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PHARMACOKINETICS AND PHARMACODYNAMICS |
From the Departments of Pharmacotherapy (Mr Ogawa, Dr Takahashi, Dr Echizen) and Biopharmaceutics (Dr Mihara), Meiji Pharmaceutical University, Tokyo; and the Departments of Cardiology (Dr Kishi, Dr Takagi, Dr Nakazawa, Dr Miyake), Pharmacology (Dr Matsumoto, Dr Kobayashi), and Hospital Pharmacy (Dr Masuhara), St. Marianna University School of Medicine, Kawasaki, Japan.
Address for reprints: Hirotoshi Echizen, MD, PhD, Department of Pharmacotherapy, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan.
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Key Words: Pilsicainide • pharmacokinetics-pharmacodynamics • Brugada syndrome
The necessity of a PK/PD study of pilsicainide has been fueled by recent clinical findings indicating that this drug may serve as a useful probe for assessing altered sodium channel responsiveness in patients suspected of Brugada syndrome (BrS).4 BrS is considered to be a sodium channelopathy associated with a high incidence of sudden death due to fatal arrhythmias developing in subjects with structurally normal hearts. The condition is more prevalent in Asians than in whites. Characteristic electrocardiogram (ECG) patterns consisting of right bundle branch block and either coved or saddleback-shape ST-segment elevation in the right precordial leads (ie, V1-V3) are hallmarks of BrS. Since the ECG findings obtained from patients with BrS show variations over time in the same patient (eg, either exaggeration or amelioration by autonomic interventions),5 it is difficult to arrive at a conclusive diagnosis of BrS in patients with apparently normal or equivocal ECG findings despite highly suspicious clinical manifestations and presence of family history of the disease. For such patients, a pharmacological provocation test with a pure sodium channel blocker such as pilsicainide may be of value. Nevertheless, there is a paucity of knowledge about PK/PD covariates that contribute to the interindividual variability in responsiveness to the drug. In this context, we decided to undertake a population PK/PD analysis of pilsicainide in Japanese patients with cardiac arrhythmias using nonlinear mixed-effects modeling (NONMEM).6 In this article, we present data indicating that gender and renal function are the major determinants of the PK variability of pilsicainide and that drug-induced BrS-like ST-segment elevation in ECGs may be a phenotypic trait of exaggerated dromotropic effects in response to the drug.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Each patient was given an intravenous infusion of pilsicainide HCL at a rate of 1 mg/kg over 10 minutes under continuous ECG monitoring.4 The patients who were given the drug for diagnosing BrS were further divided into 2 groups according to the ECG responses to the drug. Taking the criteria proposed by Brugada et al7 into consideration, we tentatively assigned those developing drug-induced ST-segment elevation of +0.15 mV or greater from the baseline ECG tracing at J point (at the end of QRS complex),ST80 point (at 80 milliseconds after the end of QRS complex), or QT160 point (at 160 milliseconds after the beginning of QRS complex) in the V2 lead of the standard 12 leads ECG as responders to pilsicainide (group A). The remaining patients were considered nonresponders (group B). Plasma pilsicainide concentrations obtained from the patients who received the drug for treatment of atrial fibrillation were used exclusively for the PK analysis. Blood biochemistry and urinalysis were performed at the Department of Clinical Chemistry, St. Marianna University School of Medicine Hospital.
Blood Samplings and ECG Recordings
Most blood samples (5 mL each) were obtained within 120 minutes after the end of pilsicainide infusion under continuous ECG monitoring. At least 2 samples were obtained from all but 6 of the patients during this period. Additional blood samples were obtained thereafter up to 24 hours postdose when possible. Blood was collected into glass tubes containing EDTA-2Na, and plasma was separated immediately by centrifugation at 1630g for 10 minutes at 4°C and stored at –20°C until analyzed.
Continuous ECG monitoring was performed during the study, and ECGs were recorded at a paper speed of 25 mm/s at 5 minutes before the pilsicainide infusion was started (baseline) and at 0, 5, 10, 30, 60, 90, and 120 minutes after completion of drug infusion. The pharmacological effects of pilsicainide on electrical conduction in the heart were assessed by changes in P wave duration, PQ interval, PEQ interval, and QRS duration. PEQ interval is defined as the isoelectrical region from the end of the P wave to the onset of the QRS complex. It largely represents the period associated with impulse propagation from the AV node to the Hisbundle and intraventricular conduction system. Measurements of these parameters were made by one of the authors (R.O.) using a digital vernier caliper (Mitsutoyo Co, Tokyo, Japan) for at least 5 consecutive beats at each sampling point, and the mean value was calculated. Both within- and between-day intraobserver variability of measurements assessed as coefficients of variation (CVs) were <2%. The respective ECG parameters at each sampling time were expressed as degrees of change from the corresponding baseline values.
Pilsicainide Assay
Plasma pilsicainide assay was performed with a high-performance liquid chromatography–ultraviolet absorption according to Shiga et al8 with minor modifications. Briefly, we used quinidine (final concentration of 1.0 µg/mL) as internal standard and a reversed-phase column (Capcel-Pak C18, 5 µm, 250 x 4.6 mm; Shiseido Co Ltd, Tokyo, Japan) for the analysis. The mean (±SD) percent recovery of pilsicainide and the internal standard from extraction were 101% ± 4% and 105% ± 4%, respectively. Calibration curves were linear over the drug concentration range of 0.05 to 1.0 µg/mL (r > 0.999, P < .01). The within- and between-day precisions for the assay were <5% as the CV and the accuracy ranged from –9% to +16% as percentage error from the theoretical concentrations ranging from 0.05 to 1.0 µg/mL.
Population PK Analysis
The population PK analysis was performed on 237 plasma concentrations of pilsicainide obtained from 91 patients by applying the NONMEM (version V, level 1.0; University of California, San Francisco).6 A preliminary study using not only the objective function (OBJ) values but also the distribution of weighted residues to evaluate the goodness of fit of PK models indicated that the 2-compartment model with zero-order input and first-order elimination from the central compartment had a better fit than the 1-compartment model did. Therefore, further analysis was performed by the 2-compartment model. The linear 2-compartment structural model was parameterized in terms of the primary PK parameters, comprising systemic clearance (CL), volumes of the central and peripheral compartments (Vc and Vp, respectively), and intercompartmental clearance (Q) using a part of the NONMEM program (PREDPP subroutines ADVAN3 and TRANS4, the first-order conditional estimate method). Compilation of the program was performed with DIGITAL Visual Fortran (Professional Edition, version 6.0A; Digital Equipment Corp, Nashua, NH). A preliminary analysis performed with a basic model showed that CL, Vc, and Vp, but not Q, were dependent on body weight. Therefore, body-weight-normalized parameters were used for CL, Vc, and Vp in the subsequent analyses. The reason Q was independent of body weight remains unclear. The choice of statistical models for the interpatient and residual (intrapatient) variability were made based on the OBJ values and the distribution of the weighted residuals as a function of patients' individual post hoc estimates of plasma pilsicainide concentrations obtained from the different error structures (ie, proportional, exponential, or additive). Since the results indicated that the proportional error model fitted to the data better than the other models did, we adopted the proportional error model for the analysis of the interindividual and residual variances in the PK of pilsicainide.
Then, we assessed whether incorporation of patients' parameters (age, gender, serum creatinine, and predicted creatinine clearance) as covariates of CL and Vp would reduce the interindividual variability assessed by the OBJ value. Particular caution was exercised to select covariates that were mutually independent. For instance, the Cockcroft-Gault equation9 used for estimating creatinine clearance depends on age and serum creatinine concentration. Thus, creatinine clearance, rather than age and serum creatinine, was selected as a possible covariate for CL of pilsicainide. In addition, because the distribution of pilsicainide occurs rapidly (typically within 5 minutes after the end of infusion) and only a limited number of data points were available during this period, covariate analysis was not done for Vc. Regarding the model selection for continuous covariates, linear (P = 
1 + 2 • Fac), reciprocal (P = 1 + 2/Fac), power (P = 1 + Fac2), and maximum effect (P = 1 + 2 • Fac/[3 + Fac]) equations were tested, where P represents PK parameters (such as CL), Fac represents the measurements of relevant covariates, and x are the estimates calculated by NONMEM. For a categorical covariate (such as gender), the equation P = 1 •(1–Fac)+ 1 • 2 • Fac was used, where Fac equals 0 for men and 1 for women. During model building, a reduction in the OBJ value of at least 6.635 (
= .01) after incorporating a single covariate was considered statistically significant. Model building was performed by a stepwise extension of the model, adding an additional covariate at each step. The validity of a full model was checked by a stepwise backward elimination of each parameter. The goodness of fit of the final population PK model was also assessed by inspecting the scatter plots of population model-predicted as well as the observed pilsicainide concentrations and weighted residual as a function of population model-predicted pilsicainide concentrations. The accuracy and robustness of the final population PK model were assessed by use of a bootstrap method.10 From the original data set of 91 patients, 400 bootstrap sets of 91 individuals were drawn by resampling. For each of the 400 bootstrap sets, the population PK parameters were estimated and then compared with those obtained in the original data set. The final model was considered validated if no significant differences were observed.
Sequential Population PK/PD Analysis With Effect Compartment
Because no measurements of intraatrial conduction time (eg, P wave, PQ and PEQ intervals) were possible in patients with atrial fibrillation (group C), population PD analysis was conducted only in patients of groups A and B. PD responses in terms of ECG parameters elicited after pilsicainide infusion were plotted against the plasma drug concentrations obtained from actual measurements or individual post hoc estimates generated by applying the NONMEM. Since visual inspection showed that PD responses lag behind plasma drug concentrations (ie, counterclockwise hysteresis), the drug concentration-effect relationship was analyzed by the so-called effect compartment model developed by Sheiner et al.11 According to this model, Ke0, defined as the elimination rate constant of drug in the effect compartment, characterizes the time-dependent aspects of equilibrium between plasma concentration and effect. The sigmoid Emax model, in which the concentration is substituted by the effect site concentration (Ce), was fitted to the time course of PD responses by the NONMEM program. The choice of the statistical model for error structure and the analysis of patient characteristics (age, gender, the presence or absence of ST-segment elevation) relevant to the PD variability were performed as outlined in the PK analysis. The model-building process and the criteria for selecting an optimal model are essentially similar to those for PK analysis as described above.
Statistical Analysis
Multiple comparisons in the demographic and baseline ECG parameters among the 3 groups were made by ANOVA followed by the 2-sided unpaired t test with Bonferroni's correction. For comparisons of proportions, either a 
2 test or Fisher exact test was used where appropriate. The least-squares regression method was used for assessing a correlation between creatinine clearance and systemic clearance of pilsicainide, those between measured plasma drug concentrations and PD responses and those predicted by the NONMEM method. Statistical analyses were performed by the SPSS 7.5J program (SPSS Inc, Chicago, Ill). A P value of less than .05 was considered statistically significant. Data are expressed as means ± SD (range) throughout the study.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Population PK Analysis
Figure 1a and its inset show scatter plots of plasma pilsicainide concentrations versus time. Gender and CLcr were found to be significant (P < .01) covariates for CL of pilsicainide in the final population PK model, as was age for Vp. Table II lists the respective population PK parameters, coefficients of covariates possessing significant fixed (ie, systematic) effects on the PK parameters, and random effect parameters (ie, inter- and intraindividual variance and their coefficient of variations). The final population PK model for CL and Vp is represented by the following equations:
 | (1) |
 | (2) |
1 and 2 are the intercepts as a function of total body weight and slope parameters for the relationship between CLcr and CL for male patients, 3 is the coefficient of CL for women, VpTV is the typical value of peripheral volume of distribution in liters per kilogram, AGE is the age of patients in years, and 4 and 5 are the intercepts as a function of total body weight and slope parameters for the relationship between age and Vp. Taking the significant patients' covariate into account, the interindividual variability of CL, Vc, Q, and Vp and the residual variability assessed as CVs were 14.1%, 31.8%, 41.8%, and 25.2%, respectively. There was a good agreement between plasma pilsicainide concentrations predicted by the final population PK model and the observed concentrations (Figure 1b). In addition, when weighted residuals for pilsicainide concentrations predicted by the final population PK model were plotted as a function of its log-transformed plasma concentrations, the data appear to distribute uniformly around the line of Y = 0 (Figure 1c), indicating that there is little concentration-dependent bias in the estimation of the plasma drug concentration. The model validation performed with bootstrapping showed that the mean parameter estimates were within –18% and +26% of those obtained with the original data set. In addition, the 95% confidence intervals of the PK parameters obtained with bootstrapping spanned the corresponding parameters obtained in the original data set.
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Sequential Population PK/PD Analysis With Effect Compartment Model
Three patients (1 in group A and 2 in group B) were excluded from the PD analysis because they developed atrial fibrillation after the infusion of the drug. Table III summarizes the number of PD data points; the error models used to describe the interindividual variance; the improvements of the OBJ value from the basic model; the population mean of Ke0 estimated by the final effect compartment PK/PD model, Emax, EC50, and shape parameter (
); and the interindividual and residual variabilities in each conduction parameter. Using sequential population PK/PD analysis with the effect compartment model, we successfully accomplished the analysis for PK/PD data with hysteresis. Age and the development of the ST-segment elevation were significant covariates for the Ke0 of 
QRS, but clinical implications of these findings remain unclear. While the correlation between the observed and predicted PD responses was not very strong as compared with that between observed and predicted plasma drug concentrations in the PK analysis, prediction of PD responses by the population PD model was considered unbiased. As a typical example, Figure 2 shows the relationship between the observed prolongation of PEQ interval (PEQ) and that predicted by the model (Figure 2a) and the weighted residual plots as a function of predicted drug concentrations (Figure 2b). The data for other PD parameters are shown only in numerical values (Table III). The multivariate analysis revealed that patients developing the drug-induced ST-segment elevation (responders) would have 50% and 40% higher Emax values for PEQ (Figure 3) and PQ, respectively, than the nonresponders would (P < .01).
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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The finding that the systemic CL of pilsicainide correlates positively with predicted CLcr may be explained by the PK characteristics of the drug. After intravenous administration, >90% of the dose is eliminated primarily via the kidneys into urine in unchanged form in young healthy subjects.3,8 The mean PK parameters (ie, CL, Vp) estimated by our population PK analysis (Figure 1, Table II) agree well with those reported in the conventional PK studies.3,8,13 The reason female patients possess significantly (P < .01) lower body-weight-normalized CL than male patients do (Table II) remains unclear. However, we are tempted to speculate that a gender difference in the activity of certain unidentified renal OCTs may be associated with our finding. Involvement of active renal tubular secretion of the drug has been strongly suggested by previous studies3,8,13 because the renal CL of pilsicainide is approximately 2 times greater than creatinine clearance in healthy subjects. Shiga et al8 demonstrated that the renal elimination of pilsicainide is interfered with significantly by the coadministration of cimetidine, which has been shown to inhibit active tubular excretion of various cationic drugs (eg, procainamide14 and metformin15). Since pilsicainide is a cationic compound, certain OCTs may be involved in its renal elimination. To our knowledge, no data are available on whether gender difference exists in the OCT activities in human renal tubular cells. However, previous studies16-19 demonstrated that female rats have a substantially lower OCT activity for tetraethyl ammonium and other cationic small molecules and lower level of mRNA expression of OCT2, but not of OCT1, than male rats in renal tubular cells. Similarly, the activity of the renal organic anion transporter (OAT) in female rats is also significantly less than in male rats.20,21 Recently, female patients have been shown to have a 50% less systemic CL of telmisartan, an angiotensin II receptor antagonist, than do male patients.22 The drug and its conjugate are claimed to be excreted primarily into bile via an OAT, canalicular multispecific OAT.23
The sequential population PK/PD analysis using the effect compartment model revealed that the patients who exhibited a BrS-like ECG pattern (coved or saddleback ST-segment elevation >1.5 mV) after pilsicainide administration (responders) also showed significantly (P < .01) greater prolongation of PQ and PEQ intervals compared to those who did not exhibit the ECG pattern (nonresponders). Emax values of PQ and PEQ in the responders were 40% and 50%, respectively, greater than those in the nonresponders (Table III, Figure 3). These findings are consistent with previous studies5,7,24,25 conducted in patients diagnosed with BrS based on ECG responses elicited by the administration of various class I antiarrhythmics (flecainide, disopyramide, and mexiletine), using similar diagnostic criteria as employed in the present study. Our data suggest that pilsicainide-induced ST-segment elevations mimicking BrS and greater prolongation of intracardiac conduction may be attributable to exaggerated responsiveness of sodium channels to pilsicainide. Since BrS is a sodium channelopathy, these parameters may serve as useful tools for probing sodium channel function in patients who have arrhythmias suspected of BrS.
Our data should be interpreted with caution because of the limitations described below. First, our patients were not homogenous regarding demographic and clinical backgrounds. For instance, patients with atrial fibrillation (group C) were significantly older and had lower CLcr compared to those suspected of BrS (groups A and B, Table I). In addition, female patients were more prevalent (36%) in the atrial fibrillation group than in groups A and B (16%). Since we analyzed the population PK of pilsicainide by incorporating age, CLcr, and gender as independent covariates, we consider that our conclusion would be tenable for the bias associated with patient heterogeneity described above. Nevertheless, we cannot categorically deny the possibility that atrial fibrillation has a unique effect on the PK of pilsicainide independent of age, CLcr, and gender. In this context, further studies are required to clarify this issue. Second, because the number of patients who participated in our population PK and PD study was relatively small, it was not feasible to undertake model validation to assess stability and performance using a data-splitting method. Thus, further studies are necessary to confirm our findings using a larger number of subjects. Finally, there are fundamental difficulties in the accurate diagnosis of BrS. All patients who participated in the present study had a family history of cardiac sudden death, syncope episodes, and right bundle branch block coupled with typical or marginal ST-segment elevations in ECG at rest despite structurally normal hearts. ST-segment elevation of >+0.15 or +0.20 mV after the administration of class I antiarrhythmic agents has been proposed as a diagnostic criterion of BrS,7 while its sensitivity and specificity remain to be confirmed by a large clinical trial. While many genetic mutations of SCN5A have been implicated in the arrhythmogenicity of BrS,26-28 such mutations are detected in at most 20% of the patients who met the above diagnostic criteria of BrS.29,30 Therefore, caution must be exercised in interpreting our data in the light of diagnostic usefulness, particularly in association with genetic mutations of SCN5A, until a more satisfactory method for molecular diagnosis of BrS becomes available.
In conclusion, we have performed a population PK analysis of the pure sodium channel blocker pilsicainide in patients with cardiac arrhythmias. We found that gender and CLcr are independent covariates associated with systemic clearance of the drug. The population PD analysis revealed that the patients who developed BrS-like ST-segment elevation after the administration of pilsicainide also have a greater dromotropic effect in PQ and PEQ intervals than those who did not. Our data further support the idea that ST-segment elevations observed in patients with BrS may be associated with greater susceptibility of sodium channels to class IC antiarrhythmics.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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REFERENCES |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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). Note that the phenotypic trait of the ST-segment elevation >0.15 mV from the baseline tracing is a significant (P < .05) covariate for the maximum prolongation of