 and 4 classes respectively. Secondly, we used neural networks on each group with all the variables to finally classify the blockers. The three classifiers obtained an overall accuracy on the test group of 90.83, 88.66 and 89.16% for each of the groups respectively.Escobar-Ropero, F.; Gomis-Tena Dolz, J.; Saiz Rodríguez, FJ.; Romero Pérez, L. (2020). Prediction of IKr Blocker Channel State Preference Based on Voltage Clamp Simulations Using Machine Learning Techniques. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.274S1

A New Protocol Test For Physical Activity Research In Obese Children (Etiobe Project)

[EN] A new protocol is presented to validate TIPS (portable physiological monitoring device designed by I3BH that can get respiration, ecg and activity of the patient) for physical habits detection. Physiological and activity parameters and data from questionnaires have been acquired from a group of obese & nonobese children (n=20). Children completed activities from sedentary level to vigorous level. Preliminary results show variability on the response of children¿s effort and feasibility of TIPS platform as an ambulatory tool.Guixeres Provinciale, J.; Zaragozá Álvarez, I.; Alcañiz Raya, ML.; Gomis-Tena Dolz, J. (2009). A New Protocol Test For Physical Activity Research In Obese Children (Etiobe Project). Studies in Health Technology and Informatics. 144:281-283. doi:10.3233/978-1-60750-017-9-281S28128314

Clasificación de Bloqueadores de Ikr Basada en Protocolos de Voltage Clamp y Técnicas de Machine Learning.

[ES] La evaluación de la cardiotoxicidad es clave en el desarrollo de nuevos compuestos y se suele realizar analizando el grado de bloqueo que provocan en los canales de potasio relacionados con el gen humano ether-à-go-go (hERG). El objetivo de este trabajo es desarrollar un clasificador que determine la preferencia de un fármaco para unirse a un estado determinado del canal. Se ha creado un conjunto de 2600, divididos en 13 clases, bloqueadores virtuales con diferentes afinidades y cinéticas por los estados conformacionales del canal. Se realizaron simulaciones utilizando tres protocolos de estimulación que aumentan la probabilidad del canal de ocupar ciertos estados. Para cada simulación se han tomado tres medidas: IC50, la constante de recuperación de la corriente de potasio IKr y una estimación del tiempo necesario para alcanzar el estado estacionario. Por lo tanto, se obtuvieron 9 variables por cada fármaco estudiado. Se desarrolló, entrenó y evaluó un clasificador en dos pasos. En primer lugar, se usaron máquinas de soporte vectorial sobre las IC50 para separar las 13 clases en tres grupos con 4, 5 y 4 clases respectivamente. En segundo lugar, se usaron redes neuronales en cada grupo para terminar de clasificar los bloqueadores. Los tres clasificadores obtuvieron una precisión global en los grupos de test del 90.83, 88.66 y 89.16% respectivamente.Escobar-Ropero, F.; Gomis-Tena Dolz, J.; Saiz Rodríguez, FJ.; Romero Pérez, L. (2020). Clasificación de Bloqueadores de Ikr Basada en Protocolos de Voltage Clamp y Técnicas de Machine Learning. Sociedad Española de Ingeniería Biomédica. 137-140. http://hdl.handle.net/10251/167115S13714

InSilico Classifiers for the Assessment of Drug Proarrhythmicity

[EN] Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC50 and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T-x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T-qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T-triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T-EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T-x, T-qNet, T-triang, and T-EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC50 test (current biomarker) was 78.9%. When combining T-x, T-qNet and T-triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I-Kr, I-CaL, I-NaL and I-Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.This work was partially supported by the Direccion general de Politica Cientifica de la Generalitat Valenciana (PROMETEO/2020/043); by "Primeros Proyectos de Investigacion" (PAID06-18) from Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), Valencia, Spain; as well as from the "Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 20172020" from the Ministerio de Ciencia e Innovacion y Universidades (PID2019-104356RB-C41/AEI/10.13039/501100011033). J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the "Formacion de Profesorado Universitario" (Grant Reference: FPU18/01659).Llopis-Lorente, J.; Gomis-Tena Dolz, J.; Cano, J.; Romero Pérez, L.; Saiz Rodríguez, FJ.; Trenor Gomis, BA. (2020). InSilico Classifiers for the Assessment of Drug Proarrhythmicity. Journal of Chemical Information and Modeling. 60(10):5172-5187. https://doi.org/10.1021/acs.jcim.0c00201S517251876010Gintant, G. A. (2008). Preclinical Torsades-de-Pointes Screens: Advantages and limitations of surrogate and direct approaches in evaluating proarrhythmic risk. Pharmacology & Therapeutics, 119(2), 199-209. doi:10.1016/j.pharmthera.2008.04.010Vicente, J., Zusterzeel, R., Johannesen, L., Mason, J., Sager, P., Patel, V., … Strauss, D. G. (2017). Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the «CiPA» Initiative and Design of a Prospective Clinical Validation Study. Clinical Pharmacology & Therapeutics, 103(1), 54-66. doi:10.1002/cpt.896Colatsky, T., Fermini, B., Gintant, G., Pierson, J. B., Sager, P., Sekino, Y., … Stockbridge, N. (2016). The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative — Update on progress. Journal of Pharmacological and Toxicological Methods, 81, 15-20. doi:10.1016/j.vascn.2016.06.002Li, Z., Ridder, B. J., Han, X., Wu, W. W., Sheng, J., Tran, P. N., … Strauss, D. G. (2018). Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the Ci PA Initiative. Clinical Pharmacology & Therapeutics, 105(2), 466-475. doi:10.1002/cpt.1184Sager, P. T., Gintant, G., Turner, J. R., Pettit, S., & Stockbridge, N. (2014). Rechanneling the cardiac proarrhythmia safety paradigm: A meeting report from the Cardiac Safety Research Consortium. American Heart Journal, 167(3), 292-300. doi:10.1016/j.ahj.2013.11.004Fermini, B., Hancox, J. C., Abi-Gerges, N., Bridgland-Taylor, M., Chaudhary, K. W., Colatsky, T., … Vandenberg, J. I. (2015). A New Perspective in the Field of Cardiac Safety Testing through the Comprehensive In Vitro Proarrhythmia Assay Paradigm. Journal of Biomolecular Screening, 21(1), 1-11. doi:10.1177/1087057115594589Mirams, G. R., Davies, M. R., Brough, S. J., Bridgland-Taylor, M. H., Cui, Y., Gavaghan, D. J., & Abi-Gerges, N. (2014). Prediction of Thorough QT study results using action potential simulations based on ion channel screens. Journal of Pharmacological and Toxicological Methods, 70(3), 246-254. doi:10.1016/j.vascn.2014.07.002Obiol-Pardo, C., Gomis-Tena, J., Sanz, F., Saiz, J., & Pastor, M. (2011). A Multiscale Simulation System for the Prediction of Drug-Induced Cardiotoxicity. Journal of Chemical Information and Modeling, 51(2), 483-492. doi:10.1021/ci100423zRomero, L., Cano, J., Gomis-Tena, J., Trenor, B., Sanz, F., Pastor, M., & Saiz, J. (2018). In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk. Journal of Chemical Information and Modeling, 58(4), 867-878. doi:10.1021/acs.jcim.7b00440Okada, J., Yoshinaga, T., Kurokawa, J., Washio, T., Furukawa, T., Sawada, K., … Hisada, T. (2018). Arrhythmic hazard map for a 3D whole-ventricle model under multiple ion channel block. British Journal of Pharmacology, 175(17), 3435-3452. doi:10.1111/bph.14357Dutta, S., Chang, K. C., Beattie, K. A., Sheng, J., Tran, P. N., Wu, W. W., … Li, Z. (2017). Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment. Frontiers in Physiology, 8. doi:10.3389/fphys.2017.00616Mirams, G. R., Cui, Y., Sher, A., Fink, M., Cooper, J., Heath, B. M., … Noble, D. (2011). Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk. Cardiovascular Research, 91(1), 53-61. doi:10.1093/cvr/cvr044Kramer, J., Obejero-Paz, C. A., Myatt, G., Kuryshev, Y. A., Bruening-Wright, A., Verducci, J. S., & Brown, A. M. (2013). MICE Models: Superior to the HERG Model in Predicting Torsade de Pointes. Scientific Reports, 3(1). doi:10.1038/srep02100O’Hara, T., Virág, L., Varró, A., & Rudy, Y. (2011). Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Computational Biology, 7(5), e1002061. doi:10.1371/journal.pcbi.1002061Britton, O. J., Abi-Gerges, N., Page, G., Ghetti, A., Miller, P. E., & Rodriguez, B. (2017). Quantitative Comparison of Effects of Dofetilide, Sotalol, Quinidine, and Verapamil between Human Ex vivo Trabeculae and In silico Ventricular Models Incorporating Inter-Individual Action Potential Variability. Frontiers in Physiology, 8. doi:10.3389/fphys.2017.00597Passini, E., Mincholé, A., Coppini, R., Cerbai, E., Rodriguez, B., Severi, S., & Bueno-Orovio, A. (2016). Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy. Journal of Molecular and Cellular Cardiology, 96, 72-81. doi:10.1016/j.yjmcc.2015.09.003Cavero, I., Guillon, J.-M., Ballet, V., Clements, M., Gerbeau, J.-F., & Holzgrefe, H. (2016). Comprehensive in vitro Proarrhythmia Assay (C i PA): Pending issues for successful validation and implementation. Journal of Pharmacological and Toxicological Methods, 81, 21-36. doi:10.1016/j.vascn.2016.05.012Dutta, S.; Strauss, D.; Colatsky, T.; Li, Z. In Optimization of an In Silico Cardiac Cell Model for Proarrhythmia Risk Assessment, 2016 Computing in Cardiology Conference (CinC); 2016; pp 869–872.Mora, M. T., Ferrero, J. M., Romero, L., & Trenor, B. (2017). Sensitivity analysis revealing the effect of modulating ionic mechanisms on calcium dynamics in simulated human heart failure. PLOS ONE, 12(11), e0187739. doi:10.1371/journal.pone.0187739Parikh, J., Gurev, V., & Rice, J. J. (2017). Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features. Frontiers in Pharmacology, 8. doi:10.3389/fphar.2017.00816Woosley, R.; Romero, K.; Heise, W. Risk Categories for Drugs that Prolong QT & Induce Torsade de Pointes (TdP); AZCERT, Inc.: Oro Valley, AZ, 2019. https://www.crediblemeds.org/ (accessed March 9, 2020).Parikh, J., Di Achille, P., Kozloski, J., & Gurev, V. (2019). Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment. Frontiers in Pharmacology, 10. doi:10.3389/fphar.2019.01054Varró, A., & Baczkó, I. (2011). Cardiac ventricular repolarization reserve: a principle for understanding drug-related proarrhythmic risk. British Journal of Pharmacology, 164(1), 14-36. doi:10.1111/j.1476-5381.2011.01367.xAntzelevitch, C. (2007). Ionic, molecular, and cellular bases of QT-interval prolongation and torsade de pointes. Europace, 9(Supplement 4), iv4-iv15. doi:10.1093/europace/eum166Viswanathan, P. (1999). Pause induced early afterdepolarizations in the long QT syndrome: a simulation study. Cardiovascular Research, 42(2), 530-542. doi:10.1016/s0008-6363(99)00035-8Britton, O. J., Bueno-Orovio, A., Van Ammel, K., Lu, H. R., Towart, R., Gallacher, D. J., & Rodriguez, B. (2013). Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proceedings of the National Academy of Sciences, 110(23), E2098-E2105. doi:10.1073/pnas.1304382110Sobie, E. A. (2009). Parameter Sensitivity Analysis in Electrophysiological Models Using Multivariable Regression. Biophysical Journal, 96(4), 1264-1274. doi:10.1016/j.bpj.2008.10.056Hoffmann, P., & Warner, B. (2006). Are hERG channel inhibition and QT interval prolongation all there is in drug-induced torsadogenesis? A review of emerging trends. Journal of Pharmacological and Toxicological Methods, 53(2), 87-105. doi:10.1016/j.vascn.2005.07.003CANTILENAJR, L., KOERNER, J., TEMPLE, R., & THROCKMORTON, D. (2006). OIII-A-1FDA evaluation of cardiac repolarization data for 19 drugs and drug candidates. Clinical Pharmacology & Therapeutics, 79(2), P29-P29. doi:10.1016/j.clpt.2005.12.106REDFERN, W., CARLSSON, L., DAVIS, A., LYNCH, W., MACKENZIE, I., PALETHORPE, S., … WALLIS, R. (2003). Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. Cardiovascular Research, 58(1), 32-45. doi:10.1016/s0008-6363(02)00846-5Tomek, J., Bueno-Orovio, A., Passini, E., Zhou, X., Minchole, A., Britton, O., … Rodriguez, B. (2019). Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. eLife, 8. doi:10.7554/elife.48890Passini, E., Trovato, C., Morissette, P., Sannajust, F., Bueno‐Orovio, A., & Rodriguez, B. (2019). Drug‐induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias. British Journal of Pharmacology, 176(19), 3819-3833. doi:10.1111/bph.14786Zhou, X., Qu, Y., Passini, E., Bueno-Orovio, A., Liu, Y., Vargas, H. M., & Rodriguez, B. (2020). Blinded In Silico Drug Trial Reveals the Minimum Set of Ion Channels for Torsades de Pointes Risk Assessment. Frontiers in Pharmacology, 10. doi:10.3389/fphar.2019.01643Lawrence, C. L., Bridgland-Taylor, M. H., Pollard, C. E., Hammond, T. G., & Valentin, J.-P. (2006). A Rabbit Langendorff Heart Proarrhythmia Model: Predictive Value for Clinical Identification of Torsades de Pointes. British Journal of Pharmacology, 149(7), 845-860. doi:10.1038/sj.bjp.0706894Ando, H., Yoshinaga, T., Yamamoto, W., Asakura, K., Uda, T., Taniguchi, T., … Sekino, Y. (2017). A new paradigm for drug-induced torsadogenic risk assessment using human iPS cell-derived cardiomyocytes. Journal of Pharmacological and Toxicological Methods, 84, 111-127. doi:10.1016/j.vascn.2016.12.003Cubeddu, L. (2016). Drug-induced Inhibition and Trafficking Disruption of ion Channels: Pathogenesis of QT Abnormalities and Drug-induced Fatal Arrhythmias. Current Cardiology Reviews, 12(2), 141-154. doi:10.2174/1573403x12666160301120217Nogawa, H., & Kawai, T. (2014). hERG trafficking inhibition in drug-induced lethal cardiac arrhythmia. European Journal of Pharmacology, 741, 336-339. doi:10.1016/j.ejphar.2014.06.044Kanlop, N., Chattipakorn, S., & Chattipakorn, N. (2011). Effects of cilostazol in the heart. Journal of Cardiovascular Medicine, 12(2), 88-95. doi:10.2459/jcm.0b013e3283439746Morosin, M., Dametto, E., Bianco, F. D., Brieda, M., & Nicolosi, G. L. (2017). An unusual etiology of torsade de pointes-induced syncope. Archives of Medical Science, 3, 686-688. doi:10.5114/aoms.2017.67287Nia, A. M., Dahlem, K. M., Gassanov, N., Hungerbühler, H., Fuhr, U., & Er, F. (2011). Clinical impact of fluvoxamine-mediated long QTU syndrome. European Journal of Clinical Pharmacology, 68(1), 109-111. doi:10.1007/s00228-011-1091-7HII, J. T. Y., WYSE, D. G., GILLIS, A. M., COHEN, J. M., & MITCHELL, L. B. (1991). Propafenone-Induced Torsade de Pointes: Cross-Reactivity with Quinidine. Pacing and Clinical Electrophysiology, 14(11), 1568-1570. doi:10.1111/j.1540-8159.1991.tb02729.xWenzel-Seifert, K., Wittmann, M., & Haen, E. (2011). QTc Prolongation by Psychotropic Drugs and the Risk of Torsade de Pointes. Deutsches Aerzteblatt Online. doi:10.3238/arztebl.2011.0687Beattie, K. A., Luscombe, C., Williams, G., Munoz-Muriedas, J., Gavaghan, D. J., Cui, Y., & Mirams, G. R. (2013). Evaluation of an in silico cardiac safety assay: Using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge. Journal of Pharmacological and Toxicological Methods, 68(1), 88-96. doi:10.1016/j.vascn.2013.04.004Romero, L., Carbonell, B., Trenor, B., Rodríguez, B., Saiz, J., & Ferrero, J. M. (2011). Systematic characterization of the ionic basis of rabbit cellular electrophysiology using two ventricular models. Progress in Biophysics and Molecular Biology, 107(1), 60-73. doi:10.1016/j.pbiomolbio.2011.06.012Zicha, S., Moss, I., Allen, B., Varro, A., Papp, J., Dumaine, R., … Nattel, S. (2003). Molecular basis of species-specific expression of repolarizing K+ currents in the heart. American Journal of Physiology-Heart and Circulatory Physiology, 285(4), H1641-H1649. doi:10.1152/ajpheart.00346.2003Li, Z., Dutta, S., Sheng, J., Tran, P. N., Wu, W., Chang, K., … Colatsky, T. (2017). Improving the In Silico Assessment of Proarrhythmia Risk by Combining hERG (Human Ether-à-go-go-Related Gene) Channel–Drug Binding Kinetics and Multichannel Pharmacology. Circulation: Arrhythmia and Electrophysiology, 10(2). doi:10.1161/circep.116.004628Sahli Costabal, F., Matsuno, K., Yao, J., Perdikaris, P., & Kuhl, E. (2019). Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. Computer Methods in Applied Mechanics and Engineering, 348, 313-333. doi:10.1016/j.cma.2019.01.033Lacerda, A. E., Kuryshev, Y. A., Chen, Y., Renganathan, M., Eng, H., Danthi, S. J., … Brown, A. M. (2007). Alfuzosin Delays Cardiac Repolarization by a Novel Mechanism. Journal of Pharmacology and Experimental Therapeutics, 324(2), 427-433. doi:10.1124/jpet.107.128405Yang, T., Chun, Y. W., Stroud, D. M., Mosley, J. D., Knollmann, B. C., Hong, C., & Roden, D. M. (2014). Screening for Acute I Kr Block Is Insufficient to Detect Torsades de Pointes Liability. Circulation, 130(3), 224-234. doi:10.1161/circulationaha.113.007765Crumb, W. J., Vicente, J., Johannesen, L., & Strauss, D. G. (2016). An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. Journal of Pharmacological and Toxicological Methods, 81, 251-262. doi:10.1016/j.vascn.2016.03.009Polak, S., Wiśniowska, B., & Brandys, J. (2009). Collation, assessment and analysis of literaturein vitrodata on hERG receptor blocking potency for subsequent modeling of drugs’ cardiotoxic properties. Journal of Applied Toxicology, 29(3), 183-206. doi:10.1002/jat.1395Gomis-Tena, J., Brown, B. M., Cano, J., Trenor, B., Yang, P.-C., Saiz, J., … Romero, L. (2020). When Does the IC50 Accurately Assess the Blocking Potency of a Drug? Journal of Chemical Information and Modeling, 60(3), 1779-1790. doi:10.1021/acs.jcim.9b01085Li, Z., Mirams, G. R., Yoshinaga, T., Ridder, B. J., Han, X., Chen, J. E., … Strauss, D. G. (2019). General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy. Clinical Pharmacology & Therapeutics, 107(1), 102-111. doi:10.1002/cpt.1647Romero, L., Trenor, B., Yang, P.-C., Saiz, J., & Clancy, C. E. (2015). In silico screening of the impact of hERG channel kinetic abnormalities on channel block and susceptibility to acquired long QT syndrome. Journal of Molecular and Cellular Cardiology, 87, 271-282. doi:10.1016/j.yjmcc.2015.08.015Milnes, J. T., Witchel, H. J., Leaney, J. L., Leishman, D. J., & Hancox, J. C. (2010). Investigating dynamic protocol-dependence of hERG potassium channel inhibition at 37°C: Cisapride versus dofetilide. Journal of Pharmacological and Toxicological Methods, 61(2), 178-191. doi:10.1016/j.vascn.2010.02.007DI VEROLI, G. Y., DAVIES, M. R., ZHANG, H., ABI-GERGES, N., & BOYETT, M. R. (2013). hERG Inhibitors with Similar Potency But Different Binding Kinetics Do Not Pose the Same Proarrhythmic Risk: Implications for Drug Safety Assessment. Journal of Cardiovascular Electrophysiology, 25(2), 197-207. doi:10.1111/jce.12289Brown, C. S., Farmer, R. G., Soberman, J. E., & Eichner, S. F. (2004). Pharmacokinetic Factors in the Adverse Cardiovascular Effects of Antipsychotic Drugs. Clinical Pharmacokinetics, 43(1), 33-56. doi:10.2165/00003088-200443010-00003Van Noord, C., Dieleman, J. P., van Herpen, G., Verhamme, K., & Sturkenboom, M. C. J. M. (2010). Domperidone and Ventricular Arrhythmia or Sudden Cardiac Death. Drug Safety, 33(11), 1003-1014. doi:10.2165/11536840-000000000-00000Wiśniowska, B., & Polak, S. (2017). Am I or am I not proarrhythmic? Comparison of various classifications of drug TdP propensity. Drug Discovery Today, 22(1), 10-16. doi:10.1016/j.drudis.2016.09.02

Analysis of surface electromyographic parameters for the assessment of muscle fatigue during moderate exercises

[EN] Muscle fatigue is a neuromuscular phenomenon which prevents the muscles to generate the required force. Although there are multiple studies about detecting this condition with surface electromyography, this method has still some limitations. The objective of this study was to determine the viability of some biomarkers as early indicators of fatigue during moderate exercise. Eight healthy volunteers performed 2 dynamic exercises involving the flexo-extension of the knee lifting 2 and 4kg attached to the ankle. A bipolar electromyographic signal was recorded simultaneously from: Rectus Femoris, Vastus Lateralis and Biceps Femoris. Muscle activations were extracted by supervised automatic segmentation and their root mean square, bandwidth, entropy and spectral moment were calculated. The maximum of the normalized coefficient of the linear cross-correlation between each muscle pair were also computed as well as the slope throughout the execution of the exercise repetitions of all above mentioned parameters. Results from 2kg exercise did not reveal clear signs of fatigue. By contrast those from 4kg showed an increase of the root mean square and spectral moment ratio, and decrease of bandwidth, sample entropy and linear cross correlation coefficient, pointing out fatigue. In this study, it has been confirmed the viability of the cross-correlation technique as a possible new biomarker related to the first signs of muscle fatigue.This study was supported by the Agència Valenciana de la Innovació project "iSARC-GENETICS, Plataforma Inteligente para la Detección Temprana, Ayuda al Diagnóstico y Seguimiento de la Sarcopenia". (INNEST/2021/365)Junquera-Godoy, I.; Gomis-Tena Dolz, J.; Martínez-De-Juan, JL.; Saiz Rodríguez, FJ.; Prats-Boluda, G. (2022). Analysis of surface electromyographic parameters for the assessment of muscle fatigue during moderate exercises. Universidad de Valladolid. 35-38. http://hdl.handle.net/10251/191588353