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PHARMACOKINETICS AND PHARMACODYNAMICS |
From the Department of Pharmaceutics, University of Florida, Gainesville (Dr Bhattaram, Dr Nagaraja, Dr Derendorf); Department of Clinical Pharmacokinetics (Dr Kroesser, Dr Kovar); and Department of Clinical Sciences (Dr Machnig) and Clinical Pharmacology (Dr Peters), Merck KGaA, Darmstadt, Germany.
Address for reprints: Hartmut Derendorf, PhD, Department of Pharmaceutics, PO Box 100494, JHMHC, University of Florida, Gainesville, FL 32610.
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONREFERENCES |
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Key Words: Eniporide • pharmacokinetics • acute myocardial infarction
Eniporide (EMD 96 875) is a novel and selective inhibitor of NHE-1 and a compound of the benzoylguanidine group. Its chemical name is 2-methyl-5-methylsulfonyl-4-(1-pyrrolyl)-benzoylguanidine methanesulfate (Figure 1). The des-4-pyrrolo-substance (EMD 112 843) has been identified as the main metabolite; it possesses a 4-amino group instead. Eniporide and its metabolite inhibited the Na+-H+ exchange in acidified rabbit erythrocytes with an IC50 value of 4.7 ± 0.6 nM and 15 ± 3nM.4 Eniporide is expected to be beneficial in patients with ischemic heart disease.
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Pharmacokinetic-pharmacodynamic (PK-PD) evaluation of eniporide with platelet swelling time as a biomarker has been reported earlier.5 Eniporide showed linear pharmacokinetics (2.5-100 mg) with an average half-life of 2 hours. The mean total body clearance and volume of distribution were 34.4 L/h and 77.5 L, respectively. An average of 43% of the dose was recovered unchanged from urine. Plasma concentrations of the major metabolite were lower than that of the parent drug, and an average of 27% of the dose was found in urine as that metabolite. A direct Emax model was found to describe the effect of eniporide on platelets. The average concentration required for half-maximum effect (IC50) was 12 ng/mL.
The goals of the current analysis are (1) to develop a population pharmacokinetic model for eniporide and its metabolite in healthy subjects and patients undergoing myocardial reperfusion therapy and (2) to explore the relationships between covariates and eniporide disposition in patients.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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Healthy Subjects
The major inclusion criteria for healthy subjects were the following: age (18-45 years), gender (male), race (Caucasian), weight (±15% of normal body weight range relative to height and frame), medical history without major pathology, all physical examination parameters without signs of clinically relevant pathology, computerized electrocardiogram recording (12-lead) without signs of pathology, and clinical laboratory tests. No subjects were enrolled in the study if there was any evidence of clinically relevant pathology (especially cardiovascular, gastrointestinal, and hematological diseases), history of alcohol abuse or drug addiction, or positive serology to HbsAg, hepatitis C virus (HCV), or HIV 1 and 2. The healthy subjects were not allowed to take any medication or multivitamin preparations within 14 days prior to entrance into the clinical research facility and for duration of the study. During the 48 hours preceding entrance into the clinic and during the stay in the clinical research facility, subjects had to abstain from taking methylxanthine-containing beverages or food and alcohol.
Patients
The effect of eniporide on the outcome of patients with acute myocardial infarction (AMI) was studied in the ESCAMI trial.6
Inclusion criteria for patients were as follows: AMI, age (18-75 years), and gender (male or female). Women were included if they were postmenopausal, using reliable contraception or an intrauterine device, or if they were surgically sterilized. Patients who were pregnant or lactating, had participated in another clinical study within the past 30 days, had received prehospital thrombolysis, or presented with Killip class IV heart failure, left bundle branch block, or known alcohol or illicit drug abuse were excluded from the study. Patients were treated for AMI according to the local hospital standards. All concomitant medications and procedures were documented.
The studies were conducted in accordance with the principles of the Declaration of Helsinki. All participants were informed verbally and in writing about the objectives, procedures, and risks of study participation and gave their written informed consent.
Sampling and Bioanalysis
Plasma samples for the analysis of eniporide and its metabolite were collected over 48 hours. For the analysis of eniporide and its metabolite, blood samples of 10 mL were drawn by an indwelling catheter or direct venipuncture. Each sample was collected into a heparin-containing tube. Samples were immediately cooled to 0°C and subsequently centrifuged within 30 minutes at 4°C at 1500g for 10 minutes. Plasma was separated, rapidly transferred to a polypropylene tube, stoppered, and stored at –20°C until analysis by high-performance liquid chromatography (HPLC).
Pharmacostatistical Modeling
Structural Model Identification
The structural model to describe the concentration-time profile of eniporide was developed in NONMEM (Version V, Globomax, Hanover, Md) with Compaq Visual Fortran (Version 6.5A).7 Wings for NONMEM (Version 3.04, developed by Dr N. H. Holford, http://wfn.sourceforge.net) was used for executing the NONMEM analysis.
The natural logarithms of plasma concentration-time data of both eniporide and its metabolite from all studies were pooled into a data set. The parent compound was described using 2- and 3-compartment model(s) with first-order elimination from the central compartment to identify the optimal structural model. The model was parameterized in terms of CL (clearance from central compartment), V (distribution volume for central compartment), and first-order transfer rate constants (K12, K21 and/or K13, and K31).
The model describing the time course of the metabolite was parameterized in terms of first-order rate constants describing the formation (K14) and elimination of metabolite (K40). The parent drug and its metabolite were fitted simultaneously using the NONMEM subroutine ADVAN6, which makes use of differential equations to describe the rate of mass transport between different compartments as amounts (Figure 2). The correction factor (366/416) was used to account for the differences in the molecular weights of the parent drug and its metabolite. Because the volume of distribution of the metabolite was unknown, it was fixed to 10 times the population estimate of parent drug (V). The data were analyzed by both first-order (FO) and first-order conditional estimation (FOCE) procedures. Initial estimates for the analysis were obtained using KINETICA (Version 4.1, Innaphase Corp, Philadelphia, Pa).
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jCL represents the difference between the jth individual's CL value and that predicted by the regression model; jCLs are independent, identically distributed (i.i.d.) random variables with mean zero and variance 
2.
Residual intraindividual variability was identically distributed and was modeled using the additive error model. The additive error model is described by
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ij is a normally distributed parameter with mean zero and variance 
2. With the fixed and random effects chosen, empirical Bayes estimates of the pharmacokinetic parameters were subsequently obtained using the POSTHOC option in the NONMEM program.
The performance of the model was judged using both statistical and graphical methods.8 Furthermore, standard errors for all parameters were calculated using the COVARIANCE option of NONMEM. For graphical model diagnostics, SPLUS 2000 was used.
Covariate Model Building
A covariate model for eniporide was explored on the information obtained from patients. Covariates such as age, gender, creatinine clearance, height, and weight were examined for their significance on the pharmacokinetics of eniporide (Table II). Creatinine clearance was calculated from serum creatinine, age, weight, and gender using the Cockcroft-Gault formula.9 Creatinine clearance values greater than 140 mL/min were replaced with 140 mL/min due to the limitations of the formula used. The covariate model building involved stepwise addition and backward elimination of covariates selected by the graphical method (Correlations). Hierarchical models were compared statistically using a likelihood ratio test. For nonhierarchical models, comparison was based on direct comparison of objective function and comparison of residuals. With use of the likelihood ratio test, stepwise additions 
and deletions 
determined the final model.
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Model Validation
A bootstrap resampling technique was applied as internal validation. The final model was fitted to the replicate data sets using the bootstrap option in Wings for NONMEM, and parameter estimates for each of the replicate data sets were obtained.10 The mean parameter estimates of the bootstrap replicates were compared with the estimates of the original data set.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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Eniporide
The model-building process was initially performed for the parent drug (untransformed and log-transformed data). Both 2- and 3-compartment models were evaluated with FO and FOCE methods. The model diagnostics (weighted residuals vs time) showed a distinct trend especially toward the later time points (16-24 hours) for a 2-compartment model (plots not shown). In an ideal situation, the weighted residuals (the weighted difference between the observed and the predicted concentrations based on the parameter estimates for the typical individual) must be randomly distributed around the zero value. The data were then analyzed by a 3-compartment model. No trend was observed in the plots of the weighted residuals versus time. Model- and Bayesian-predicted concentrations were symmetrically distributed around the line of unity (Figure 4). No significant differences were observed in the estimates by FO and FOCE methods. The model was more stable when the log-transformed data were analyzed. In further analysis, it was decided to use log-transformed data with the FO method.
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Eniporide + Metabolite
A structural model was developed by simultaneous analysis of both eniporide and its metabolite using log-transformed data and the FO method. Model diagnostics indicated that a 1-compartment model was adequate to characterize the pharmacokinetics of the metabolite. Covariate analysis, however, was not performed for the metabolite as it was reported to be at least 3 times less active than the parent drug.
Covariate Analysis
The covariate analysis consisted of age, weight, height, gender, and creatinine clearance. The results of stepwise forward analysis showed that weight (P < .005) and creatinine clearance (P < .005) were significant covariates on V, whereas age was a significant covariate on CL of eniporide. In the next step, the covariates identified in the previous step were included in the model. Upon backward elimination, it was found that creatinine clearance on V and age on CL were significant covariates (P < .001) for eniporide (Table III).
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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Eniporide has a mixed elimination profile with a less active metabolite; half of the dose is eliminated unchanged in urine. The data from healthy subjects and patients were combined to get the final estimates of fixed- and random-effect parameters. A 3-compartment model was found to best describe the data of eniporide, whereas a 1-compartment model was sufficient to explain the metabolite data. The estimates of CL and V of eniporide were slightly different between the healthy subjects and patients; CL was approximately 30% lower and V approximately 17% lower in the patient population compared to healthy volunteers. The summary of the pharmacokinetic parameters of eniporide and its metabolite is shown in Table IV.
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Relationships between CL, V of eniporide, and covariate data from patients were studied. Statistical significance was achieved for the effect of age on clearance and creatinine clearance on volume of distribution (P < .001). These covariate effects explain the pharmacokinetic differences between the populations as the patient population included renally impaired patients and was, on average, 38 years older than the healthy volunteer population (Table II). However, the inclusion of covariates resulted in approximately 3% and 1% decrease in the interindividual variabilities of CL and V, respectively, indicating their clinical insignificance. Also, the observed differences in CL and V are unlikely to warrant any dose adjustment in patients.
The results of bootstrap estimates for the covariate model in patients were in relatively good agreement, indicating the stability of the model.
In conclusion, we developed and validated a pharmacokinetic model for eniporide and its metabolite, which described the data from healthy subjects and patients. No clinically relevant relationship of eniporide with any of the covariates tested was found. However, the model would be updated with additional information from special populations (hepatic impairment, renal failure, etc.) as more data are available to suggest any dose modifications to maximize the benefit-risk ratio.
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REFERENCES |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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1. Masereel B, Pochet L, Laeckmann D. An overview of inhibitors of Na(+)/H(+) exchanger. Eur J Med Chem. 2003;38: 547-554.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
2. Allen DG, Xiao XH. Role of the cardiac Na+/H+ exchanger during ischemia and reperfusion. Cardiovasc Res. 2003;57: 934-941.
3. Avkiran M, Marber MS. Na(+)/H(+) exchange inhibitors for cardioprotective therapy: progress, problems and prospects. J Am Coll Cardiol. 2002;39: 747-753.
4. Fischer H, Seelig A, Beier N, Raddatz P. New drugs for the Na+/H+ exchanger: influence of Na+ concentration and determination of inhibition constants with a microphysiometer. J Membr Biol. 1999;168: 39-45.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
5. Kovar A, Peters T, Beier N, Derendorf H. Pharmacokinetic/pharmacodynamic evaluation of the NHE inhibitor eniporide. J Clin Pharmacol. 2001;41: 139-148.[Abstract]
6. Zeymer U, Suryapranata H, Monassier JP, Opolski G. The Na(+)/H(+) exchange inhibitor eniporide as an adjunct to early reperfusion therapy for acute myocardial infarction: results of the evaluation of the safety and cardioprotective effects of eniporide in acute myocardial infarction (ESCAMI) trial. J Am Coll Cardiol. 2001;38: 1644-1650.
7. Beal SL, Boeckman AJ, Sheiner LB. NONMEM: User's Guides. San Francisco: University of California, San Francisco; 1998.
8. Ette EI, Ludden TM. Population pharmacokinetic modeling: the importance of informative graphics. Pharm Res. 1995;12: 1845-1855.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
9. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16: 31-41.[Web of Science][Medline] [Order article via Infotrieve]
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