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Identification of Ikr kinetics and drug binding in native myocytes.

Zhou Q, Zygmunt AC, Cordeiro JM, Siso-Nadal F, Miller RE, Buzzard GT, Fox JJ - Ann Biomed Eng (2009)

Bottom Line: Determining the effect of a compound on I (Kr) is a standard screen for drug safety.Often the effect is described using a single IC(50) value, which is unable to capture complex effects of a drug.Although the method was developed for I (Kr), the same strategy can be applied to other ion channels, once appropriate channel-specific voltage protocols and qualitative features are identified.

View Article: PubMed Central - PubMed

Affiliation: Gene Network Sciences, 58 Charles Street, Cambridge, MA 02141, USA. qzhou@gnsbiotech.com

ABSTRACT
Determining the effect of a compound on I (Kr) is a standard screen for drug safety. Often the effect is described using a single IC(50) value, which is unable to capture complex effects of a drug. Using verapamil as an example, we present a method for using recordings from native myocytes at several drug doses along with qualitative features of I (Kr) from published studies of HERG current to estimate parameters in a mathematical model of the drug effect on I (Kr). I (Kr) was recorded from canine left ventricular myocytes using ruptured patch techniques. A voltage command protocol was used to record tail currents at voltages from -70 to -20 mV, following activating pulses over a wide range of voltages and pulse durations. Model equations were taken from a published I (Kr) Markov model and the drug was modeled as binding to the open state. Parameters were estimated using a combined global and local optimization algorithm based on collected data with two additional constraints on I (Kr) I-V relation and I (Kr) inactivation. The method produced models that quantitatively reproduce both the control I (Kr) kinetics and dose dependent changes in the current. In addition, the model exhibited use and rate dependence. The results suggest that: (1) the technique proposed here has the practical potential to develop data-driven models that quantitatively reproduce channel behavior in native myocytes; (2) the method can capture important drug effects that cannot be reproduced by the IC(50) method. Although the method was developed for I (Kr), the same strategy can be applied to other ion channels, once appropriate channel-specific voltage protocols and qualitative features are identified.

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Use and frequency dependent block of IKr by 2 uM verapamil. Trains of pulse protocol52 as shown in the inset were applied at intervals of 0.6, 1, and 2 s. Normalized peak tail current is plotted vs. time. (a) Mean and SD (Standard Deviation) of all model predictions. SD is shown one-sided for clarity. Results for the 1-s interval not shown for clarity. (b) Prediction of the model from fitting to the averaged data. (c) Prediction of the model from the averaged parameters
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Fig9: Use and frequency dependent block of IKr by 2 uM verapamil. Trains of pulse protocol52 as shown in the inset were applied at intervals of 0.6, 1, and 2 s. Normalized peak tail current is plotted vs. time. (a) Mean and SD (Standard Deviation) of all model predictions. SD is shown one-sided for clarity. Results for the 1-s interval not shown for clarity. (b) Prediction of the model from fitting to the averaged data. (c) Prediction of the model from the averaged parameters

Mentions: We next use the verapamil–IKr models to explore the use- and frequency-dependent block of IKr by verapamil. Three different methods are used to predict the rate- and use-dependence of verapamil from the models. First, all 12 models (generated from data from all 12 cells) are simulated, producing a range of predictions. Second, 3 data sets at the same dose were averaged, and a single model is produced by estimating parameters using four averaged data sets simultaneously. Third, the 12 parameter sets are averaged to produce a single model. This method assumes that the model parameters vary independently from one cell to the next. As shown in Fig. 9, results from all three methods are qualitatively similar: verapamil block is both use- and rate-dependent, as inhibition of the current increases with both the number of pulses and at faster rates. These behaviors cannot be reproduced by using an IC50 value to modify the conductance of the current based on drug concentration. The model shown in Fig. 9c (from averaging the 12 parameter sets) exhibits a stronger drug effect than the model in Fig. 9b because it has a significantly larger binding rate; averaging the parameters biases the result toward larger values (see the Data Supplement for parameter values). A significant benefit of the first method is that it produces a range of predicted results that matches the variability of the cells used to generate the models.Figure 9


Identification of Ikr kinetics and drug binding in native myocytes.

Zhou Q, Zygmunt AC, Cordeiro JM, Siso-Nadal F, Miller RE, Buzzard GT, Fox JJ - Ann Biomed Eng (2009)

Use and frequency dependent block of IKr by 2 uM verapamil. Trains of pulse protocol52 as shown in the inset were applied at intervals of 0.6, 1, and 2 s. Normalized peak tail current is plotted vs. time. (a) Mean and SD (Standard Deviation) of all model predictions. SD is shown one-sided for clarity. Results for the 1-s interval not shown for clarity. (b) Prediction of the model from fitting to the averaged data. (c) Prediction of the model from the averaged parameters
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getmorefigures.php?uid=PMC2690829&req=5

Fig9: Use and frequency dependent block of IKr by 2 uM verapamil. Trains of pulse protocol52 as shown in the inset were applied at intervals of 0.6, 1, and 2 s. Normalized peak tail current is plotted vs. time. (a) Mean and SD (Standard Deviation) of all model predictions. SD is shown one-sided for clarity. Results for the 1-s interval not shown for clarity. (b) Prediction of the model from fitting to the averaged data. (c) Prediction of the model from the averaged parameters
Mentions: We next use the verapamil–IKr models to explore the use- and frequency-dependent block of IKr by verapamil. Three different methods are used to predict the rate- and use-dependence of verapamil from the models. First, all 12 models (generated from data from all 12 cells) are simulated, producing a range of predictions. Second, 3 data sets at the same dose were averaged, and a single model is produced by estimating parameters using four averaged data sets simultaneously. Third, the 12 parameter sets are averaged to produce a single model. This method assumes that the model parameters vary independently from one cell to the next. As shown in Fig. 9, results from all three methods are qualitatively similar: verapamil block is both use- and rate-dependent, as inhibition of the current increases with both the number of pulses and at faster rates. These behaviors cannot be reproduced by using an IC50 value to modify the conductance of the current based on drug concentration. The model shown in Fig. 9c (from averaging the 12 parameter sets) exhibits a stronger drug effect than the model in Fig. 9b because it has a significantly larger binding rate; averaging the parameters biases the result toward larger values (see the Data Supplement for parameter values). A significant benefit of the first method is that it produces a range of predicted results that matches the variability of the cells used to generate the models.Figure 9

Bottom Line: Determining the effect of a compound on I (Kr) is a standard screen for drug safety.Often the effect is described using a single IC(50) value, which is unable to capture complex effects of a drug.Although the method was developed for I (Kr), the same strategy can be applied to other ion channels, once appropriate channel-specific voltage protocols and qualitative features are identified.

View Article: PubMed Central - PubMed

Affiliation: Gene Network Sciences, 58 Charles Street, Cambridge, MA 02141, USA. qzhou@gnsbiotech.com

ABSTRACT
Determining the effect of a compound on I (Kr) is a standard screen for drug safety. Often the effect is described using a single IC(50) value, which is unable to capture complex effects of a drug. Using verapamil as an example, we present a method for using recordings from native myocytes at several drug doses along with qualitative features of I (Kr) from published studies of HERG current to estimate parameters in a mathematical model of the drug effect on I (Kr). I (Kr) was recorded from canine left ventricular myocytes using ruptured patch techniques. A voltage command protocol was used to record tail currents at voltages from -70 to -20 mV, following activating pulses over a wide range of voltages and pulse durations. Model equations were taken from a published I (Kr) Markov model and the drug was modeled as binding to the open state. Parameters were estimated using a combined global and local optimization algorithm based on collected data with two additional constraints on I (Kr) I-V relation and I (Kr) inactivation. The method produced models that quantitatively reproduce both the control I (Kr) kinetics and dose dependent changes in the current. In addition, the model exhibited use and rate dependence. The results suggest that: (1) the technique proposed here has the practical potential to develop data-driven models that quantitatively reproduce channel behavior in native myocytes; (2) the method can capture important drug effects that cannot be reproduced by the IC(50) method. Although the method was developed for I (Kr), the same strategy can be applied to other ion channels, once appropriate channel-specific voltage protocols and qualitative features are identified.

Show MeSH
Related in: MedlinePlus