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Population Pharmacokinetics and Pharmacodynamics of Meropenem in Pediatric Patients

From the Center for Anti-infective Research and Development, Hartford Hospital, Hartford, Connecticut.

Address for reprints: David P. Nicolau, PharmD, FCCP, Center for Anti-infective Research and Development, Hartford Hospital, 80 Seymour Street, Hartford, CT 06102.

	ABSTRACT

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Meropenem is a highly potent carbapenem antibiotic against gram-positive and gram-negative bacteria. Meropenem plasma concentration data from 99 pediatric patients (aged 0.08-17.3 years) were used to develop a population pharmacokinetic model. Pharmacokinetic analysis was performed using NONMEM with exponential interindividual variability and combinational residual error model. A 2-compartment model was found to fit the data best. Creatinine clearance and body weight were the most significant covariates explaining variabilities in meropenem pharmacokinetics among pediatric patients. The validated final model was used to predict meropenem plasma concentrations in 37 pediatric meningitis patients, receiving 40 mg/kg meropenem, who had minimum inhibitory concentration values of the causative pathogens and outcome available. Since the causative pathogens in all patients were eradicated, no break points for required exposure could be found. The microbiological outcomes indicate that the current clinical dosage regimen provides sufficient drug exposure to eradicate the pathogens commonly involved in pediatric meningitis.

Key Words: Meropenem • population pharmacokinetics • meningitis • pediatrics

Understanding the disposition of the drug in the specific population is important for establishing a well-tolerated and effective dosage regimen. In the case of meropenem, up to 70% of meropenem is excreted unchanged in the urine in normal volunteers,⁵ indicating that renal function is an influential factor for the disposition of meropenem in humans. It is well recognized that renal blood flow, the glomerular filtration rate, and tubular secretion change rapidly within the first year of life.⁶ The correlation between some other covariates, such as body weight and age, with the pharmacokinetic (PK) parameters of many drugs is also suggested in the literature.^7,8 So there is good reason to believe that meropenem PK in pediatric patients is closely related with some potential covariates. Unfortunately, there is only limited information on this issue to date.⁹

In this study, a population PK (PopPK) technique was employed to investigate the typical PK parameters, potential covariates, and interindividual and intraindividual variabilities of meropenem disposition in pediatric patients. An advantage of the PopPK technique compared to the traditional PK method is that it does not require intensive blood sampling in each subject, which makes it more feasible and attractive in pediatric PK studies. Another advantage is that the PopPK model, which links PK parameters with demographic or pathologic characteristics, makes it possible to predict with reasonable accuracy the PK parameters of a particular subject from his or her covariates alone without taking blood samples. In the current study, the PopPK model of meropenem in pediatric patients was developed and validated. This model was then used to predict meropenem exposure in pediatric meningitis patients, and their PK-pharmacodynamic (PD) indices, that is, percentage time above minimum inhibitory concentration (%T>MIC), the maximum concentration over MIC (C_max/MIC), and the minimum concentration over MIC (C_min/MIC), were applied to determine their association with microbiological outcomes in these patients.

	MATERIALS AND METHODS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Data Assembly
The present study in PK and PD data analysis was approved by the Institutional Review Board at Hartford Hospital (Hartford, Conn). Pediatric patients participating in 3 previously conducted multicenter clinical trials took part in the present PopPK analysis. The patients were diagnosed with bacterial meningitis or other infections and received meropenem therapy. Plasma samples were obtained over an 8-hour period after the first dose of 10, 20, or 40 mg/kg of meropenem infusion over 5 or 30 minutes. Meropenem concentrations were measured using a validated high-performance liquid chromatographic method.¹⁰ Body surface area (BSA) was estimated from total body weight alone¹¹ because height data were not available for most patients. Creatinine clearance (CLcr) was estimated using the Cockcroft and Gault equation.¹² PD analysis was conducted on a subset of pediatric meningitis patients with available MIC data on the causative pathogens.¹³ Because of the age difference among the pediatric patients, the dose intervals of 8, 12, or 24 hours were applied to these patients, who received 40 mg/kg of meropenem with infusion over 30 minutes.

PopPK Model Development
The nonlinear mixed-effects model program (NONMEM; version V, level 1.1, double precision)¹⁴ was used for the PopPK model development (Globo-Max; Hanover, Md). First-order conditional estimation was used throughout the analysis. Both 1-compartment and 2-compartment models were fit to the data when choosing the basic structural model. Model discrimination was performed by Akaike information criterion (AIC) values and by visually checking the fitting plots. An exponential model was used to describe the interindividual variability, and the combined proportional and additive model was used to describe the intraindividual variability.

The effects of the patient characteristics on meropenem PK parameters were first investigated graphically. The individual PK parameters obtained from the basic PK model by posterior conditional estimation technique were plotted against each covariate (Cov) separately. The following covariates were examined: age, weight, BSA, serum creatinine concentration (SCr), CLcr, and unit body weight dosage (dose/weight). The continuous covariates showing correlation with the PK parameters were normalized to their corresponding medians and then introduced into the model as shown by equation 1.

(1)

where P_k is the PK parameter, _k1 is the typical value of the PK parameter in the population, _k2 is the effect coefficient of the covariate, Cov is the value of the covariate, and Cov_median is the median of the covariate in the population under investigation.

The significance of the influence of the covariates was evaluated by the changes of the minimum value of the objective function (OBJ). Changes of the OBJ on the addition of 1 covariate approximate the ² distribution with 1 degree of freedom. A decrease of greater than 3.84 (P < .05) was considered statistically significant during the covariate screening process. The full model was built by incorporating all the significant covariates. The final model was developed by a backward deletion technique. The coefficients in the full model were excluded from the model one by one. The OBJ was compared with that of the full model. A change of greater than 7.88 (P < .005) was considered statistically significant. The goodness of fit was evaluated by checking the plots of weighted residual versus predicted concentrations and observed concentrations versus predicted concentrations.

Model Validation
The reliability and stability of the PopPK model developed was assessed by a nonparametric bootstrap procedure.¹⁵ During this process, each patient was randomly sampled with replacement from the original data set to form new data sets containing the same number of patients as the original data set. One thousand bootstrap data sets were generated, and each of them was fitted individually to the final PopPK model. All of the model parameters were estimated, and their bootstrap 95% confidence intervals (CIs) were calculated.

The predictive performance of the final PopPK model was evaluated using the bootstrap approach.¹⁵ Two hundred bootstrapped models (models generated from fitting the final model to the bootstrap data sets) were randomly selected from the 1000 bootstrap runs. They were then fitted to their corresponding bootstrap data sets and the original data set separately. Mean absolute error (MAE), the absolute difference between observed and model-predicted concentrations, and mean squared error (MSE), the squared difference between observed and model-predicted concentrations, were employed as the metrics for evaluating the predictive performance. The difference between the MAE obtained from fitting the bootstrap model to the bootstrap data set and the MAE from fitting the bootstrap model to the original data set was designated as "optimism" (OPT1). Similarly, the difference between the 2 MSEs was designated as OPT2. The average OPT1 and OPT2 of the 200 paired runs were calculated and added to the original MAE and MSE obtained when fitting the final model to the original data set, thus producing the improved MAE and MSE (MAE_imp and MSE_imp), respectively. The percentages of OPT1 and OPT2 relative to MAE_imp and MSE_imp were calculated.

PD Data Analysis
The microbiological responses in pediatric meningitis patients were categorized as eradication (negative cerebrospinal fluid [CSF] cultures at the end of therapy or sterile cultures on a subsequent CSF analysis) or persistence (positive CSF cultures on the second lumbar puncture after the therapy stopped) in the clinical trial. The final PopPK model was used to calculate the PK parameters including V_c, K₁₀, K₁₂, and K₂₁ of each patient with an identified causative pathogen. Then, WinNonlin (version 3.3; Pharsight Corp, Mountain View, Calif) was used to simulate their steady-state concentrations. An 8% protein-binding ratio was employed for calculating the unbound drug concentrations.¹⁶ %T>MIC, C_max/MIC, and C_min/MIC of the unbound drug were obtained as the PK-PD indices for predicting the microbiological outcomes. Classification and regression tree analysis was used to select the break points of the PK-PD indices that predicted a positive microbiological response.

	RESULTS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Population PK Analysis
A total of 425 plasma concentrations from 99 pediatric patients enrolled in 3 trials were used for the present PopPK analysis. The concentration-time profile of these patients for the PopPK study is shown in Figure 1. The demographics of the patients in the PopPK are shown in Table I. AIC values and fitting plots indicated that a 2-compartment model described the data best. Therefore, the PK parameters under investigation were total clearance (CL, L/h), volume of distribution of the central compartment (V_C, L), intercompartment clearance (Q, L/h) and volume of distribution of the peripheral compartment (V_P, L).

View larger version (13K): [in this window] [in a new window]   Figure 1. Plasma concentrations of meropenem in 99 pediatric patients after a single dose.

Among the examined covariates, SCr and unit body weight dosage did not demonstrate an effect on any of the PK parameters. CLcr had a significant correlation with CL, which is in accordance with the established finding that up to 70% of meropenem is excreted from the body via urine.⁵ When age, weight, and BSA were incorporated into the basic model for each of the PK parameters, all of them markedly improved the fitting (decrease of OBJ greater than 3.84, P < .05). Because of the high collinearity (r > 0.9) between these 3 covariates, only 1 of them should be added on each of the PK parameters to avoid a collinearity effect.¹⁷ So it was decided to incorporate the covariate that produced the largest decrease of OBJ. The resultant full model included CLcr and weight as covariates for CL, weight for V_c and V_p, and age for Q.

During the backward deletion process to find the final model, it was found that age could be excluded from the full model without causing a significant increase of the OBJ (4.34, P > .01), while all the other coefficients should remain in the model. The final model was as follows: CL (L/h) = 4.22 x (CLcr/53.35)^0.29 x (weight/13.5)^0.86, V_C(L) = 2.62 x (weight/13.5)^1.06, Q(L/h) = 2.97, V_P(L) = 2.50 x (WT/13.5)^0.30. The interindividual variabilities of CL, V_c, Q, and V_p were 30%, 28%, 30%, and 48%, respectively. The proportional and additive residual errors were 18% and 0.29 mg/L, respectively. The final PopPK model parameter estimates are presented in Table II. As can be seen from Figures 2 and 3, the final model adequately described the data.

View larger version (15K): [in this window] [in a new window]   Figure 2. Demonstration of goodness of fit: weighted residual versus predicted concentration.

View larger version (14K): [in this window] [in a new window]   Figure 3. Demonstration of goodness of fit: predicted versus observed concentration.

Using the Bayesian estimation method, the individual PK parameters of the patients were also obtained. The means ± standard deviations of CL, V_C, Q, V_P, T_1/2() and T_1/2(ß) of the 99 patients were 5.6 ± 4.0 L/h, 3.3 ± 2.7 L, 3.0 ± 0.4 L/h, 2.6 ± 1.3 L, 0.2 ± 0.1 hours, and 1.3 ± 0.7 hours, respectively.

The reliability and stability of this PopPK model were confirmed by the results of the nonparametric bootstrap procedure. As shown in Table II, the mean population parameter estimates obtained from the bootstrap procedure were generally comparable to the estimates from the final model, indicating little bias in the parameters. Furthermore, the estimates of the structural parameters and random effects from the final model all fell in the 95% CIs of the corresponding parameters obtained by the 1000 bootstrap runs, indicating the final model was fairly robust. The average OPT1 and OPT2 of the 200 paired runs were 0.16 mg/L and 7.52 mg²/L², respectively. The average OPT1 and OPT2 constituted 3.2% and 6.9% of the MAE_imp and MSE_imp, respectively.

PD Analysis
There were 37 meningitis patients with available MICs ranging from 0.004 to 0.6 µg/mL and outcome data. Most patients in this study were infected with Haemophilus influenzae type B (26/37). The other causative pathogens included Streptococcus pneumoniae (5/37), Neisseria meningitidis (5/37), and Salmonella Grp C (1/37). The medians (range) of %T>MIC, C_max/MIC, and C_min/MIC, obtained from the simulated unbound drug concentration, were 100% (72%-100%), 3266.36 (154.14-25 595.68), and 5.19 (0.18-53.87), respectively. The causative pathogens in the CSF were eradicated in all 37 patients at the end of treatment. Therefore, PD indices could not be correlated with a positive or negative effect.

	DISCUSSION

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
There is very limited information on the population PK of meropenem in pediatrics in the literature so far. The sole study we found was carried out by Parker et al in 1995,⁹ in which 65 pediatric patients were used for the development of the PopPK model of meropenem. Similar to our study, the authors found that CLcr and age were the most important determinant of clearance; body weight correlated linearly with V_c and V_p and nonlinearly with Q. However, it seemed that their model development process was fundamentally problematic; that is, the interindividual variabilities of the PK parameters in the PK structural model could not be obtained. Their solution for this problem was skipping the fundamental step and incorporating CLcr and body weight from the very beginning. Although the fixed-effect and random-effect parameters were obtained from the final model, no goodness-of-fit demonstration was shown in the article. Of note was that 1 individual with the highest concentration observed was poorly predicted by the model, indicating some limitations in the PopPK model developed.

The reasons for their failure in finding the basic model could be that the sample size was not large enough and the statistical model was not appropriate. In the current study, another 34 pediatric patients plus the 65 patients in Parker et al's study were included in the analysis. Through a complete PopPK model development procedure, a model adequately describing the data was established. The goodness of fit was confirmed by the scatter plots of weighted residual versus predicted concentrations and observed concentrations versus predicted concentrations.

An important indicator for the accuracy of the PopPK model is the comparability between the PK parameters obtained by the population analysis method and traditional approach. In this study, the CL (5.6 ± 4.0 L/h), V_ss (5.9 ± 3.8 L), and T_1/2(ß) (1.3 ± 0.7 hours) were consistent with values calculated by the traditional PK method (0.34 ± 0.04 L/h/kg, 0.43 ± 0.06 L/kg, and 1.1 ± 0.5 hours, respectively).¹⁸

Age was found to be the significant covariate for Q during the development of the covariate model; however, the influence disappeared when the backward deletion procedure was carried out to find the final model (OBJ = 4.3, P > .005). The most significant impact of excluding age from the model was that the coefficient of variation (CV) of the estimation of Q decreased substantially from 213% to 33%, indicating that this PK parameter could be estimated more precisely in the absence of a covariate. The CVs of CL, V_c, and V_p also had small improvement or stayed at the same level (from 22%, 52%, and 36% to 20%, 46%, and 37%, respectively). The reason for this finding could be that intercompartment clearance was a complex process involving not only body composition and organ maturation but also patients' disease status. Since Q is not the primary interest of clinical application, it was decided that no covariate would be included for this parameter.

The current PopPK model was developed not only for description purposes but also for prediction use. Now, an agreement has been achieved that a PopPK model with intention to be used for predictive purposes should be validated before its application. In this study, bootstrap,¹⁹ a resampling technique, was chosen for the model validation because it allows the use of the entire data set for model development when there is no external data set for validation. This property is especially important in pediatric settings in which ethical and medical concerns prevent recruitment into studies. Bootstrapping has been recognized as a method for assessing the reliability, stability, and validation of the PopPK model.²⁰

Table II showed that the mean parameter estimates obtained from the 1000 bootstrap replicates of the data were generally comparable to the estimates from the traditional NONMEM analysis. Furthermore, the estimates of the structural parameters and random effects from the final model all fell in the 95% CIs of the corresponding parameters obtained by the bootstrap runs, indicating that the final model was fairly robust.

The predictive performance of PopPK models can be assessed with several metrics. In the current analysis, 2 frequently used ones, MAE as a measure of accuracy and MSE as a measure of precision, were employed. Theoretically, the prediction errors would be larger when a model was fit to a data set other than the one from which it was derived. Thus, OPT was calculated and added to the original prediction error. The resulting metrics, MAE_imp and MSE_imp, were an estimation of the prediction error that might happen when the final PopPK model was extrapolated to another population. One may speculate that since nonparametric bootstrap technique constructs validation data sets by recombining the individuals from the original population in a random way, the resultant data set may be more similar to the original one than a real external data set. So MAE_imp and MSE_imp may still underestimate the true prediction error. However, Ette et al¹⁵ pointed out that when one develops a PopPK model for predictive purposes, it is assumed that the population from which the model was derived will be very similar to (or representative of) the population to which the model is applied. Nevertheless, the science of validating complete probability models is still evolving; bootstrapping remains a good choice for PopPK model validation as stated by the Food and Drug Administration.²⁰ In the current study, the average OPT1 and OPT2 constituted only 3.2% and 6.9% of the MAE_imp and MSE_imp, respectively. These numbers were less than the limit of 15%,²¹ suggesting that the model had no substantial deficiency and could be applied to an external data set.

Data from animal studies and clinical trials demonstrate that meropenem is a time-dependent antibiotic, meaning that %T>MIC is the most important PD index for predicting its efficacy.^22-24 A threshold of 40% T>MIC (free drug) was proposed to achieve bactericidal outcomes against Escherichia coli and Pseudomonas aeruginosa.^23,25,26 Thus, it is desirable that the clinicians determine the %T>MIC of different dosage regimens before they begin therapy. However, this is not realistic under the traditional PK study circumstances because of the difficulty of taking serial blood samples from patients. The PopPK method, by correlating PK parameters with the demographic and clinical factors of patients, provides the possibility to investigate the PK-PD relationships without taking blood samples.

In the current study, the PopPK model was used to predict the PK parameters of 37 pediatric patients with meningitis. The %T>MIC of each patient was determined from the simulated plasma meropenem concentrations. It was found that 89% (33/37) of patients receiving 40 mg/kg meropenem every 8, 12, or 24 hours achieved 100% T>MIC, and the lowest exposure obtained was free drug concentration above the MIC for 72% of the dose interval. All exceeded the threshold of 40%, which was not surprising since the MICs of all the isolates ranged from 0.004 to 0.6 µg/mL and were susceptible to meropenem according to the current Conventional Clinical and Laboratory Standards Institute break points. The calculated result of %T>MIC was in agreement with microbiological outcomes as bacteria eradication was demonstrated in all patients at the end of therapy. Therefore, a dosage regimen of 40 mg/kg appears adequate to treat pediatric patients with meningitis caused by pathogens with MICs up to 0.6 µg/mL. Since pathogens with higher MICs were not available, we could not determine the PD effectiveness of meropenem against bacteria with higher MICs.

In summary, a PopPK model was successfully developed to describe the correlation between the PK parameters and covariates as well as the interindividual and intraindividual variabilities of meropenem in pediatric patients. It was found that the clearance of meropenem was correlated with creatinine clearance and body weight and that the volume of distribution was correlated with body weight. The bootstrap procedure confirmed the reliability and stability of the PopPK model and its appropriateness for predictive purposes. And for the first time, the validated PopPK model was used to predict the plasma concentrations of meropenem in pediatric patients without blood sampling. The calculated %T>MIC in the patients under investigation revealed that the current 40 mg/kg dosing regimen provides sufficient meropenem exposures to ensure the eradication of the common causative pathogens in pediatric meningitis.

	ACKNOWLEDGEMENTS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Funding for this study was provided by AstraZeneca Pharmaceuticals L.P. (Wilmington, Del).

	REFERENCES

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Request Reprints Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (4) Citing Articles via Google Scholar Google Scholar Articles by Du, X. Articles by Nicolau, D. P. Search for Related Content PubMed PubMed Citation Articles by Du, X. Articles by Nicolau, D. P. Social Bookmarking                   What's this?