Biology:Pharmacological cardiotoxicity

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Illustration of a bottle of drugs inside a danger sign

Pharmacological cardiotoxicity is a cardiac damage under the action of drugs and it can occur both affecting the performances of the cardiac muscle and by altering the ion channels/currents of the functional cardiac cells, named the cardiomyocytes.[1]

Two distinct case in which can occur are related to anti-cancer drugs and antiarrhythmic drugs. From early observations, some of the first ones which go under the name of anthracycline. It has emerged that such drugs cause a progressive form of heart failure leading to cardiac death.[2] The mechanism of cell injury is thought to account for iron-dependent generation of reactive oxygen species with a spreading of oxidative damage to the cardiomyocytes.[2] On the other hand, related to the antiarrhythmic drugs, the cardiotoxicity is associated to the risk of induce a potential fatal arrhythmias due to an imbalance in the amount of ion currents that flows in/out the cell membrane of the cardiomyocytes.[3]

Pharmacological action

The pharmacological action represents a mechanism by means of a specific effect can be obtained. Depending on the class and type of the drug, the pharmacological action may be different.[3]

In the case of electrophysiology, the drug directly acts at the level of the cells, affecting the mechanism of opening/closing of the ionic channels, as it happens with the anti-arrhythmic drugs. Due to the ionic permeability properties of the cardiac cells membrane, during the action potential, the opening of the ion channels generates ion currents that flow in/out of the lipophilic cell membrane.[4]

The anti-arrhythmic drugs action is that of modifying such ion currents, acting on the structure of the ion channel, and trying to restore the physiological opening/closing mechanism of the ion channels. It may be that, instead of providing a benefit to the heart, such as the aforementioned desired effect, a new drug can negatively affect the ion currents, ending up to excessively modifying the amount of ion currents flowing throughout the cell membrane, thus increasing the risk of inducing a potentially fatal arrhythmias.[5][6]

Examples of pharmacological cardiotoxicity

Anti-arrhythmic drugs cardiotoxicity

Representation of the ion channel opening/closing.

The anti-arrhythmic drugs are a class of pharmacological compounds whose action is that of restore the normal sinus rhythm when a patient is affected by an arrhythmia, so their action is that of performing a pharmacological cardioversion.[7]

Indeed, the pharmacological cardiotoxicity of anti-arrhythmic compounds is related to the action of these drugs to induce potential fatal arrhythmias such as torsade de pointes or ventricular fibrillation.[8] The anti-arrhythmic drugs directly act on the opening/closing of ion channels, thus modifying the ion currents.[9]

In treating arrhythmias, the pharmacological therapeutic action is related to the generation of a new combination of the blockage/opening of ion channels. Nevertheless, this new pharmacologically induced configuration may lead to an unbalance in ionic currents and as a consequence causing a modification in the action potential morphology which increases the risk of inducing an arrhythmia.[9]

Over the years, it has been studied how the change of the action potential shape, i.e. prolongation of the repolarization phase or early after depolarizations, is bonded to the likelihood of inducing fatal arrhythmias, such as torsade de pointes.[10] Thus, the risk of inducing a fatal arrhythmias has to be prevented assessing the pharmacological cardiotoxicity at the early stages of the manufacturing of a new drug.[10]

Clinical cardiotoxicity assessment

During the study of a new pharmacological compound, the clinical trial is one of the phases before the market release.[11]

At this level, following the directions of the clinical trial protocol, the new drug is administrated to the patient as a therapy, and the patient's clinical status is monitored aiming to evaluate possible side effects.[11][12]

Old paradigm

To assess pharmacological cardiotoxicity, it was common practice to measure QT interval in vivo and the blockage of potassium channel.[13] Nevertheless, a new paradigm has been developed to overcome the limits of the previous one since 2013. In fact, it has been demonstrated that the old paradigm was stringent, labeling as pro-arrhythmic some pharmacological compounds which actually were not.[13]

New paradigm: CiPA

The comprehensive in vitro pro-arrhythmia assay was born, accounting for both experimental data and detailed computational models which take into account multiple ionic currents instead of measuring just QT interval and potassium channel blockage. This new paradigm aims to interlink the clinical evidence with in silico modeling to reconstruct the atrial and ventricular action potential and evaluate the likelihood for early afterdepolarization to occur.[13]

In Silico cardiotoxicity assessment

Background

In the last years, in silico medicine turned out to be promising, aiding scientists and clinicians to prevent and adequately cure several diseases.[14] Computational modeling aid in understanding complex phenomena, allowing scientists to vary parameters aiming to measure variables that otherwise could have not been investigated.[14]

In the field of electrophysiology, the pharmacological cardiotoxicity assessment can be carried out leveraging specific computational models. According to the type and parameters to be investigated in the research, it is possible to analyze the pharmacological effect on the atria and ventricles separately.[15][16]

Since the two cardiac chambers are very different each other and play a key role both on a functional and anatomical basis, suitable computational models have to be accounted for to describe their different behaviour. During the years, several models have been developed o best characterize and replicate the cellular action potential behaviour of the most relevant anatomical region of the heart, such as Courtemanche model for atria or O'Hara model for ventricles.[15][16]

Creation of a population of cellular action potentials

Ventricular Action Potential

In this way, it has been possible to create a virtual cellular population of cardiomyocytes and vary their conductances that are related to the main ionic currents which contribute to the action potential morphology, reflective of a specific anatomical region of the heart.[17][18]

In order to create a stable population of cellular action potentials, the biomarkers have to be considered. During the years, several biomarkers have been developed to best characterize the instability of cellular action potentials. Few biomarkers are reported:[17]

  • APD90: it represents the action potential duration when the phase of the repolarization is at 90%, so it is possible to associate to this value a time and it can be expressed as:[19]

[math]\displaystyle{ APD_{90}=t_{90}-t_0 }[/math]

  • APD90: it represents the action potential duration when the phase of the repolarization is at 50%, so it is possible to associate to this value a time and it can be expressed as:[19]

[math]\displaystyle{ APD_{50}=t_{50}-t_0 }[/math]

  • APD20: it represents the action potential duration when the phase of the repolarization is at 20%, so it is possible to associate to this value a time and it can be expressed as:[19]

[math]\displaystyle{ APD_{20}=t_{20}-t_0 }[/math]

  • Triangulation: it is a measure of how triangular is an action potential, expressed as:[19]

[math]\displaystyle{ Triangulation=APD_{90}-APD_{50} }[/math]

  • APA: it represents the action potential amplitude, expressed as:[19]

[math]\displaystyle{ APA=V_{Max}-V_0 }[/math]

Many other can be used according to the needs of the research .[20]

Regional clusterization

Once the cellular population is stable, all the action potential are compared to physiological data related to the most relevant anatomical regions to appropriately filter the action potential, aiming to consider just the physiologically relevant ones.[21]

At the atrial level, the clusterization occurs with data associated to:[21]

  • Right atrium
  • Right atrial appendage
  • Left atrium
  • Left atrial appendage
  • Atrioventricular rings
  • Crista terminalis
  • Right Bachmann's bundle
  • Left Bachmann's bundle
  • Pectinate muscles

Simulation of the pharmacological action

Early afterdepolarization

According to pharmacokinetic and pharmacodynamic data of the drugs, the pharmacological action is integrated in the model. By means of specific electrical stimuli protocols,[22] the pharmacological effect of a new drug can be investigated in a completely safe, and controlled computational environment, providing preliminary important considerations concerning the cardiotoxicity of new pharmacological compounds.[23]

According to the outcome of the simulations, several aspects can be investigated to identify the pro-arrhythmicity of a new pharmacological compound.[24][25] The typical changes, called repolarization abnormalities, in the action potential morphology that are considered pro-arrhythmic are:[25]

Torsade de point risk score

Simulation can be carried out at different effective plasmatic therapeutic level of the drugs to identify the level at which cardiotoxicity cannot be neglected. The data collected could be finally managed to create a score system aimed to define the torsadogenic risk, namely the risk of inducing torsade de pointes, of the new drugs.[26][6]

A possible torsade de point risk score to assess cardiotoxicity could be:[6] [math]\displaystyle{ TdPRS=\frac{\sum_{c}(W_c\cdot nRA_c)}{N\cdot \sum_{c}W_c)} }[/math]

where [math]\displaystyle{ \sum_{c} }[/math] is the sum of all concentrations, [C] is the concentration taken into account, [math]\displaystyle{ W_c=\frac{EFTPC}{[C]} }[/math] , [math]\displaystyle{ N }[/math] is the total number of models in the population, and [math]\displaystyle{ nRA_c }[/math] represents the number of models showing repolarization abnormalities.[6]

Tissue simulations

More detailed computation simulations can be carried out accounting for not cellular models, but taking into consideration the functional syncytium and enabling the cells to mutually interact, the so-called electrotonic coupling.[27]

In case of tissue simulation or in wider cases, such as in whole organ simulations, all the cellular models are note applicable anymore, and several corrections have to be made. Firstly, the governing equations can not be just ordinary differential equations, but a system of partial differential equations has to be accounted for.[28] A suitable choice may be the monodomain model:[29]

[math]\displaystyle{ \triangledown \cdot(D\nabla V)=(C_m\frac{\partial V}{\partial t} + I_{ion}(V,u)) }[/math] [math]\displaystyle{ in }[/math] [math]\displaystyle{ \Omega }[/math]

[math]\displaystyle{ n \cdot(D\nabla V)=0 }[/math] [math]\displaystyle{ in }[/math] [math]\displaystyle{ \partial \Omega }[/math]

where [math]\displaystyle{ D }[/math] is the effective conductivity tensor, [math]\displaystyle{ C_m }[/math]is the capacitance of the cellular membrane, [math]\displaystyle{ I_{ion} }[/math] the transmembrane ionic current, [math]\displaystyle{ \Omega }[/math] and [math]\displaystyle{ \partial\Omega }[/math] are the domain of interest and its boundary, respectively, with [math]\displaystyle{ n }[/math] the outward boundary of [math]\displaystyle{ \partial\Omega }[/math].[29]

See also

References

  1. ↑ Iqubal, A.; Ehtaishamul Haque, S.; Sharma, S.; Asif Ansari, M. (2018). "CLINICAL UPDATES ON DRUG-INDUCED CARDIOTOXICITY". International Journal of Pharmaceutical Sciences and Research 9 (1): 16–26. doi:10.13040/IJPSR.0975-8232.9(1).16-26. 
  2. ↑ 2.0 2.1 Ewer, Michael S.; Ewer, Steven M. (September 2015). "Cardiotoxicity of anticancer treatments" (in en). Nature Reviews Cardiology 12 (9): 547–558. doi:10.1038/nrcardio.2015.65. ISSN 1759-5002. PMID 25962976. https://www.nature.com/articles/nrcardio.2015.65. 
  3. ↑ 3.0 3.1 Ramalingam, Mahesh; Kim*, Sung-Jin (2016). "Pharmacological Activities and Applications of Spicatoside A". Biomolecules & Therapeutics (Biomolecules & Therapeutics) 24 (5): 469–474. doi:10.4062/biomolther.2015.214. PMID 27169821. 
  4. ↑ Bartos, D. C.; Grandi, E.; Ripplinger, C. M. (2011-01-17). Terjung, Ronald. ed (in en). Comprehensive Physiology. 5 (1 ed.). Wiley. pp. 1423–1464. doi:10.1002/cphy.c140069. ISBN 978-0-470-65071-4. 
  5. ↑ Zipes, Douglas P. (April 1987). "Proarrhythmic effects of antiarrhythmic drugs" (in en). The American Journal of Cardiology 59 (11): E26–E31. doi:10.1016/0002-9149(87)90198-6. PMID 2437787. https://linkinghub.elsevier.com/retrieve/pii/0002914987901986. 
  6. ↑ 6.0 6.1 6.2 6.3 Fogli Iseppe, Alex; Ni, Haibo; Zhu, Sicheng; Zhang, Xianwei; Coppini, Raffaele; Yang, Pei-Chi; Srivatsa, Uma; Clancy, Colleen E. et al. (August 2021). "Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning" (in en). Clinical Pharmacology & Therapeutics 110 (2): 380–391. doi:10.1002/cpt.2240. ISSN 0009-9236. PMID 33772748. 
  7. ↑ Jones, Benjamin; Burnand, Cally (May 2021). "Antiarrhythmic drugs" (in en). Anaesthesia & Intensive Care Medicine 22 (5): 319–323. doi:10.1016/j.mpaic.2021.03.009. https://linkinghub.elsevier.com/retrieve/pii/S1472029921000850. 
  8. ↑ Lancaster, M Cummins; Sobie, Ea (October 2016). "Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms: In silico prediction of Torsades risk" (in en). Clinical Pharmacology & Therapeutics 100 (4): 371–379. doi:10.1002/cpt.367. PMID 26950176. 
  9. ↑ 9.0 9.1 Carmeliet, Edward; Mubagwa, Kanigula (July 1998). "Antiarrhythmic drugs and cardiac ion channels: mechanisms of action" (in en). Progress in Biophysics and Molecular Biology 70 (1): 1–72. doi:10.1016/S0079-6107(98)00002-9. PMID 9785957. 
  10. ↑ 10.0 10.1 Llopis-Lorente, Jordi; Gomis-Tena, Julio; Cano, Jordi; Romero, Lucía; Saiz, Javier; Trenor, Beatriz (2020-10-26). "In Silico Classifiers for the Assessment of Drug Proarrhythmicity" (in en). Journal of Chemical Information and Modeling 60 (10): 5172–5187. doi:10.1021/acs.jcim.0c00201. ISSN 1549-9596. PMID 32786710. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00201. 
  11. ↑ 11.0 11.1 Kandi, Venkataramana; Vadakedath, Sabitha (2023-02-16). "Clinical Trials and Clinical Research: A Comprehensive Review" (in en). Cureus 15 (2): 15. doi:10.7759/cureus.35077. ISSN 2168-8184. PMID 36938261. 
  12. ↑ Juni, P. (2001-07-07). "Systematic reviews in health care: Assessing the quality of controlled clinical trials". BMJ 323 (7303): 42–46. doi:10.1136/bmj.323.7303.42. PMID 11440947. 
  13. ↑ 13.0 13.1 13.2 Sager, Philip T.; Gintant, Gary; Turner, J. Rick; Pettit, Syril; Stockbridge, Norman (March 2014). "Rechanneling the cardiac proarrhythmia safety paradigm: A meeting report from the Cardiac Safety Research Consortium" (in en). American Heart Journal 167 (3): 292–300. doi:10.1016/j.ahj.2013.11.004. PMID 24576511. 
  14. ↑ 14.0 14.1 Viceconti, Marco; Dall’Ara, Enrico (January 2019). "From bed to bench: How in silico medicine can help ageing research" (in en). Mechanisms of Ageing and Development 177: 103–108. doi:10.1016/j.mad.2018.07.001. PMID 30005915. https://linkinghub.elsevier.com/retrieve/pii/S0047637418300812. 
  15. ↑ 15.0 15.1 Courtemanche, Marc; Ramirez, Rafael J.; Nattel, Stanley (1998-07-01). "Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model" (in en). American Journal of Physiology. Heart and Circulatory Physiology 275 (1): H301–H321. doi:10.1152/ajpheart.1998.275.1.H301. ISSN 0363-6135. PMID 9688927. https://www.physiology.org/doi/10.1152/ajpheart.1998.275.1.H301. 
  16. ↑ 16.0 16.1 O'Hara, Thomas; Virág, László; Varró, András; Rudy, Yoram (2011-05-26). McCulloch, Andrew D.. ed. "Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation" (in en). PLOS Computational Biology 7 (5): e1002061. doi:10.1371/journal.pcbi.1002061. ISSN 1553-7358. PMID 21637795. Bibcode: 2011PLSCB...7E2061O. 
  17. ↑ 17.0 17.1 Muszkiewicz, Anna; Britton, Oliver J.; Gemmell, Philip; Passini, Elisa; Sánchez, Carlos; Zhou, Xin; Carusi, Annamaria; Quinn, T. Alexander et al. (January 2016). "Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm" (in en). Progress in Biophysics and Molecular Biology 120 (1–3): 115–127. doi:10.1016/j.pbiomolbio.2015.12.002. PMID 26701222. 
  18. ↑ Sarkar, Amrita X.; Christini, David J.; Sobie, Eric A. (2012-06-01). "Exploiting mathematical models to illuminate electrophysiological variability between individuals: Electrophysiological variability" (in en). The Journal of Physiology 590 (11): 2555–2567. doi:10.1113/jphysiol.2011.223313. PMID 22495591. 
  19. ↑ 19.0 19.1 19.2 19.3 19.4 Lachaud, Quentin; Aziz, Muhamad Hifzhudin Noor; Burton, Francis L; Macquaide, Niall; Myles, Rachel C; Simitev, Radostin D; Smith, Godfrey L (2022-12-09). "Electrophysiological heterogeneity in large populations of rabbit ventricular cardiomyocytes" (in en). Cardiovascular Research 118 (15): 3112–3125. doi:10.1093/cvr/cvab375. ISSN 0008-6363. PMID 35020837. PMC 9732512. https://academic.oup.com/cardiovascres/article/118/15/3112/6502285. 
  20. ↑ Britton, Oliver J.; Bueno-Orovio, Alfonso; Virág, László; Varró, András; Rodriguez, Blanca (2017-05-05). "The Electrogenic Na+/K+ Pump Is a Key Determinant of Repolarization Abnormality Susceptibility in Human Ventricular Cardiomyocytes: A Population-Based Simulation Study". Frontiers in Physiology 8: 278. doi:10.3389/fphys.2017.00278. ISSN 1664-042X. PMID 28529489. 
  21. ↑ 21.0 21.1 Ferrer, Ana; Sebastián, Rafael; Sánchez-Quintana, Damián; Rodríguez, José F.; Godoy, Eduardo J.; Martínez, Laura; Saiz, Javier (2015-11-02). Panfilov, Alexander V. ed. "Detailed Anatomical and Electrophysiological Models of Human Atria and Torso for the Simulation of Atrial Activation" (in en). PLOS ONE 10 (11): e0141573. doi:10.1371/journal.pone.0141573. ISSN 1932-6203. PMID 26523732. Bibcode: 2015PLoSO..1041573F. 
  22. ↑ Abi-Gerges, Najah; Small, Ben G; Lawrence, Chris L; Hammond, Tim G; Valentin, Jean-Pierre; Pollard, Chris E (March 2006). "Gender differences in the slow delayed ( I Ks ) but not in inward ( I K1 ) rectifier K + currents of canine Purkinje fibre cardiac action potential: key roles for I Ks , β -adrenoceptor stimulation, pacing rate and gender: Gender, pacing rate and stimulated I Ks" (in en). British Journal of Pharmacology 147 (6): 653–660. doi:10.1038/sj.bjp.0706491. PMID 16314855. 
  23. ↑ Passini, Elisa; Britton, Oliver J.; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N.; Gallacher, David J.; Greig, Robert J. H.; Bueno-Orovio, Alfonso et al. (2017). "Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity". Frontiers in Physiology 8: 668. doi:10.3389/fphys.2017.00668. ISSN 1664-042X. PMID 28955244. 
  24. ↑ Smith, J M; Clancy, E A; Valeri, C R; Ruskin, J N; Cohen, R J (January 1988). "Electrical alternans and cardiac electrical instability." (in en). Circulation 77 (1): 110–121. doi:10.1161/01.CIR.77.1.110. ISSN 0009-7322. PMID 3335062. https://www.ahajournals.org/doi/10.1161/01.CIR.77.1.110. 
  25. ↑ 25.0 25.1 Weiss, James N.; Garfinkel, Alan; Karagueuzian, Hrayr S.; Chen, Peng-Sheng; Qu, Zhilin (December 2010). "Early afterdepolarizations and cardiac arrhythmias". Heart Rhythm 7 (12): 1891–1899. doi:10.1016/j.hrthm.2010.09.017. ISSN 1547-5271. PMID 20868774. PMC 3005298. https://doi.org/10.1016/j.hrthm.2010.09.017. 
  26. ↑ Tisdale, James E.; Jaynes, Heather A.; Kingery, Joanna R.; Mourad, Noha A.; Trujillo, Tate N.; Overholser, Brian R.; Kovacs, Richard J. (July 2013). "Development and Validation of a Risk Score to Predict QT Interval Prolongation in Hospitalized Patients" (in en). Circulation: Cardiovascular Quality and Outcomes 6 (4): 479–487. doi:10.1161/CIRCOUTCOMES.113.000152. ISSN 1941-7713. PMID 23716032. 
  27. ↑ del Rio, Carlos; Hamlin, Robert; Billman, George (2016-09-01). "Myocardial electrotonic coupling modulates repolarization heterogeneities in vivo: Implications for the assessment of pro-arrhythmic liabilities in vitro and in silico" (in en). Journal of Pharmacological and Toxicological Methods. Focused Issue on Safety Pharmacology 81: 354. doi:10.1016/j.vascn.2016.02.063. ISSN 1056-8719. https://www.sciencedirect.com/science/article/pii/S1056871916000642. 
  28. ↑ Sundnes, Joakim; Nielsen, Bjørn Fredrik; Mardal, Kent Andre; Cai, Xing; Lines, Glenn Terje; Tveito, Aslak (2006-07-01). "On the Computational Complexity of the Bidomain and the Monodomain Models of Electrophysiology" (in en). Annals of Biomedical Engineering 34 (7): 1088–1097. doi:10.1007/s10439-006-9082-z. ISSN 1573-9686. PMID 16773461. https://doi.org/10.1007/s10439-006-9082-z. 
  29. ↑ 29.0 29.1 Mountris, Konstantinos A.; Dong, Leiting; Guan, Yue; Atluri, Satya N.; Pueyo, Esther (2022-11-01). "A meshless fragile points method for the solution of the monodomain model for cardiac electrophysiology simulation" (in en). Journal of Computational Science 65: 101880. doi:10.1016/j.jocs.2022.101880. ISSN 1877-7503. https://www.sciencedirect.com/science/article/pii/S1877750322002393. 

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