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Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations.

Corrias A, Jie X, Romero L, Bishop MJ, Bernabeu M, Pueyo E, Rodriguez B - Philos Trans A Math Phys Eng Sci (2010)

Bottom Line: To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels.Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market.We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology.

View Article: PubMed Central - PubMed

Affiliation: Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK.

ABSTRACT
In this paper, we illustrate how advanced computational modelling and simulation can be used to investigate drug-induced effects on cardiac electrophysiology and on specific biomarkers of pro-arrhythmic risk. To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels. The review highlights the variety of proposed biomarkers, the complexity of the mechanisms of drug-induced pro-arrhythmia and the existence of significant animal species differences in drug-induced effects on cardiac electrophysiology. Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market. Computational modelling and simulation has significantly contributed to the understanding of cardiac electrophysiology and arrhythmias over the last 40 years. In the second part of this paper, we illustrate how state-of-the-art open source computational modelling and simulation tools can be used to simulate multi-scale effects of drug-induced ion channel block in ventricular electrophysiology at the cellular, tissue and whole ventricular levels for different animal species. We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology.

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Schematic of a Na+ channel and drug-binding sites. The four domains of the pore-forming α subunit are shown together with two β sheets (β1 and β2). Each domain is composed of six segments (S1–S6). The pore-lining segments are shown in green (S5–S6), while the voltage-sensitive (S4) segments are shown in yellow. A part of the amino acid sequence in the S4 segments of the fourth domain is shown enlarged in the lower panel. The particular amino acids in positions 1764 and 1771 are shown in blue as they are believed to be involved in the binding of anti-arrhythmic drugs such as lidocaine and flecainide. Adapted from fig. 2 of Schauer & Catterall (2006).
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RSTA20100083F1: Schematic of a Na+ channel and drug-binding sites. The four domains of the pore-forming α subunit are shown together with two β sheets (β1 and β2). Each domain is composed of six segments (S1–S6). The pore-lining segments are shown in green (S5–S6), while the voltage-sensitive (S4) segments are shown in yellow. A part of the amino acid sequence in the S4 segments of the fourth domain is shown enlarged in the lower panel. The particular amino acids in positions 1764 and 1771 are shown in blue as they are believed to be involved in the binding of anti-arrhythmic drugs such as lidocaine and flecainide. Adapted from fig. 2 of Schauer & Catterall (2006).

Mentions: Lidocaine (a local anaesthetic belonging to class Ib anti-arrhythmic drugs) was first described as a cardiac INa blocker with possible anti-arrhythmic effects by Bean et al. (1983). Lidocaine displayed affinity to the inactivated state of the channel (Liu et al. 2003) and appears to alter the movement of the S4 segment in the IV domain of the ion channel (Sheets & Hanck 2003) where residues at positions 1764(F) and 1771(Y) have been found to be implicated in the binding (Ragsdale et al. 1996; figure 1). The effect on the macroscopic current is a lower peak in the gating charge/voltage (QV) relationship. Boltzmann fits also showed a lower half-activation value and a bigger slope factor in the presence of lidocaine compared with control (Hanck et al. 2000). In healthy canine hearts, lidocaine slowed conduction velocity in a rate-dependent manner (no effect at 1000 ms pacing cycle length, 13–17% decrease at 200 ms pacing cycle length (Anderson et al. 1990)). In a recent review, Singh & Patrick (2007) classified lidocaine as having no effect (or minor shortening) on ERP, confirming early findings by Olsson et al. (1975) in which a correlation between ERP changes and administration of lidocaine could not be found. In ΔKPQ mutant Na+ channels (increased persistent INa(late) compared with wild type) expressed in HEK cells, lidocaine blocked the late Na+ current (INa(late)) more than the peak (INa(T)) (An et al. 1996).


Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations.

Corrias A, Jie X, Romero L, Bishop MJ, Bernabeu M, Pueyo E, Rodriguez B - Philos Trans A Math Phys Eng Sci (2010)

Schematic of a Na+ channel and drug-binding sites. The four domains of the pore-forming α subunit are shown together with two β sheets (β1 and β2). Each domain is composed of six segments (S1–S6). The pore-lining segments are shown in green (S5–S6), while the voltage-sensitive (S4) segments are shown in yellow. A part of the amino acid sequence in the S4 segments of the fourth domain is shown enlarged in the lower panel. The particular amino acids in positions 1764 and 1771 are shown in blue as they are believed to be involved in the binding of anti-arrhythmic drugs such as lidocaine and flecainide. Adapted from fig. 2 of Schauer & Catterall (2006).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2944395&req=5

RSTA20100083F1: Schematic of a Na+ channel and drug-binding sites. The four domains of the pore-forming α subunit are shown together with two β sheets (β1 and β2). Each domain is composed of six segments (S1–S6). The pore-lining segments are shown in green (S5–S6), while the voltage-sensitive (S4) segments are shown in yellow. A part of the amino acid sequence in the S4 segments of the fourth domain is shown enlarged in the lower panel. The particular amino acids in positions 1764 and 1771 are shown in blue as they are believed to be involved in the binding of anti-arrhythmic drugs such as lidocaine and flecainide. Adapted from fig. 2 of Schauer & Catterall (2006).
Mentions: Lidocaine (a local anaesthetic belonging to class Ib anti-arrhythmic drugs) was first described as a cardiac INa blocker with possible anti-arrhythmic effects by Bean et al. (1983). Lidocaine displayed affinity to the inactivated state of the channel (Liu et al. 2003) and appears to alter the movement of the S4 segment in the IV domain of the ion channel (Sheets & Hanck 2003) where residues at positions 1764(F) and 1771(Y) have been found to be implicated in the binding (Ragsdale et al. 1996; figure 1). The effect on the macroscopic current is a lower peak in the gating charge/voltage (QV) relationship. Boltzmann fits also showed a lower half-activation value and a bigger slope factor in the presence of lidocaine compared with control (Hanck et al. 2000). In healthy canine hearts, lidocaine slowed conduction velocity in a rate-dependent manner (no effect at 1000 ms pacing cycle length, 13–17% decrease at 200 ms pacing cycle length (Anderson et al. 1990)). In a recent review, Singh & Patrick (2007) classified lidocaine as having no effect (or minor shortening) on ERP, confirming early findings by Olsson et al. (1975) in which a correlation between ERP changes and administration of lidocaine could not be found. In ΔKPQ mutant Na+ channels (increased persistent INa(late) compared with wild type) expressed in HEK cells, lidocaine blocked the late Na+ current (INa(late)) more than the peak (INa(T)) (An et al. 1996).

Bottom Line: To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels.Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market.We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology.

View Article: PubMed Central - PubMed

Affiliation: Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK.

ABSTRACT
In this paper, we illustrate how advanced computational modelling and simulation can be used to investigate drug-induced effects on cardiac electrophysiology and on specific biomarkers of pro-arrhythmic risk. To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels. The review highlights the variety of proposed biomarkers, the complexity of the mechanisms of drug-induced pro-arrhythmia and the existence of significant animal species differences in drug-induced effects on cardiac electrophysiology. Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market. Computational modelling and simulation has significantly contributed to the understanding of cardiac electrophysiology and arrhythmias over the last 40 years. In the second part of this paper, we illustrate how state-of-the-art open source computational modelling and simulation tools can be used to simulate multi-scale effects of drug-induced ion channel block in ventricular electrophysiology at the cellular, tissue and whole ventricular levels for different animal species. We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology.

Show MeSH
Related in: MedlinePlus