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Optimal treatment schedule of meropenem for adult patients with febrile neutropenia based on pharmacokinetic–pharmacodynamic analysis
J Infect Chemother (2011) 17:831–841 DOI 10.1007/s10156-011-0271-9
ORIGINAL ARTICLE
Optimal treatment schedule of meropenem for adult patients with febrile neutropenia based on pharmacokinetic–pharmacodynamic analysis Yuka Ohata • Yoshiko Tomita • Mitsunobu Nakayama Kazuo Tamura • Yusuke Tanigawara
•
Received: 2 April 2011 / Accepted: 7 June 2011 / Published online: 20 July 2011 Ó Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases 2011
Abstract The objectives of this study were to develop a population pharmacokinetic (PK) model of meropenem, to simulate the percent time above minimum inhibitory concentration (%T [ MIC) at various MICs, and to estimate effective dosage regimens by calculating the target attainment rates against various strains of bacteria. A total of 209 plasma samples (1–3 concentrations per patient) were obtained from 98 adult Japanese patients with febrile neutropenia in an open-labeled Phase 3 study. The final population PK model was fit to a two-compartment model with zero-order input. Creatinine clearance had a positive significant correlation with CL. Gender had a significant effect on Vc; however, this effect was small, and the PK profile in male patients was similar to that in female patients. The population PK parameters developed in this study are useful in simulating PK profiles of meropenem at various dosage regimens precisely for calculation of %T [ MIC. The PK–PD analysis indicated that 0.5 g every 6 h (q6h) was more effective than 1 g q12h, although provided 2 g per day in total. A meropenem dosage regimen of 1 g q8h and/or longer infusion duration was better against a pathogen of comparatively low sensitivity, Pseudomonas Y. Ohata Y. Tomita M. Nakayama Drug Development Division, Dainippon Sumitomo Pharma Co., Ltd, Osaka, Japan K. Tamura Division of Medical Oncology, Hematology and Infectious Diseases, Department of Medicine, Fukuoka University, Fukuoka, Japan Y. Tanigawara (&) Department of Clinical Pharmacokinetics and Pharmacodynamics, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan e-mail: [email protected]
aeruginosa (for MIC C2 lg/ml). Although causative bacteria were identified in a small number of patients, the target attainment rates at 75%T [ MIC (89%) were comparable to microbiological response (89%). The present PK–PD analyses under various conditions are useful in the treatment of febrile neutropenia. Keywords Meropenem %T [ MIC Pharmacokinetics Pharmacodynamics Febrile neutropenia Monte Carlo simulation
Introduction Meropenem is a widely used carbapenem antibiotic with high activity against most gram-positive and gram-negative bacteria. It is also active against clinically relevant aerobic, nutritionally fastidious, and anaerobic bacterial species. Therefore, meropenem is commonly used in empiric therapies for infectious diseases. A number of pharmacokinetic (PK)–pharmacodynamic (PD) simulations (so-called Monte Carlo simulations) for meropenem have been reported, and such simulations are considered to be an important approach for the prediction of antibacterial efficacy of meropenem [1–10]. For PK simulation, some reports used a simple compartment model that did not describe covariates for PK variability or individual variability [1, 2, 6, 7, 9]. For an important PD index for meropenem, all the previous studies considered the percent time above minimum inhibitory concentration (%T [ MIC). Levels of 20–30%T [ MIC were proposed to achieve bacteriostatic effects and 40–50%T [ MIC was proposed to achieve bactericidal outcomes [1–10]. Some reports estimated target attainment rates of bacteriostatic exposures (target, 30%T [ MIC) and/or bactericidal
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exposures (target, 50%T [ MIC) after various dosage regimens against bacteria for various MICs [1, 7, 10]. Other studies estimated target attainment rates against various strains of bacteria based on extrapolated MIC distribution data from surveillance studies [2, 7, 11]. Additionally, clinical effectiveness was also discussed with regard to PK–PD target attainment [11]. Most of the PK data for calculation of PK–PD target attainment were obtained from healthy subjects [1, 2, 6, 7, 9] or from patients with a mix of various infections [3–5, 11, 12], such as intraabdominal infections, community-acquired pneumonia, or ventilator-associated pneumonia [3]. Using data only from patients with febrile neutropenia, Lee et al. [8] and Ariano et al. [13] conducted PK–PD analysis. However, they did not predict PK–PD target attainment for various MICs against various other bacteria based on their model. The present study was primarily aimed to develop a population PK model of meropenem in Japanese adult patients with febrile neutropenia. The relationships between %T [ MIC and clinical response or bacteriological efficacy were investigated, and %T [ MIC attainment of meropenem for various dosage regimens were simulated to consider more effective dosage strategy. The MIC distribution data of clinical isolates was obtained from a Japanese surveillance study conducted in 2006 [14]. The optimal dosage regimens were proposed based on PK–PD simulations for bacteriostatic and bactericidal effects against various bacteria with various MICs.
Patients and methods Patient data
J Infect Chemother (2011) 17:831–841 Table 1 Patients’ characteristics Characteristics
Mean ± SD (or count) (n = 98)
Gender (male:female)
64:34
Age (years)
59.3 ± 12.9
Body weight (kg) Serum creatinine (mg/dl)
57.2 ± 11.2a 0.730 ± 0.225
Creatinine clearanceb (ml/min)
91.7 ± 40.7a
a
Range
18.0–78.0 39.0–114a 0.360–1.33 30.7–327a
n = 97
b
Creatinine clearance was calculated using the Cockcroft and Gault equation
informed consent was obtained from all the patients themselves or their legal representatives. The purpose of the Phase 3 study was to evaluate the efficacy, safety, and PK profile of meropenem in the treatment of patients with febrile neutropenia. The primary endpoint was to achieve each defervescence within 4 days from the beginning of the treatment. When patients obtained a normal temperature defined as a maximum \37.5°C and body temperature decreased by more than 0.5°C, the result was judged as effective. The secondary endpoints were clinical response, microbiological efficacy, and defervescence within 7 days. Clinical efficacy was evaluated by improvement of symptoms including fever, microbiological efficacy, and the results of laboratory tests. It was categorized into three groups: success, failure, or unknown. The microbiological response during the treatment was also classified into three groups: eradicated, failure, or unknown.
Patient data were obtained from an open-labeled Phase 3 clinical study conducted in Japan from July 2006 through February 2008 [15]. Study subjects were patients with febrile neutropenia defined as C38.0°C of fever, or not lower than 37.5°C of fever persisting for one or more hours, with no identified pathogenic bacteria. The number of neutrophils in those patients was\500/mm3, or\1000/mm3 which was predicted to decrease to \500/mm3. The age of the actual subjects ranged from 18 to 78 years (mean ± SD, 59.3 ± 12.9 years), and their weight (±SD) was 57.2 ± 11.2 kg. The demographic background is summarized in Table 1.
Meropenem dosage
Study protocol
The plasma concentrations (209 data points) of meropenem in 98 adult patients were determined using high-performance liquid chromatography (HPLC). All plasma samples were analyzed at Mitsubishi Chemical BCL (Tokyo, Japan). Each plasma sample was mixed with an equal volume of 1 M 3-morpholinopropanesulfonic acid (MOPS) buffer solution as a stabilizer, transferred to an ultrafiltration
The open-labeled Phase 3 clinical study was conducted in Japan in accordance with the Declaration of Helsinki and domestic regulations on clinical trials (Japanese Good Clinical Practice guidance). The study was approved by the ethical committee in each participating hospital. Written
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Meropenem was administered intravenously at a daily dose of 1 g every 8 h (q8h), administered by infusion for 0.5 h or longer for 7–14 days. Plasma samples were collected for meropenem assay at 15–25 min after the start of infusion or at 15 min to 8 h after the end of infusion. One to three samples of blood per patient were drawn in heparinized tubes. After centrifugation, plasma was frozen and stored until assay. Assay of meropenem concentrations in plasma
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device, and centrifuged at 12,000 rpm (approximately 8,000 g) for 5 min. Filtrate was injected into the HPLC system for analysis and chromatographically separated on a reversed-phase column (Hypersil C18, 4.6 mm U 9 10 cm, 3 lm). The mobile phase consisted of PIC A (tetrabutyl ammonium phosphate) solution and methanol (5/1, v/v). The flow rate was 1.0 ml/min. UV detector wavelength was 300 nm. The absolute calibration method was adopted for determination of meropenem. Meropenem in the plasma was quantified using the peak area of standard samples for calibration. This analytical method was validated for selectivity (no peak interfering with peak of meropenem, n = 6), linearity (r = 0.999), accuracy (intraday assay: -4.4% to 3.4%, n = 5; interday assay: -5.7% to 0.9%, n = 5), precision (intraday assay: -2.9% to 4.5%, n = 5; interday assay: 3.6% to 7.4%, n = 5), recovery (97.2% to 103.6% at 0.05, 10, or 200 lg/ml), calibration curve (0.05–200 lg/ml), and stability (stored first at -20°C for 96 h, and then, at -80°C for 60 days). Bacteriological tests Venous blood was drawn from the patients for culture and sensitivity tests by a sterile technique. If necessary, sputum, feces, or urine of the patients was also examined. The samples were collected before the drug treatment, at 4 and 7 days after the start of treatment, and at the end of the study. Isolation and identification of microorganisms followed standard procedures. The MICs of meropenem against the isolated strains were measured according to the Clinical Laboratory Standards Institute guidelines [16]. Population PK model development Model building and model validation were conducted in accordance with FDA and European Medicines Agency (EMEA) guidelines [17, 18] related to population PK analysis. The population PK analysis was performed using a nonlinear mixed effect model program, NONMEM, double precision, version V, level 1.1 (Globo-Max, LLC, a division of ICON), with Intel Visual Fortran professional edition, version 6.6. The NONMEM companion interface software used was PDx-POP (version 2, Globo-Max, LLC, a division of ICON). Output files for the population analysis results were produced using SAS (version 8.2; SAS Institute, Japan) or Microsoft Office Excel Professional Edition 2003 (Microsoft Corporation). Figures were drawn using Origin (version 7.5; OriginLab Corporation). A two-compartment model with zero-order input (ADVAN3 and TRANS4) was used as a basic model. The basic PK parameters were total body clearance (CL, l/h), volume of distribution for the central compartment (Vc, l), intercompartmental clearance (Q, l/h), and volume of
833
distribution for the peripheral compartment (Vp, l). The first-order conditional estimation with interaction (FOCEINTER) method was adopted. An exponential model was selected to describe the interindividual variability, described for CL as an example: CLj ¼ TVCL exp gjCL where gjCL is a random variable that represents the difference between clearances of the jth individual (CLj) and the population mean value (TVCL). The gjCL is normally distributed with an expectation of zero and a variance of x2CL. A log-normal distribution was modeled for the intraindividual (residual) variability as follows: Cij ¼ Cpred;ij exp eij where Cij is the ith observed plasma concentration of meropenem for the jth individual, Cpred,ij is the concentration predicted by the population PK model, and eij is a randomly distributed variable with mean of zero and variance r2. The minimum value of the NONMEM objective function (OBJ) was used as a statistic for choosing suitable models during the model-building process. The difference in OBJ approximates a v2 distribution with degrees of freedom equal to the number of added or reduced parameters. First, a forward inclusion procedure was performed in NONMEM to build the full model, and then a backward deletion procedure was applied to the final model candidate. For the forward inclusion process, decrease in OBJ of at least 3.84 (P = 0.05, 1 degree of freedom) was used to incorporate a covariate into the model. For the backward deletion process, increase in OBJ of at least 6.63 (P = 0.01, 1 degree of freedom) was required to retain the covariate. Creatinine clearance (CLCR) was primarily added into the basic model because CLCR showed a significant correlation with CL among the candidates for covariates. Empirical Bayesian estimates of the parameters, such as CLj for each patient, were determined by means of the post hoc option of the NONMEM estimation step, using the population parameters in the updated model. The relations between other covariate candidates and post hoc estimates of meropenem PK parameters were investigated graphically at first. Model development was performed by the difference of OBJ described above and by visually checking the figures. To evaluate the validity of the population PK model developed, a nonparametric bootstrap technique was conducted. One thousand bootstrap resampled data sets were generated, each containing the same number of patients as the original data set, and each of them was fitted individually to the final population PK model. Geometric mean and 95% confidence intervals for parameter estimates
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successfully analyzed including the covariance step were compared to the mean parameter estimates obtained from the original data set. To check the suitability of the final model for PK simulation, a visual predictive check was performed. Based on the final model, 1,000 plasma concentration profiles at the dose of 1 g q8h by 0.5-h infusion were basically simulated without residual errors, and 95% prediction intervals at each time point were plotted with the observed data.
Table 2 MIC distributions of meropenem against pathogens isolated from the patients
Bacteroides fragilis group (71)
0.03 to [16
0.25
PK–PD simulations
P. aeruginosa (322)
B0.06 to [128
1
Strains (number of stocks)
MIC range (lg/ml)
MIC50 (lg/ml)
MIC90 (lg/ml)
E. coli (141)
B0.015–0.06
B0.015
0.03
MSSAa (58)
B0.06–0.12
0.12
0.12
BLNARb (112) Acinetobacter spp. (110)
0.03–2 0.12–32
0.25 0.25
0.5 1 2 16
Data were obtained from the Jpn J Antibiot [14]
Plasma concentrations of meropenem were simulated for 1,000 virtual subjects, 500 males and 500 females. The virtual subject dataset was generated by SAS. The dataset consisted of dosage regimen, sampling time (0.01 h of grid interval), and subject characteristics (CLCR and gender). The dosage regimens for simulations were 0.5 g q12h, 1 g q12h, 0.5 g q8h, 1 g q8h, 2 g q8h, or 0.5 g q6h. The infusion duration was basically 0.5 h. The CLCR of each subject was randomly generated from a log-normal distribution using arithmetic mean (4.44) and standard deviation (0.38) of logarithm of observed CLCR in the Phase 3 study. A Monte Carlo simulation for virtual plasma concentrations was conducted using $SIMULATION mode in NONMEM. Individual CL and Vc were generated from a log-normal distribution based on the population PK mean value as the mean and interindividual variability as the variance. Calculating %T [ MIC at MICs of 0.06–32 lg/ml for each plasma meropenem profile on the 4th day (steady state) in 1,000 simulated subjects, mean and 95% prediction intervals of %T[MIC were obtained for each MIC and each dose. Target attainment rates for bacteriostatic exposures (target, 30%T [ MIC), bactericidal exposures (target, 50%T [ MIC), or clinical response exposures (target, 75%T [ MIC) were also calculated. The previous study reported that 80% of clinical response rate was obtained in febrile neutropenic patients with more than 75%T [ MIC for meropenem [13]. Target attainment rate against each strain, such as Escherichia coli (E. coli), methicillin-susceptible Staphylococcus aureus (MSSA), b-lactamase-negative ampicillinresistant Haemophilus influenzae (BLNAR), Acinetobacter spp., Bacteroides fragilis group, and Pseudomonas aeruginosa (P. aeruginosa) were predicted, using the reported MIC distribution data of meropenem against clinically isolated strains shown in Table 2 [14]. E. coli and MSSA were selected as typical gram-negative and gram-positive organisms, respectively. P. aeruginosa was selected as a representative organism in association with severe infections leading to significant morbidity. To investigate the effect of infusion duration, target attainment was simulated for a longer infusion time of 1, 2, 3, or 4 h against pathogens at various MIC breakpoints.
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a
Methicillin-susceptible Staphylococcus aureus
b
b-Lactamase-negative ampicillin-resistant Haemophilus influenzae
Results Population PK analysis As no PK data were obtained for 2 patients, a total of 209 plasma meropenem concentrations from 98 patients (1–3 concentrations from each patient) were used for population PK analysis. The critical models during model development are summarized in Table 3. Interindividual variability was modeled for CL and Vc. An exponential error model was found suitable for the interindividual variability and the intraindividual (residual) variability. Among the candidates for covariates, CLCR showed a significant correlation with CL. The covariate model for CL was described according to a power function as follows: hCL CR CLCR CL ¼ TVCL CLCR;geomean where CLCR,geomean is the geometric mean (85 ml/min) of CLCR and hCLCR is the influence factor. TVCL reveals a typical value of clearance, which describes CL when CLCR was equal to the geometric mean of CLCR in the population. As there was no CLCR measurement for 1 patient, the CLCR,geomean was used instead of the individual CLCR for that case. hCLCR (0.648) was significantly different from zero, and thus CLCR was incorporated on CL into the model. After the incorporation of CLCR on CL into the model, influence of gender (GEN) was estimated. A categorical variable was modeled as follows: Vc ¼ TVVc hGEN in which TVVc is the population mean value of Vc of meropenem in male patients, and hGEN is assigned as 1 for males. As hGEN (0.793) was different from unity, there existed some gender difference in Vc. The goodness of fit for the model was improved by the incorporation of the
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Table 3 Major investigated models during population PK analysis Model
Equations
DOBJ from:
OBJ
Model 1 1 (basic) 2 3 (final) 4 (full) 5
CL = hCL, Vc = hVC , Q = hQ, Vp = hVP
Model 2
Model 3
650.581
–
–
–
CL = hCL 9 ðCLCR =85Þ
582.774
-67.807
–
–
CL = hCL 9 ðCLCR =85ÞhCLCR VC = hVC 9 hGEN
575.331
–
-7.443
–
9 hCL,GEN VC = hVC 9 hGEN
569.104
–
–
-6.227
9 hGEN VC = hVC 9 hGEN
575.365
–
–
0.034
hCLCR
hCLCR
CL = hCL 9 ðCLCR =85Þ
hCLCR
CL = hCL 9 ðCLCR =85Þ
CLCR, creatinine clearance (ml/min) calculated by Cockcroft and Gault equation, geometric mean of CLCR 85 ml/min; GEN, gender, male:female = 64:34
effect of gender on Vc. Body weight did not show any correlation with Vc. After the incorporation of gender on Vc, influence of gender on CL was estimated. By the foregoing forward selection step, the following full population PK model was suggested: CL ¼ hCL ðCLCR =85Þ
hCLCR
hCL;GEN expðgCL Þ;
Table 4 Final estimates of population PK parameters Parameter
hCL
CLðL=hÞ ¼ 11:2 ðCLCR =85Þ0:648 ; Vc ðLÞ ¼ 13:8 hGEN ; male; hGEN ¼ 1; female; hGEN ¼ 0:793; Vc for female is 10:7 L; QðL=hÞ ¼ 4:20; Vp ðLÞ ¼ 3:10: The parameter estimates in the final population PK model are presented in Table 4. The interindividual variability (x2) for CL was 0.0441 and coefficient of variance (CV, %) was 21.2%. The value of x2Vc was 0.0271
Geometric mean
95% CI
11.2
11.2
10.5–11.8
hCLCR
0.648
0.648
0.544–0.773
x2CL
0.0441
0.0392
0.0157–0.0685
VC (L) = hVC 9 hGEN 9 exp(gV1 )
Vp ¼ h V p : As the inclusion of BLOCK (2) into $OMEGA in the full model (model 4 in Table 3) provided a decrease in OBJ of 0.543, no significant covariance between interindividual variability to CL and that to Vc was found. A combined exponential and additive error model, such as ‘‘Cij = Cpred,ij 9 e1,ij ? e2,ij’’, instead of the exponential error model in the final model (model 3 in Table 3), resulted in a small estimate (0.000194) of additive error margin (e2,ij), and the decrease in OBJ was 0.153 (P = 0.696). During the backward deletion process to build the final model, it was found that the coefficient of gender effect on CL could be excluded from the full model without causing a significant increase of the OBJ (6.227, P [ 0.01), whereas all other coefficients should remain in the model. The final model obtained was as follows:
Bootstrap validationa
CL (L/h) = hCL 9 ðCLCR =85ÞhCLCR 9 exp(gCL)
Vc ¼ hVc hGEN exp gVc ; Q ¼ hQ ;
Final estimates
hVC
13.8
13.6
11.4–15.7
hGEN for female
0.793
0.795
hGEN for male
1
–
x2Vc
0.0271
0.004
4.20
3.91
1.15–11.6
3.10
3.04
1.61–5.42
0.0645
0.0623
Q (L/h) = hQ hQ
0.660–0.957 – 0–0.661
Vp (L) = hVP hVP Y = F9exp(e) r2
0.0415–0.0892
– not calculated, CI confidence intervals, Y observed meropenem concentration, F predicted meropenem concentration a
All 1,000 computations were completed successfully
with CV of 16.6%. Regarding residual error, the value of intraindividual variability (r2) was 0.0645 and CV was 25.8%. The final model adequately described the observed data except for five outliers (weighted residuals [ ±5) from three patients. Even after excluding all the data for these patients’ data (six time points), parameter estimates changed little, and the data for all patients were included in the calculations of the final parameters. The reliability and stability of the final population PK model were confirmed by a bootstrap technique. As shown in Table 4, the parameter estimates obtained based on the original data set were close to the geometric means of the parameter estimates and fell within 95% confidence
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(B)
100
Plasma concentration (µg/mL)
Plasma concentration (µg/mL)
(A) 10
1
0.1
0.01 0
2
4
6
8
100
10
1
0.1
0.01 0
10
2
(C)
6
8
10
8
10
(D)
100
Plasma concentration (µg/mL)
Plasma concentration (µg/mL)
4
Time after dosing (h)
Time after dosing (h)
10
1
0.1
0.01 0
2
4
6
8
100
10
1
0.1
0.01 0
10
2
Fig. 1 The observed plasma concentration of meropenem obtained from 98 Japanese adult patients and predicted lines at 1 g q8h. a CLCR C 70 ml/min: infusion duration was 0.5 h for simulated lines and less than 1 h for observed data. b CLCR C 70 ml/min: infusion duration was 1 h for simulated lines and 1 h or more for observed data. c 50 B CLCR \ 70 ml/min: infusion duration was 0.5 h for simulated lines and all actually applied time for observed data. d 30 B CLCR \ 50 ml/min: infusion duration was 0.5 h for simulated
4
6
Time after dosing (h)
Time after dosing (h)
lines and all actually applied time for observed data. The plots (open circle) represent observed values obtained from 98 patients. The broken lines show 2.5th and 97.5th percentile of the simulated plasma concentrations obtained from a Monte Carlo simulation of 1,000 virtual patients using parameter estimates from the final model. Bold lines, population mean for males, thin lines, population mean for females
intervals of these parameter estimates. The observed plasma concentrations and 95% prediction intervals of predicted plasma concentration profiles were indicated categorically by renal function groups (Fig. 1). The prediction intervals of model-predicted concentration including interindividual variability (IPRED) of virtual patients included most of the observed values evenly. The simulated concentrations for male patients were similar with those for female patients. To understand changes of Cmax, Tmax, and plasma concentration levels with prolonged infusion time, mean simulated plasma concentrations of patients with normal renal function at a dose of 1 g q8h by 0.5-, 2-, or 4-h infusion are shown in Fig. 2.
PK–PD simulations for various MICs One thousand virtual subjects were generated in silico for three subgroups according to renal function (CLCR): CLCR C 70 ml/min (normal renal function group),
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Plasma concentration (µg/mL)
100
10
1
0.1
0.01 0
2
4
6
8
Time after dosing (h)
Fig. 2 Simulated plasma concentration in febrile neutropenic patients with normal renal function (CLCR C 70 ml/min) at 1 g q8h (males). Solid line, predicted curve for 0.5-h infusion; broken line, predicted curve for 2-h infusion; dotted line, predicted curve for 4-h infusion
70 [ CLCR C 50 ml/min (reduced renal function group), and 50 [ CLCR C 30 ml/min (moderate renal impairment group). Meropenem dosage was identical for all subjects,
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Table 5 Effects of renal function on %T [ MIC values of meropenem for various MICs (dose: 1 g q8h by 0.5 h infusion) MIC of bacteria (lg/ml)
%T [ MIC (%), mean (95% prediction intervalsa) 70 ml/min B CLCR
50 B CLCR \ 70 ml/min
30 B CLCR \ 50 ml/min
32
10.2 (4.9–16.9)
14.3 (8.2–23.2)
18.6 (10.2–31.9)
16
21.5 (12.3–35.8)
30.3 (18.4–48.7)
38.9 (22.4–62.5)
8
33.7 (18.1–56.3)
47.3 (28.6–76.6)
59.7 (34.2–95.0)
4
46.5 (24.5–75.9)
64.2 (39.6–100)
78.3 (47.3–100)
2
59.4 (32.1–96.5)
79.2 (50.9–100)
90.7 (60.1–100)
1
71.4 (40.5–100)
90.1 (62.2–100)
96.8 (72.7–100)
0.5 0.25
81.5 (49.1–100) 89.1 (57.9–100)
96.1 (73.4–100) 98.7 (84.7–100)
99.1 (85.4–100) 99.8 (98.1–100)
0.12
94.4 (67.0–100)
99.7 (96.5–100)
100 (100–100)
0.06
97.5 (75.7–100)
99.9 (100–100)
100 (100–100)
One thousand virtual patients in each group were simulated by the population PK model a
95% prediction intervals were calculated by percentile method
Table 6 Prediction of %T [ MIC of meropenem for various MICs and various dosing regimens MIC of bacteria (lg/ml)
%T [ MIC (%), mean (95% prediction intervalsa) 0.5 g q12h
0.5 g q8h
0.5 g q6h
1 g q12h
1 g q8h
2 g q8h
32
0.3 (0–2.3)
0.5 (0.0–3.4)
0.8 (0.0–5.0)
6.7 (3.3–10.8)
10.2 (4.9–16.9)
21.5 (12.3–35.8)
16
6.7 (3.3–10.8)
10.2 (4.9–16.9)
14.0 (6.8–23.7)
14.3 (8.2–23.3)
21.5 (12.3–35.8)
33.7 (18.1–56.3)
8
14.3 (8.2–23.3)
21.5 (12.3–35.8)
29.2 (16.4–49.6)
22.4 (12.0–36.9)
33.7 (18.1–56.3)
46.5 (24.5–75.9)
4
22.4 (12.0–36.9)
33.7 (18.1–56.3)
45.5 (24.1–77.0)
30.9 (16.3–50.1)
46.5 (24.5–75.9)
59.4 (32.1–96.4)
2
30.9 (16.3–50.1)
46.5 (24.5–75.9)
62.2 (32.7–100)
39.5 (21.4–63.6)
59.4 (32.1–96.5)
71.4 (40.5–100)
1
39.5 (21.4–63.6)
59.4 (32.1–96.4)
77.0 (42.8–100)
48.3 (27.0–77.5)
71.4 (40.5–100)
81.5 (49.1–100)
0.5 0.25
48.3 (27.0–77.5) 57.0 (32.7–91.1)
71.4 (40.5–100) 81.5 (49.1–100)
87.8 (54.0–100) 94.5 (65.6–100)
57.0 (32.7–91.1) 65.4 (38.6–100)
81.5 (49.1–100) 89.1 (57.9–100)
89.1 (57.9–100) 94.2 (66.5–100)
0.12
65.9 (38.9–100)
89.4 (58.4–100)
98.1 (77.8–100)
73.7 (44.7–100)
94.4 (67.0–100)
97.5 (75.7–100)
0.06
73.7 (44.7–100)
94.4 (67.0–100)
99.5 (89.4–100)
80.5 (50.5–100)
97.5 (75.7–100)
99.0 (84.5–100)
One thousand virtual adult patients with normal renal function (CLCR C 70 ml/min) were administered meropenem by 0.5 h infusion at each regimen a
95% prediction intervals were calculated by percentile method
that is, 1 g q8h by 0.5-h infusion (Table 5). For patients with normal renal function (CLCR C 70 ml/min), estimated %T [ MIC are compared for various MICs and various dosing regimens (Table 6). Target attainment rates for bacteriostatic exposure (target, 30%T [ MIC), bactericidal exposure (target, 50%T [ MIC), and clinical response exposures (target, 75%T [ MIC) at each MIC were also calculated (Fig. 3). After dosing 0.5 g q8h by 0.5-h infusion, a high target attainment rate ([80%) at 30%T [ MIC was achieved with MIC of 2 lg/ml or less (Fig. 3a), that at 50%T [ MIC was achieved with MIC of 0.5 lg/ml or less (Fig. 3b), and that at 75%T [ MIC was achieved with MIC of 0.12 lg/ml or less (Fig. 3c). With dose escalation from 0.5 g q8h to 1 g q8h by 0.5-h infusion, target attainment rates for achieving 30%T [ MIC with MIC of 4 lg/ml, that at 50%T [ MIC
with MIC of 1 lg/ml, and that at 75%T [ MIC with MIC of 0.25 lg/ml were increased from 60.0% to 91.7%, from 68.4% to 89.9%, and from 63.8% to 82.7%, respectively. PK–PD simulations for various pathogens Target attainment rates for meropenem against various pathogens are shown in Fig. 4. E. coli, MSSA, and BLNAR were classified into one group of highly sensitive pathogens (MIC90 B0.5 lg/ml). Target attainment rates at 50%T [ MIC against E. coli, MSSA, and BLNAR were more than 92.3% at the dose of 0.5 g q8h by 0.5-h infusion. Target attainment rates at 50%T [ MIC against Acinetobacter spp., Bacteroides fragilis group, and P. aeruginosa were 88.5%, 84.8%, and 60.1% at the dose of 0.5 g q8h by 0.5-h infusion, respectively (Fig. 4b). With dose escalation from
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Target attainment rate (%)
(A) 30%T>MIC
(B) 50%T>MIC
(C) 75%T>MIC
100
0.5 g
80
q12h q8h q6h
60
1g
40
q12h q8h
20
2g q8h
0
.06 .12 .25
.5
1
2
4
8
16
32
.06 .12 .25
.5
MIC (µg/mL)
1
2
Fig. 3 Probabilities of achieving bacteriostatic (target, 30%T [ MIC), bactericidal (target, 50%T [ MIC), and clinical response (target, 75%T [ MIC) exposure at various MICs for meropenem by various dosing schedules: a 30%T[MIC; b 50%T [ MIC; c 75%T [
8
16
.06 .12 .25
32
.5
1
2
Target attainment rate (%)
60
E. Coli 0.5 g q8h MSSA
40
0.5 g q8h BLNAR 0.5 g q8h
20
8
16
32
0.5 g q8h 0.5 h infusion 1 g q8h 0.5 h infusion 1h 2h 3h 4h 2 g q8h 0.5 h infusion
100
100
80
4
MIC (µg/mL)
MIC. One thousand virtual patients with normal renal function (CLCR C 70 ml/min) were simulated at each regimen. MIC, minimum inhibitory concentration
(A) Target attainment rate (%)
4
MIC (µg/mL)
80
60
40
20
0 0
10
20
30
40
50
60
70
80
90 100
%T>MIC (%) 0 0
10
20
30
40
50
60
70
80
90
100
%T>MIC (%)
(B)
Target attainment rate (%)
100
0.5 g q8h to 1 g q8h and 2 g q8h by 0.5-h infusion, target attainment rates at 50%T [ MIC increased from 60.1% to 71.8% and 80.6%, respectively.
80
Acinetovacter spp.
60
0.5 g q8h 1 g q8h
Dosage regimen optimization
Bacteroides fragile group
40
0.5 g q8h 1 g q8h
P. aeruginosa 20
0.5 g q8h 1 g q8h 2 g q8h
0 0
10
20
30
40
50
60
70
80
90
100
%T>MIC (%)
Fig. 4 Target attainment rates for meropenem against various pathogens by 0.5-h infusion method: Escherichia coli (E. coli), methicillinsusceptible Staphylococcus aureus (MSSA), and b-lactamase-negative ampicillin-resistant Haemophilus influenzae (BLNAR) (a); Acinetobacter spp., Bacteroides fragilis group, and Pseudomonas aeruginosa (P. aeruginosa) (b). One thousand virtual patients with normal renal function (CLCR C 70 ml/min) were simulated at each regimen
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Fig. 5 Target attainment rates for meropenem against P. aeruginosa in patients with various dosing schedules of meropenem. One thousand virtual patients with normal renal function (CLCR C 70 ml/min) were simulated at each regimen
Target attainment rates at 30%T [ MIC against P. aeruginosa were 77.5%, 86.6%, 91.8%, 93.4%, and 93.3% in patients with normal renal function (CLCR C70 ml/min) at 0.5 g q8h by 0.5-h infusion, 1 g q8h by 0.5-, 2-, or 3-h infusion, and 2 g q8h by 0.5-h infusion, respectively (Fig. 5). When the infusion time was prolonged from 0.5 to 3 h at 1 g q8h in the patients with normal renal function, target attainment rate at 50%T [ MIC increased from 71.8% to 83.8%. Target attainment rate at 2 g q8h by 0.5-h infusion was comparable to that after dosing of 1 g q8h by 2-h infusion in patients with normal renal function.
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839
Rates of defervescence, and clinical and bacteriological efficacy
[3, 8, 13, 21]. Population PK was investigated in Caucasian patients in two of the studies [3, 13] and in Asian patients in the other studies [8, 12]. In the present study, gender was found to have a statistically significant effect on Vc, but this effect was small, and the PK profile of meropenem in males was virtually similar to that in females (see Fig. 1). In contrast to previous reports [8, 12, 13], body weight in patients was not related to Vc either before or after incorporating the correlation of gender with Vc into the covariate model. Body weight in males (59.9 ± 11.8 kg) was similar to that in females (52.3 ± 8.03 kg), and there was no significant difference [P = 0.472 by analysis of variance (ANOVA)] in our population. Body weight includes both lean body mass (LBM) and body fat. Body weight and LBM in males are generally greater than in females, as the ratio of body fat to body weight in males is lower than that in females. The logarithm of the partition coefficient (1 - octanol/ water) in meropenem is reported as -3.28 [22], and therefore meropenem seems to be distributed to watersoluble compartments. Considering the physicochemical characteristics of meropenem, LBM in patient might be related to Vc and body weight may have an indirect relationship to Vc. In this study population, age of patients was distributed relatively higher (mean age, 59.3 years) than in other reports (mean age, 36 and 39.6 years) [3, 8]. As elderly people have more variable LBM/body weight ratio, the correlation between body weight and Vc might be masked; this may be the reason why a correlation between body weight and Vc was not observed, but instead a gender effect on Vc was observed, in this analysis. Mean of %T [ MIC at MIC of 2 lg/ml in patients with normal renal function (CLCR C70 ml/min), reduced renal function (70 [ CLCR C 50 ml/min), or moderate renal impairment (50 [ CLCR C 30 ml/min) was 59.4%, 79.2%, or 90.7%, respectively (see Table 5). The reason why patients with reduced renal function reached greater
The primary endpoint was achievement of defervescence in the present Phase 3 study. Defervescence was achieved in 40 of 100 patients (40.0%) within 4 days after the start of the treatment. Two of 9 for whom causative organisms were identified became afebrile (22.2%). Symptom improvement was observed in 47.3% of the patients (44/93 patients) after exclusion of 7 patients from the clinical evaluation. For bacteriological evaluation, a total of six bacterial strains were isolated from nine patients (Table 7). The MIC of meropenem against each isolate was 1 lg/ml or less, except for one Staphylococcus epidermidis strain with MIC of 16 lg/ml. Eight strains were determined to be eradicated by repeated culture. One strain could not be evaluated because of lack of any repeated culture. The overall eradication rate was 89% (8/9) (Table 7). The %T [ MIC values for the strains isolated from eight patients were greater than 90%, and the remaining one strain (Staphylococcus epidermidis) showed 30.9% (Table 7).
Discussion Meropenem is excreted mainly via a renal route. Approximately 60% of unchanged meropenem was excreted in the urine through the kidneys [19]. Therefore, clearance of meropenem is affected by renal function in patients [20, 21]. In this population PK model of meropenem for Japanese patients with febrile neutropenia, CL was found to be associated with CLCR but was not affected by other clinical laboratory tests as indicators of other organ functions. This result was consistent with the previous results of population PK studies of meropenem in adults, in which CLCR was reported to be the most important determinant of clearance
Table 7 Relationships between PK–PD parameters and efficacies Subject
Infusion (h)
Isolates
MIC (lg/ml)
%T [ MIC (%)
Defervescencea
Bacteriological efficacy
101
0.71
Escherichia coli
\0.06
100
No
Eradicated
201
1.12
a-Hemolytic Streptococcus
\0.06
100
No
Eradicated
803
1.07
Staphylococcus epidermidis
Yes
Eradicated
817
1
Aerobic gram-positive rod
0.25
100
No
Eradicated
1002
1.42
Staphylococcus epidermidis
1
100
No
Eradicated
1005
0.96
E. coli
\0.06
100
Yes
Eradicated
1009
1.33
Staphylococcus epidermidis
\0.06
1101
0.5
Streptococcus oralis
\0.06
1103
0.5
Coagulase-negative staphylococcus
\0.06
a
16
30.9
95.9
No
Unknown
100
No
Eradicated
100
No
Eradicated
Primary endpoint
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%T [ MIC than patients with normal renal function is that the plasma level of meropenem slowly decreased in patients with impaired renal function. In other words, patients with renal impairment were able to get higher exposure to meropenem than patients with normal renal function. The Japanese package insert of meropenem (Meropen) in 2010 indicated that meropenem must be administered in a reduced dosage or prolonged dosing interval to patients with severely impaired renal function, such as CLCR B30 ml/min, and treatment schedule and dose should be adequately adjusted depending on the degree of renal function. The results of the present quantitative simulations show practical guidance for dosing regimen of meropenem in patients with reduced renal function. Within the same total daily dose, 0.5 g q6h (total, 2 g/day) by 0.5-h infusion achieved higher %T [ MIC at MIC of 8 lg/ml than 1 g q12h (total, 2 g/day) by 0.5-h infusion (see Table 6). The present results indicated that 0.5 g q6h can be a therapeutic option as more effective dosage than 1 g q12h within a total dose of 2 g/day. According to a Japanese nationwide surveillance study in 2006 [14], the MIC90 of meropenem for common pathogenic bacteria, such as E. coli, MSSA, and BLNAR, was below or equal to 0.5 lg/ml. The intermediately sensitive pathogens were Acinetobacter spp. (MIC90 1 lg/ml, 110 strains) and Bacteroides fragilis group (MIC90 2 lg/ml, 71 strains). P. aeruginosa (MIC90 16 lg/ml, 322 strains) was a comparatively low sensitivity pathogen. Because only a small number of pathogens were microbiologically documented from febrile neutropenic patients, the previous surveillance results were used for the PK–PD simulations. Most of the pathogens in febrile neutropenic patients reported by Ariano et al. [13] and observed in this study (see Table 7) were comparable with the surveillance results. Target attainment rates for achieving 50%T [ MIC against E. coli, MSSA, and BLNAR were more than 95.2% at 0.5 g q8h by 0.5-h infusion (see Fig. 4a). The PK–PD simulations in this study indicated that the optimal dosage of meropenem was 0.5 g q8h by 0.5-h infusion against highly susceptible pathogens, such as E. coli, MSSA, and BLNAR. Target attainment rates against Acinetobacter spp. and Bacteroides fragilis group were 88.5% and 84.8% at 0.5 g q8h by 0.5-h infusion, respectively (see Fig. 4b). PK–PD simulations in this study revealed that 0.5 g q8h by 0.5-h infusion is useful against intermediately sensitive pathogens, such as Acinetobacter spp. and Bacteroides fragilis group. On the other hand, target attainment rate against P. aeruginosa was only 60.1% at 0.5 g q8h by 0.5-h infusion (Fig. 4b). At 1 g q8h by 0.5-h infusion, the probability of target attainment for bactericidal exposure (target: 50%T [ MIC) achieved 71.8% against P. aeruginosa. Higher dosage (1 g q8h) along with prolonged infusion time (4 h) resulted in a high attainment rate
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(88.4%). Prolonged infusion time from 0.5 to 4 h at 1 g q8h increased %T [ MIC at every MIC; e.g., patients with normal renal function at MIC of 4 lg/ml, from 46.5% (see Table 6) to 72.4%. The optimal dosage of meropenem was 1 g q8h or by longer infusion against comparatively low sensitivity pathogens, such as some strains of P. aeruginosa (especially in the case of MIC C2 lg/ml). Many studies already reported that prolonged infusion time is an alternative method to optimize meropenem pharmacodynamics [1–3, 7, 10]. The overall eradication rate was 89% (eight of nine patients; Table 7). According to the microbiological tests, MIC values of eight strains in nine patients were B1 lg/ml, except for one strain (MIC = 16 lg/ml). The probability of achieving 75%T [ MIC in these nine patients was also 89%. Although there were only a small number of patients for whom the causative organisms were identified, the target PK–PD attainment was comparable to the microbiological response. In febrile neutropenic patients, an 80% clinical response rate was evident when the %T [ MIC for meropenem exceeded 75% of the dosing interval [13]. After dosing of 0.5 g q8h by 0.5-h infusion to patients with normal renal function, a high target attainment rate ([80%) at 75%T [ MIC was achieved at MIC of 0.12 lg/ml or less (see Fig. 3c). By dose escalation, target attainment rates for achieving 75%T [ MIC with MIC of 0.25 lg/ml were increased from 63.8% (0.5 g q8h) to 82.7% (1 g q8h). After dosing of 1 g q8h by 2-h infusion, a high target attainment rate ([80%) at 75%T [ MIC was achieved at MIC of 0.5 lg/ml or less (data not shown). Because some febrile neutropenic patients may suffer from bacteria with relatively high MICs, it is tempting to speculate that larger dose amount and/or longer infusion time would be necessary for febrile neutropenic patients. Careful attention should be paid when the causative pathogen is unknown and the site of infection is suspected to be apart from blood, because the present PK–PD simulations were based on plasma concentrations of meropenem. It was reported that the concentration–time profile in cerebrospinal fluid (CSF) is distinct from that in plasma; Cmax in CSF is lower and t1/2 is longer than those in plasma [23]. An additional possibility is that undetectable pathogens may have high MIC value. Considering the foregoing, a higher dose may be required for febrile neutropenic patients when the causative pathogen is not identified. Hathorn and Lyle [24] reported in a review article that the ratio of febrile neutropenic patients microbiologically infected was between 30% and 50%. In the current study, defervescence was achieved in 40 patients among 100 patients (40%) within 4 days after starting the treatment with dose of 1 g q8h, suggesting comparable ratios of antibiotic treatment to effective patient population.
J Infect Chemother (2011) 17:831–841
Because many febrile episodes in neutropenic patients can be treated successfully with early initiation of empirical antimicrobial therapy, PK–PD simulations in this study using %T [ MIC as a PD index for meropenem could suggest useful information for meropenem dosing when bacterial infection is suspected as the disease cause. In conclusion, this article reports that microbiological efficacy of meropenem correlated with PK–PD parameters based on a developed population PK model in febrile neutropenic patients from a Phase 3 study conducted in accordance with GCP standards in Japan. Additionally, the effect of meropenem against various bacteria was predicted with various dosing regimens and infusion durations. The population PK parameters in this study were useful in simulating PK of meropenem at various dosage regimens to calculate %T [ MIC. The present PK–PD analyses under various conditions such as dosage methods and MICs of meropenem against causative pathogens will be useful in the treatment of febrile neutropenia.
841
10.
11.
12.
13.
14.
15.
16.
References 1. Kuti JL, Dandekar PK, Nightingale CH, Nicolau DP. Use of Monte Carlo simulation to design an optimized pharmacodynamic dosing strategy for meropenem. J Clin Pharmacol. 2003;43:1116–23. 2. Mattoes HM, Kuti JL, Drusano GL, Nicolau DP. Optimizing antimicrobial pharmacodynamics: dosage strategies for meropenem. Clin Ther. 2004;26:1187–98. 3. Li C, Kuti JL, Nightingale CH, Nicolau DP. Population pharmacokinetic analysis and dosing regimen optimization of meropenem in adult patients. J Clin Pharmacol. 2006;46:1171–8. 4. Kuti JL, Ong C, Lo M, Melnick D, Soto N, Nicolau DP. Comparison of probability of target attainment calculated by Monte Carlo simulation with meropenem clinical and microbiological response for the treatment of complicated skin and skin structure infections. Int J Antibicrobial Agents. 2006;28:62–8. 5. Filho LS, Eagye KJ, Kuti JL, Nicolau DP. Addressing resistance evolution in Pseudomonas aeruginosa using pharmacodynamic modeling: application to meropenem dosage and combination therapy. Clin Microbiol Infect. 2007;13:579–85. 6. Watanabe A, Fujimura S, Kikuchi T, Gomi K, Fuse K, Nukiwa T. Evaluation of dosing designs of carbapenems for severe respiratory infection using Monte Carlo simulation. J Infect Chemother. 2007;13:332–40. 7. Mikamo H, Totsuka K. The comparison of optimized pharmacodynamic dosing strategy for meropenem using Monte Carlo simulation. Jpn J Antibiot. 2005;58:159–67. 8. Lee DG, Choi SM, Shin WS, Lah HO, Yim DS. Population pharmacokinetics of meropenem in febrile neutropenic patients in Korea. Int J Antimicrob Agents. 2006;28:333–9. 9. Wang H, Zhang B, Ni Y, Kuti JL, Chen B, Chen M, et al. Pharmacodynamic target attainment of seven antimicrobials
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against gram-negative bacteria collected from China in 2003–2004. Int J Antimicrob Agents. 2007;30:452–7. Ohata Y, Tomita Y, Nakayama M, Kozuki T, Sunakawa S, Tanigawara Y. Optimal dosage regimen of meropenem for pediatric patients based on pharmacokinetic/pharmacodynamic consideration. Drug Metab Pharmacokinet (in press). Li C, Du X, Kuti JL, Nicolau DP. Clinical pharmacodynamics of meropenem in patients with lower respiratory tract infections. Antimicrob Agents Chemother. 2007;51:1725–30. Ikawa K, Morikawa N, Ohgi H, Ikeda K, Sueda T, Taniwaki M, et al. Pharmacokinetic-pharmacodynamic target attainment analysis of meropenem in Japanese adult patients. J Infect Chemother. 2010;16:25–32. Ariano RE, Nyhle´n A, Donnelly JP, Sitar DS, Harding GKM, Zelenitsky SA. Pharmacokinetics and pharmacodynamics of meropenem in febrile neutropenic patients with bacteremia. Ann Pharmacother. 2005;39:32–8. Yamaguchi K, Ishii Y, Iwata M, Watanabe N, Uehara N, Yasujima M, et al. Nationwide surveillance of parenteral antibiotics containing meropenem activities against clinically isolated strains in 2006. Jpn J Antibiot. 2007;60:344–77. Clinical phase 3 study of meropenem. Pharmaceuticals and medical devices agency, Japan [CTD, common technical document] 2010. http://www.info.pmda.go.jp/shinyaku/P201000014/ index.html. Clinical and Laboratory Standards Institute. Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically. In: Approved standard M7-A7. CLSI, Wayne, PA, 2006. Guidance for Industry: Population Pharmacokinetics. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER) (U.S.) (February 1999). Guideline on Reporting the Results of Population Pharmacokinetic Analyses. European Medicines Agency (UK) (January 2008). Nakajima M, Uematsu T, Kanamaru M. Clinical phase I study of meropenem. Chemotherapy 1992;40(S-I):258–275. Christensson BA, Nilsson-Ehle I, Hutchison M, Haworth SJ, Oqvist B, Norrby SR. Pharmacokinetics of meropenem in subjects with various degrees of renal impairment. Antimicrob Agents Chemother. 1992;36:1532–7. Chimata M, Nagase M, Suzuki Y, Shimomura M, Kakuta S. Pharmacokinetics of meropenem in patients with carious degrees of renal function, including patients with end-stage renal disease. Antimicrob Agents Chemother. 1993;37:229–33. Shibayama T, Sugiyama D, Kamiyama E, Tokui T, Hirota T, Ikeda T. Characterization of CS-023 (R04908463), a novel parenteral carbapenem antibiotic, and meropenem as substrates of human renal transporters. Drug Metab Pharmacokinet. 2007;22(1):41–7. Tsumura R, Ikawa K, Morikawa M, Ikeda K, Shibukawa M, Iida K, et al. The pharmacokinetics and pharmacodynamics of meropenem in the cerebrospinal fluid of neurosurgical patients. J Chemother. 2008;20(5):615–21. Hathorn WJ, Lyke K. Empirical treatment of febrile neutropenia: Evolution of current therapeutic approaches. Clin Infect Dis 1997;24(suppl 2):s256–s265.
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ORIGINAL ARTICLE
Optimal treatment schedule of meropenem for adult patients with febrile neutropenia based on pharmacokinetic–pharmacodynamic analysis Yuka Ohata • Yoshiko Tomita • Mitsunobu Nakayama Kazuo Tamura • Yusuke Tanigawara
•
Received: 2 April 2011 / Accepted: 7 June 2011 / Published online: 20 July 2011 Ó Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases 2011
Abstract The objectives of this study were to develop a population pharmacokinetic (PK) model of meropenem, to simulate the percent time above minimum inhibitory concentration (%T [ MIC) at various MICs, and to estimate effective dosage regimens by calculating the target attainment rates against various strains of bacteria. A total of 209 plasma samples (1–3 concentrations per patient) were obtained from 98 adult Japanese patients with febrile neutropenia in an open-labeled Phase 3 study. The final population PK model was fit to a two-compartment model with zero-order input. Creatinine clearance had a positive significant correlation with CL. Gender had a significant effect on Vc; however, this effect was small, and the PK profile in male patients was similar to that in female patients. The population PK parameters developed in this study are useful in simulating PK profiles of meropenem at various dosage regimens precisely for calculation of %T [ MIC. The PK–PD analysis indicated that 0.5 g every 6 h (q6h) was more effective than 1 g q12h, although provided 2 g per day in total. A meropenem dosage regimen of 1 g q8h and/or longer infusion duration was better against a pathogen of comparatively low sensitivity, Pseudomonas Y. Ohata Y. Tomita M. Nakayama Drug Development Division, Dainippon Sumitomo Pharma Co., Ltd, Osaka, Japan K. Tamura Division of Medical Oncology, Hematology and Infectious Diseases, Department of Medicine, Fukuoka University, Fukuoka, Japan Y. Tanigawara (&) Department of Clinical Pharmacokinetics and Pharmacodynamics, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan e-mail: [email protected]
aeruginosa (for MIC C2 lg/ml). Although causative bacteria were identified in a small number of patients, the target attainment rates at 75%T [ MIC (89%) were comparable to microbiological response (89%). The present PK–PD analyses under various conditions are useful in the treatment of febrile neutropenia. Keywords Meropenem %T [ MIC Pharmacokinetics Pharmacodynamics Febrile neutropenia Monte Carlo simulation
Introduction Meropenem is a widely used carbapenem antibiotic with high activity against most gram-positive and gram-negative bacteria. It is also active against clinically relevant aerobic, nutritionally fastidious, and anaerobic bacterial species. Therefore, meropenem is commonly used in empiric therapies for infectious diseases. A number of pharmacokinetic (PK)–pharmacodynamic (PD) simulations (so-called Monte Carlo simulations) for meropenem have been reported, and such simulations are considered to be an important approach for the prediction of antibacterial efficacy of meropenem [1–10]. For PK simulation, some reports used a simple compartment model that did not describe covariates for PK variability or individual variability [1, 2, 6, 7, 9]. For an important PD index for meropenem, all the previous studies considered the percent time above minimum inhibitory concentration (%T [ MIC). Levels of 20–30%T [ MIC were proposed to achieve bacteriostatic effects and 40–50%T [ MIC was proposed to achieve bactericidal outcomes [1–10]. Some reports estimated target attainment rates of bacteriostatic exposures (target, 30%T [ MIC) and/or bactericidal
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exposures (target, 50%T [ MIC) after various dosage regimens against bacteria for various MICs [1, 7, 10]. Other studies estimated target attainment rates against various strains of bacteria based on extrapolated MIC distribution data from surveillance studies [2, 7, 11]. Additionally, clinical effectiveness was also discussed with regard to PK–PD target attainment [11]. Most of the PK data for calculation of PK–PD target attainment were obtained from healthy subjects [1, 2, 6, 7, 9] or from patients with a mix of various infections [3–5, 11, 12], such as intraabdominal infections, community-acquired pneumonia, or ventilator-associated pneumonia [3]. Using data only from patients with febrile neutropenia, Lee et al. [8] and Ariano et al. [13] conducted PK–PD analysis. However, they did not predict PK–PD target attainment for various MICs against various other bacteria based on their model. The present study was primarily aimed to develop a population PK model of meropenem in Japanese adult patients with febrile neutropenia. The relationships between %T [ MIC and clinical response or bacteriological efficacy were investigated, and %T [ MIC attainment of meropenem for various dosage regimens were simulated to consider more effective dosage strategy. The MIC distribution data of clinical isolates was obtained from a Japanese surveillance study conducted in 2006 [14]. The optimal dosage regimens were proposed based on PK–PD simulations for bacteriostatic and bactericidal effects against various bacteria with various MICs.
Patients and methods Patient data
J Infect Chemother (2011) 17:831–841 Table 1 Patients’ characteristics Characteristics
Mean ± SD (or count) (n = 98)
Gender (male:female)
64:34
Age (years)
59.3 ± 12.9
Body weight (kg) Serum creatinine (mg/dl)
57.2 ± 11.2a 0.730 ± 0.225
Creatinine clearanceb (ml/min)
91.7 ± 40.7a
a
Range
18.0–78.0 39.0–114a 0.360–1.33 30.7–327a
n = 97
b
Creatinine clearance was calculated using the Cockcroft and Gault equation
informed consent was obtained from all the patients themselves or their legal representatives. The purpose of the Phase 3 study was to evaluate the efficacy, safety, and PK profile of meropenem in the treatment of patients with febrile neutropenia. The primary endpoint was to achieve each defervescence within 4 days from the beginning of the treatment. When patients obtained a normal temperature defined as a maximum \37.5°C and body temperature decreased by more than 0.5°C, the result was judged as effective. The secondary endpoints were clinical response, microbiological efficacy, and defervescence within 7 days. Clinical efficacy was evaluated by improvement of symptoms including fever, microbiological efficacy, and the results of laboratory tests. It was categorized into three groups: success, failure, or unknown. The microbiological response during the treatment was also classified into three groups: eradicated, failure, or unknown.
Patient data were obtained from an open-labeled Phase 3 clinical study conducted in Japan from July 2006 through February 2008 [15]. Study subjects were patients with febrile neutropenia defined as C38.0°C of fever, or not lower than 37.5°C of fever persisting for one or more hours, with no identified pathogenic bacteria. The number of neutrophils in those patients was\500/mm3, or\1000/mm3 which was predicted to decrease to \500/mm3. The age of the actual subjects ranged from 18 to 78 years (mean ± SD, 59.3 ± 12.9 years), and their weight (±SD) was 57.2 ± 11.2 kg. The demographic background is summarized in Table 1.
Meropenem dosage
Study protocol
The plasma concentrations (209 data points) of meropenem in 98 adult patients were determined using high-performance liquid chromatography (HPLC). All plasma samples were analyzed at Mitsubishi Chemical BCL (Tokyo, Japan). Each plasma sample was mixed with an equal volume of 1 M 3-morpholinopropanesulfonic acid (MOPS) buffer solution as a stabilizer, transferred to an ultrafiltration
The open-labeled Phase 3 clinical study was conducted in Japan in accordance with the Declaration of Helsinki and domestic regulations on clinical trials (Japanese Good Clinical Practice guidance). The study was approved by the ethical committee in each participating hospital. Written
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Meropenem was administered intravenously at a daily dose of 1 g every 8 h (q8h), administered by infusion for 0.5 h or longer for 7–14 days. Plasma samples were collected for meropenem assay at 15–25 min after the start of infusion or at 15 min to 8 h after the end of infusion. One to three samples of blood per patient were drawn in heparinized tubes. After centrifugation, plasma was frozen and stored until assay. Assay of meropenem concentrations in plasma
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device, and centrifuged at 12,000 rpm (approximately 8,000 g) for 5 min. Filtrate was injected into the HPLC system for analysis and chromatographically separated on a reversed-phase column (Hypersil C18, 4.6 mm U 9 10 cm, 3 lm). The mobile phase consisted of PIC A (tetrabutyl ammonium phosphate) solution and methanol (5/1, v/v). The flow rate was 1.0 ml/min. UV detector wavelength was 300 nm. The absolute calibration method was adopted for determination of meropenem. Meropenem in the plasma was quantified using the peak area of standard samples for calibration. This analytical method was validated for selectivity (no peak interfering with peak of meropenem, n = 6), linearity (r = 0.999), accuracy (intraday assay: -4.4% to 3.4%, n = 5; interday assay: -5.7% to 0.9%, n = 5), precision (intraday assay: -2.9% to 4.5%, n = 5; interday assay: 3.6% to 7.4%, n = 5), recovery (97.2% to 103.6% at 0.05, 10, or 200 lg/ml), calibration curve (0.05–200 lg/ml), and stability (stored first at -20°C for 96 h, and then, at -80°C for 60 days). Bacteriological tests Venous blood was drawn from the patients for culture and sensitivity tests by a sterile technique. If necessary, sputum, feces, or urine of the patients was also examined. The samples were collected before the drug treatment, at 4 and 7 days after the start of treatment, and at the end of the study. Isolation and identification of microorganisms followed standard procedures. The MICs of meropenem against the isolated strains were measured according to the Clinical Laboratory Standards Institute guidelines [16]. Population PK model development Model building and model validation were conducted in accordance with FDA and European Medicines Agency (EMEA) guidelines [17, 18] related to population PK analysis. The population PK analysis was performed using a nonlinear mixed effect model program, NONMEM, double precision, version V, level 1.1 (Globo-Max, LLC, a division of ICON), with Intel Visual Fortran professional edition, version 6.6. The NONMEM companion interface software used was PDx-POP (version 2, Globo-Max, LLC, a division of ICON). Output files for the population analysis results were produced using SAS (version 8.2; SAS Institute, Japan) or Microsoft Office Excel Professional Edition 2003 (Microsoft Corporation). Figures were drawn using Origin (version 7.5; OriginLab Corporation). A two-compartment model with zero-order input (ADVAN3 and TRANS4) was used as a basic model. The basic PK parameters were total body clearance (CL, l/h), volume of distribution for the central compartment (Vc, l), intercompartmental clearance (Q, l/h), and volume of
833
distribution for the peripheral compartment (Vp, l). The first-order conditional estimation with interaction (FOCEINTER) method was adopted. An exponential model was selected to describe the interindividual variability, described for CL as an example: CLj ¼ TVCL exp gjCL where gjCL is a random variable that represents the difference between clearances of the jth individual (CLj) and the population mean value (TVCL). The gjCL is normally distributed with an expectation of zero and a variance of x2CL. A log-normal distribution was modeled for the intraindividual (residual) variability as follows: Cij ¼ Cpred;ij exp eij where Cij is the ith observed plasma concentration of meropenem for the jth individual, Cpred,ij is the concentration predicted by the population PK model, and eij is a randomly distributed variable with mean of zero and variance r2. The minimum value of the NONMEM objective function (OBJ) was used as a statistic for choosing suitable models during the model-building process. The difference in OBJ approximates a v2 distribution with degrees of freedom equal to the number of added or reduced parameters. First, a forward inclusion procedure was performed in NONMEM to build the full model, and then a backward deletion procedure was applied to the final model candidate. For the forward inclusion process, decrease in OBJ of at least 3.84 (P = 0.05, 1 degree of freedom) was used to incorporate a covariate into the model. For the backward deletion process, increase in OBJ of at least 6.63 (P = 0.01, 1 degree of freedom) was required to retain the covariate. Creatinine clearance (CLCR) was primarily added into the basic model because CLCR showed a significant correlation with CL among the candidates for covariates. Empirical Bayesian estimates of the parameters, such as CLj for each patient, were determined by means of the post hoc option of the NONMEM estimation step, using the population parameters in the updated model. The relations between other covariate candidates and post hoc estimates of meropenem PK parameters were investigated graphically at first. Model development was performed by the difference of OBJ described above and by visually checking the figures. To evaluate the validity of the population PK model developed, a nonparametric bootstrap technique was conducted. One thousand bootstrap resampled data sets were generated, each containing the same number of patients as the original data set, and each of them was fitted individually to the final population PK model. Geometric mean and 95% confidence intervals for parameter estimates
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successfully analyzed including the covariance step were compared to the mean parameter estimates obtained from the original data set. To check the suitability of the final model for PK simulation, a visual predictive check was performed. Based on the final model, 1,000 plasma concentration profiles at the dose of 1 g q8h by 0.5-h infusion were basically simulated without residual errors, and 95% prediction intervals at each time point were plotted with the observed data.
Table 2 MIC distributions of meropenem against pathogens isolated from the patients
Bacteroides fragilis group (71)
0.03 to [16
0.25
PK–PD simulations
P. aeruginosa (322)
B0.06 to [128
1
Strains (number of stocks)
MIC range (lg/ml)
MIC50 (lg/ml)
MIC90 (lg/ml)
E. coli (141)
B0.015–0.06
B0.015
0.03
MSSAa (58)
B0.06–0.12
0.12
0.12
BLNARb (112) Acinetobacter spp. (110)
0.03–2 0.12–32
0.25 0.25
0.5 1 2 16
Data were obtained from the Jpn J Antibiot [14]
Plasma concentrations of meropenem were simulated for 1,000 virtual subjects, 500 males and 500 females. The virtual subject dataset was generated by SAS. The dataset consisted of dosage regimen, sampling time (0.01 h of grid interval), and subject characteristics (CLCR and gender). The dosage regimens for simulations were 0.5 g q12h, 1 g q12h, 0.5 g q8h, 1 g q8h, 2 g q8h, or 0.5 g q6h. The infusion duration was basically 0.5 h. The CLCR of each subject was randomly generated from a log-normal distribution using arithmetic mean (4.44) and standard deviation (0.38) of logarithm of observed CLCR in the Phase 3 study. A Monte Carlo simulation for virtual plasma concentrations was conducted using $SIMULATION mode in NONMEM. Individual CL and Vc were generated from a log-normal distribution based on the population PK mean value as the mean and interindividual variability as the variance. Calculating %T [ MIC at MICs of 0.06–32 lg/ml for each plasma meropenem profile on the 4th day (steady state) in 1,000 simulated subjects, mean and 95% prediction intervals of %T[MIC were obtained for each MIC and each dose. Target attainment rates for bacteriostatic exposures (target, 30%T [ MIC), bactericidal exposures (target, 50%T [ MIC), or clinical response exposures (target, 75%T [ MIC) were also calculated. The previous study reported that 80% of clinical response rate was obtained in febrile neutropenic patients with more than 75%T [ MIC for meropenem [13]. Target attainment rate against each strain, such as Escherichia coli (E. coli), methicillin-susceptible Staphylococcus aureus (MSSA), b-lactamase-negative ampicillinresistant Haemophilus influenzae (BLNAR), Acinetobacter spp., Bacteroides fragilis group, and Pseudomonas aeruginosa (P. aeruginosa) were predicted, using the reported MIC distribution data of meropenem against clinically isolated strains shown in Table 2 [14]. E. coli and MSSA were selected as typical gram-negative and gram-positive organisms, respectively. P. aeruginosa was selected as a representative organism in association with severe infections leading to significant morbidity. To investigate the effect of infusion duration, target attainment was simulated for a longer infusion time of 1, 2, 3, or 4 h against pathogens at various MIC breakpoints.
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a
Methicillin-susceptible Staphylococcus aureus
b
b-Lactamase-negative ampicillin-resistant Haemophilus influenzae
Results Population PK analysis As no PK data were obtained for 2 patients, a total of 209 plasma meropenem concentrations from 98 patients (1–3 concentrations from each patient) were used for population PK analysis. The critical models during model development are summarized in Table 3. Interindividual variability was modeled for CL and Vc. An exponential error model was found suitable for the interindividual variability and the intraindividual (residual) variability. Among the candidates for covariates, CLCR showed a significant correlation with CL. The covariate model for CL was described according to a power function as follows: hCL CR CLCR CL ¼ TVCL CLCR;geomean where CLCR,geomean is the geometric mean (85 ml/min) of CLCR and hCLCR is the influence factor. TVCL reveals a typical value of clearance, which describes CL when CLCR was equal to the geometric mean of CLCR in the population. As there was no CLCR measurement for 1 patient, the CLCR,geomean was used instead of the individual CLCR for that case. hCLCR (0.648) was significantly different from zero, and thus CLCR was incorporated on CL into the model. After the incorporation of CLCR on CL into the model, influence of gender (GEN) was estimated. A categorical variable was modeled as follows: Vc ¼ TVVc hGEN in which TVVc is the population mean value of Vc of meropenem in male patients, and hGEN is assigned as 1 for males. As hGEN (0.793) was different from unity, there existed some gender difference in Vc. The goodness of fit for the model was improved by the incorporation of the
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Table 3 Major investigated models during population PK analysis Model
Equations
DOBJ from:
OBJ
Model 1 1 (basic) 2 3 (final) 4 (full) 5
CL = hCL, Vc = hVC , Q = hQ, Vp = hVP
Model 2
Model 3
650.581
–
–
–
CL = hCL 9 ðCLCR =85Þ
582.774
-67.807
–
–
CL = hCL 9 ðCLCR =85ÞhCLCR VC = hVC 9 hGEN
575.331
–
-7.443
–
9 hCL,GEN VC = hVC 9 hGEN
569.104
–
–
-6.227
9 hGEN VC = hVC 9 hGEN
575.365
–
–
0.034
hCLCR
hCLCR
CL = hCL 9 ðCLCR =85Þ
hCLCR
CL = hCL 9 ðCLCR =85Þ
CLCR, creatinine clearance (ml/min) calculated by Cockcroft and Gault equation, geometric mean of CLCR 85 ml/min; GEN, gender, male:female = 64:34
effect of gender on Vc. Body weight did not show any correlation with Vc. After the incorporation of gender on Vc, influence of gender on CL was estimated. By the foregoing forward selection step, the following full population PK model was suggested: CL ¼ hCL ðCLCR =85Þ
hCLCR
hCL;GEN expðgCL Þ;
Table 4 Final estimates of population PK parameters Parameter
hCL
CLðL=hÞ ¼ 11:2 ðCLCR =85Þ0:648 ; Vc ðLÞ ¼ 13:8 hGEN ; male; hGEN ¼ 1; female; hGEN ¼ 0:793; Vc for female is 10:7 L; QðL=hÞ ¼ 4:20; Vp ðLÞ ¼ 3:10: The parameter estimates in the final population PK model are presented in Table 4. The interindividual variability (x2) for CL was 0.0441 and coefficient of variance (CV, %) was 21.2%. The value of x2Vc was 0.0271
Geometric mean
95% CI
11.2
11.2
10.5–11.8
hCLCR
0.648
0.648
0.544–0.773
x2CL
0.0441
0.0392
0.0157–0.0685
VC (L) = hVC 9 hGEN 9 exp(gV1 )
Vp ¼ h V p : As the inclusion of BLOCK (2) into $OMEGA in the full model (model 4 in Table 3) provided a decrease in OBJ of 0.543, no significant covariance between interindividual variability to CL and that to Vc was found. A combined exponential and additive error model, such as ‘‘Cij = Cpred,ij 9 e1,ij ? e2,ij’’, instead of the exponential error model in the final model (model 3 in Table 3), resulted in a small estimate (0.000194) of additive error margin (e2,ij), and the decrease in OBJ was 0.153 (P = 0.696). During the backward deletion process to build the final model, it was found that the coefficient of gender effect on CL could be excluded from the full model without causing a significant increase of the OBJ (6.227, P [ 0.01), whereas all other coefficients should remain in the model. The final model obtained was as follows:
Bootstrap validationa
CL (L/h) = hCL 9 ðCLCR =85ÞhCLCR 9 exp(gCL)
Vc ¼ hVc hGEN exp gVc ; Q ¼ hQ ;
Final estimates
hVC
13.8
13.6
11.4–15.7
hGEN for female
0.793
0.795
hGEN for male
1
–
x2Vc
0.0271
0.004
4.20
3.91
1.15–11.6
3.10
3.04
1.61–5.42
0.0645
0.0623
Q (L/h) = hQ hQ
0.660–0.957 – 0–0.661
Vp (L) = hVP hVP Y = F9exp(e) r2
0.0415–0.0892
– not calculated, CI confidence intervals, Y observed meropenem concentration, F predicted meropenem concentration a
All 1,000 computations were completed successfully
with CV of 16.6%. Regarding residual error, the value of intraindividual variability (r2) was 0.0645 and CV was 25.8%. The final model adequately described the observed data except for five outliers (weighted residuals [ ±5) from three patients. Even after excluding all the data for these patients’ data (six time points), parameter estimates changed little, and the data for all patients were included in the calculations of the final parameters. The reliability and stability of the final population PK model were confirmed by a bootstrap technique. As shown in Table 4, the parameter estimates obtained based on the original data set were close to the geometric means of the parameter estimates and fell within 95% confidence
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(B)
100
Plasma concentration (µg/mL)
Plasma concentration (µg/mL)
(A) 10
1
0.1
0.01 0
2
4
6
8
100
10
1
0.1
0.01 0
10
2
(C)
6
8
10
8
10
(D)
100
Plasma concentration (µg/mL)
Plasma concentration (µg/mL)
4
Time after dosing (h)
Time after dosing (h)
10
1
0.1
0.01 0
2
4
6
8
100
10
1
0.1
0.01 0
10
2
Fig. 1 The observed plasma concentration of meropenem obtained from 98 Japanese adult patients and predicted lines at 1 g q8h. a CLCR C 70 ml/min: infusion duration was 0.5 h for simulated lines and less than 1 h for observed data. b CLCR C 70 ml/min: infusion duration was 1 h for simulated lines and 1 h or more for observed data. c 50 B CLCR \ 70 ml/min: infusion duration was 0.5 h for simulated lines and all actually applied time for observed data. d 30 B CLCR \ 50 ml/min: infusion duration was 0.5 h for simulated
4
6
Time after dosing (h)
Time after dosing (h)
lines and all actually applied time for observed data. The plots (open circle) represent observed values obtained from 98 patients. The broken lines show 2.5th and 97.5th percentile of the simulated plasma concentrations obtained from a Monte Carlo simulation of 1,000 virtual patients using parameter estimates from the final model. Bold lines, population mean for males, thin lines, population mean for females
intervals of these parameter estimates. The observed plasma concentrations and 95% prediction intervals of predicted plasma concentration profiles were indicated categorically by renal function groups (Fig. 1). The prediction intervals of model-predicted concentration including interindividual variability (IPRED) of virtual patients included most of the observed values evenly. The simulated concentrations for male patients were similar with those for female patients. To understand changes of Cmax, Tmax, and plasma concentration levels with prolonged infusion time, mean simulated plasma concentrations of patients with normal renal function at a dose of 1 g q8h by 0.5-, 2-, or 4-h infusion are shown in Fig. 2.
PK–PD simulations for various MICs One thousand virtual subjects were generated in silico for three subgroups according to renal function (CLCR): CLCR C 70 ml/min (normal renal function group),
123
Plasma concentration (µg/mL)
100
10
1
0.1
0.01 0
2
4
6
8
Time after dosing (h)
Fig. 2 Simulated plasma concentration in febrile neutropenic patients with normal renal function (CLCR C 70 ml/min) at 1 g q8h (males). Solid line, predicted curve for 0.5-h infusion; broken line, predicted curve for 2-h infusion; dotted line, predicted curve for 4-h infusion
70 [ CLCR C 50 ml/min (reduced renal function group), and 50 [ CLCR C 30 ml/min (moderate renal impairment group). Meropenem dosage was identical for all subjects,
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Table 5 Effects of renal function on %T [ MIC values of meropenem for various MICs (dose: 1 g q8h by 0.5 h infusion) MIC of bacteria (lg/ml)
%T [ MIC (%), mean (95% prediction intervalsa) 70 ml/min B CLCR
50 B CLCR \ 70 ml/min
30 B CLCR \ 50 ml/min
32
10.2 (4.9–16.9)
14.3 (8.2–23.2)
18.6 (10.2–31.9)
16
21.5 (12.3–35.8)
30.3 (18.4–48.7)
38.9 (22.4–62.5)
8
33.7 (18.1–56.3)
47.3 (28.6–76.6)
59.7 (34.2–95.0)
4
46.5 (24.5–75.9)
64.2 (39.6–100)
78.3 (47.3–100)
2
59.4 (32.1–96.5)
79.2 (50.9–100)
90.7 (60.1–100)
1
71.4 (40.5–100)
90.1 (62.2–100)
96.8 (72.7–100)
0.5 0.25
81.5 (49.1–100) 89.1 (57.9–100)
96.1 (73.4–100) 98.7 (84.7–100)
99.1 (85.4–100) 99.8 (98.1–100)
0.12
94.4 (67.0–100)
99.7 (96.5–100)
100 (100–100)
0.06
97.5 (75.7–100)
99.9 (100–100)
100 (100–100)
One thousand virtual patients in each group were simulated by the population PK model a
95% prediction intervals were calculated by percentile method
Table 6 Prediction of %T [ MIC of meropenem for various MICs and various dosing regimens MIC of bacteria (lg/ml)
%T [ MIC (%), mean (95% prediction intervalsa) 0.5 g q12h
0.5 g q8h
0.5 g q6h
1 g q12h
1 g q8h
2 g q8h
32
0.3 (0–2.3)
0.5 (0.0–3.4)
0.8 (0.0–5.0)
6.7 (3.3–10.8)
10.2 (4.9–16.9)
21.5 (12.3–35.8)
16
6.7 (3.3–10.8)
10.2 (4.9–16.9)
14.0 (6.8–23.7)
14.3 (8.2–23.3)
21.5 (12.3–35.8)
33.7 (18.1–56.3)
8
14.3 (8.2–23.3)
21.5 (12.3–35.8)
29.2 (16.4–49.6)
22.4 (12.0–36.9)
33.7 (18.1–56.3)
46.5 (24.5–75.9)
4
22.4 (12.0–36.9)
33.7 (18.1–56.3)
45.5 (24.1–77.0)
30.9 (16.3–50.1)
46.5 (24.5–75.9)
59.4 (32.1–96.4)
2
30.9 (16.3–50.1)
46.5 (24.5–75.9)
62.2 (32.7–100)
39.5 (21.4–63.6)
59.4 (32.1–96.5)
71.4 (40.5–100)
1
39.5 (21.4–63.6)
59.4 (32.1–96.4)
77.0 (42.8–100)
48.3 (27.0–77.5)
71.4 (40.5–100)
81.5 (49.1–100)
0.5 0.25
48.3 (27.0–77.5) 57.0 (32.7–91.1)
71.4 (40.5–100) 81.5 (49.1–100)
87.8 (54.0–100) 94.5 (65.6–100)
57.0 (32.7–91.1) 65.4 (38.6–100)
81.5 (49.1–100) 89.1 (57.9–100)
89.1 (57.9–100) 94.2 (66.5–100)
0.12
65.9 (38.9–100)
89.4 (58.4–100)
98.1 (77.8–100)
73.7 (44.7–100)
94.4 (67.0–100)
97.5 (75.7–100)
0.06
73.7 (44.7–100)
94.4 (67.0–100)
99.5 (89.4–100)
80.5 (50.5–100)
97.5 (75.7–100)
99.0 (84.5–100)
One thousand virtual adult patients with normal renal function (CLCR C 70 ml/min) were administered meropenem by 0.5 h infusion at each regimen a
95% prediction intervals were calculated by percentile method
that is, 1 g q8h by 0.5-h infusion (Table 5). For patients with normal renal function (CLCR C 70 ml/min), estimated %T [ MIC are compared for various MICs and various dosing regimens (Table 6). Target attainment rates for bacteriostatic exposure (target, 30%T [ MIC), bactericidal exposure (target, 50%T [ MIC), and clinical response exposures (target, 75%T [ MIC) at each MIC were also calculated (Fig. 3). After dosing 0.5 g q8h by 0.5-h infusion, a high target attainment rate ([80%) at 30%T [ MIC was achieved with MIC of 2 lg/ml or less (Fig. 3a), that at 50%T [ MIC was achieved with MIC of 0.5 lg/ml or less (Fig. 3b), and that at 75%T [ MIC was achieved with MIC of 0.12 lg/ml or less (Fig. 3c). With dose escalation from 0.5 g q8h to 1 g q8h by 0.5-h infusion, target attainment rates for achieving 30%T [ MIC with MIC of 4 lg/ml, that at 50%T [ MIC
with MIC of 1 lg/ml, and that at 75%T [ MIC with MIC of 0.25 lg/ml were increased from 60.0% to 91.7%, from 68.4% to 89.9%, and from 63.8% to 82.7%, respectively. PK–PD simulations for various pathogens Target attainment rates for meropenem against various pathogens are shown in Fig. 4. E. coli, MSSA, and BLNAR were classified into one group of highly sensitive pathogens (MIC90 B0.5 lg/ml). Target attainment rates at 50%T [ MIC against E. coli, MSSA, and BLNAR were more than 92.3% at the dose of 0.5 g q8h by 0.5-h infusion. Target attainment rates at 50%T [ MIC against Acinetobacter spp., Bacteroides fragilis group, and P. aeruginosa were 88.5%, 84.8%, and 60.1% at the dose of 0.5 g q8h by 0.5-h infusion, respectively (Fig. 4b). With dose escalation from
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Target attainment rate (%)
(A) 30%T>MIC
(B) 50%T>MIC
(C) 75%T>MIC
100
0.5 g
80
q12h q8h q6h
60
1g
40
q12h q8h
20
2g q8h
0
.06 .12 .25
.5
1
2
4
8
16
32
.06 .12 .25
.5
MIC (µg/mL)
1
2
Fig. 3 Probabilities of achieving bacteriostatic (target, 30%T [ MIC), bactericidal (target, 50%T [ MIC), and clinical response (target, 75%T [ MIC) exposure at various MICs for meropenem by various dosing schedules: a 30%T[MIC; b 50%T [ MIC; c 75%T [
8
16
.06 .12 .25
32
.5
1
2
Target attainment rate (%)
60
E. Coli 0.5 g q8h MSSA
40
0.5 g q8h BLNAR 0.5 g q8h
20
8
16
32
0.5 g q8h 0.5 h infusion 1 g q8h 0.5 h infusion 1h 2h 3h 4h 2 g q8h 0.5 h infusion
100
100
80
4
MIC (µg/mL)
MIC. One thousand virtual patients with normal renal function (CLCR C 70 ml/min) were simulated at each regimen. MIC, minimum inhibitory concentration
(A) Target attainment rate (%)
4
MIC (µg/mL)
80
60
40
20
0 0
10
20
30
40
50
60
70
80
90 100
%T>MIC (%) 0 0
10
20
30
40
50
60
70
80
90
100
%T>MIC (%)
(B)
Target attainment rate (%)
100
0.5 g q8h to 1 g q8h and 2 g q8h by 0.5-h infusion, target attainment rates at 50%T [ MIC increased from 60.1% to 71.8% and 80.6%, respectively.
80
Acinetovacter spp.
60
0.5 g q8h 1 g q8h
Dosage regimen optimization
Bacteroides fragile group
40
0.5 g q8h 1 g q8h
P. aeruginosa 20
0.5 g q8h 1 g q8h 2 g q8h
0 0
10
20
30
40
50
60
70
80
90
100
%T>MIC (%)
Fig. 4 Target attainment rates for meropenem against various pathogens by 0.5-h infusion method: Escherichia coli (E. coli), methicillinsusceptible Staphylococcus aureus (MSSA), and b-lactamase-negative ampicillin-resistant Haemophilus influenzae (BLNAR) (a); Acinetobacter spp., Bacteroides fragilis group, and Pseudomonas aeruginosa (P. aeruginosa) (b). One thousand virtual patients with normal renal function (CLCR C 70 ml/min) were simulated at each regimen
123
Fig. 5 Target attainment rates for meropenem against P. aeruginosa in patients with various dosing schedules of meropenem. One thousand virtual patients with normal renal function (CLCR C 70 ml/min) were simulated at each regimen
Target attainment rates at 30%T [ MIC against P. aeruginosa were 77.5%, 86.6%, 91.8%, 93.4%, and 93.3% in patients with normal renal function (CLCR C70 ml/min) at 0.5 g q8h by 0.5-h infusion, 1 g q8h by 0.5-, 2-, or 3-h infusion, and 2 g q8h by 0.5-h infusion, respectively (Fig. 5). When the infusion time was prolonged from 0.5 to 3 h at 1 g q8h in the patients with normal renal function, target attainment rate at 50%T [ MIC increased from 71.8% to 83.8%. Target attainment rate at 2 g q8h by 0.5-h infusion was comparable to that after dosing of 1 g q8h by 2-h infusion in patients with normal renal function.
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839
Rates of defervescence, and clinical and bacteriological efficacy
[3, 8, 13, 21]. Population PK was investigated in Caucasian patients in two of the studies [3, 13] and in Asian patients in the other studies [8, 12]. In the present study, gender was found to have a statistically significant effect on Vc, but this effect was small, and the PK profile of meropenem in males was virtually similar to that in females (see Fig. 1). In contrast to previous reports [8, 12, 13], body weight in patients was not related to Vc either before or after incorporating the correlation of gender with Vc into the covariate model. Body weight in males (59.9 ± 11.8 kg) was similar to that in females (52.3 ± 8.03 kg), and there was no significant difference [P = 0.472 by analysis of variance (ANOVA)] in our population. Body weight includes both lean body mass (LBM) and body fat. Body weight and LBM in males are generally greater than in females, as the ratio of body fat to body weight in males is lower than that in females. The logarithm of the partition coefficient (1 - octanol/ water) in meropenem is reported as -3.28 [22], and therefore meropenem seems to be distributed to watersoluble compartments. Considering the physicochemical characteristics of meropenem, LBM in patient might be related to Vc and body weight may have an indirect relationship to Vc. In this study population, age of patients was distributed relatively higher (mean age, 59.3 years) than in other reports (mean age, 36 and 39.6 years) [3, 8]. As elderly people have more variable LBM/body weight ratio, the correlation between body weight and Vc might be masked; this may be the reason why a correlation between body weight and Vc was not observed, but instead a gender effect on Vc was observed, in this analysis. Mean of %T [ MIC at MIC of 2 lg/ml in patients with normal renal function (CLCR C70 ml/min), reduced renal function (70 [ CLCR C 50 ml/min), or moderate renal impairment (50 [ CLCR C 30 ml/min) was 59.4%, 79.2%, or 90.7%, respectively (see Table 5). The reason why patients with reduced renal function reached greater
The primary endpoint was achievement of defervescence in the present Phase 3 study. Defervescence was achieved in 40 of 100 patients (40.0%) within 4 days after the start of the treatment. Two of 9 for whom causative organisms were identified became afebrile (22.2%). Symptom improvement was observed in 47.3% of the patients (44/93 patients) after exclusion of 7 patients from the clinical evaluation. For bacteriological evaluation, a total of six bacterial strains were isolated from nine patients (Table 7). The MIC of meropenem against each isolate was 1 lg/ml or less, except for one Staphylococcus epidermidis strain with MIC of 16 lg/ml. Eight strains were determined to be eradicated by repeated culture. One strain could not be evaluated because of lack of any repeated culture. The overall eradication rate was 89% (8/9) (Table 7). The %T [ MIC values for the strains isolated from eight patients were greater than 90%, and the remaining one strain (Staphylococcus epidermidis) showed 30.9% (Table 7).
Discussion Meropenem is excreted mainly via a renal route. Approximately 60% of unchanged meropenem was excreted in the urine through the kidneys [19]. Therefore, clearance of meropenem is affected by renal function in patients [20, 21]. In this population PK model of meropenem for Japanese patients with febrile neutropenia, CL was found to be associated with CLCR but was not affected by other clinical laboratory tests as indicators of other organ functions. This result was consistent with the previous results of population PK studies of meropenem in adults, in which CLCR was reported to be the most important determinant of clearance
Table 7 Relationships between PK–PD parameters and efficacies Subject
Infusion (h)
Isolates
MIC (lg/ml)
%T [ MIC (%)
Defervescencea
Bacteriological efficacy
101
0.71
Escherichia coli
\0.06
100
No
Eradicated
201
1.12
a-Hemolytic Streptococcus
\0.06
100
No
Eradicated
803
1.07
Staphylococcus epidermidis
Yes
Eradicated
817
1
Aerobic gram-positive rod
0.25
100
No
Eradicated
1002
1.42
Staphylococcus epidermidis
1
100
No
Eradicated
1005
0.96
E. coli
\0.06
100
Yes
Eradicated
1009
1.33
Staphylococcus epidermidis
\0.06
1101
0.5
Streptococcus oralis
\0.06
1103
0.5
Coagulase-negative staphylococcus
\0.06
a
16
30.9
95.9
No
Unknown
100
No
Eradicated
100
No
Eradicated
Primary endpoint
123
840
%T [ MIC than patients with normal renal function is that the plasma level of meropenem slowly decreased in patients with impaired renal function. In other words, patients with renal impairment were able to get higher exposure to meropenem than patients with normal renal function. The Japanese package insert of meropenem (Meropen) in 2010 indicated that meropenem must be administered in a reduced dosage or prolonged dosing interval to patients with severely impaired renal function, such as CLCR B30 ml/min, and treatment schedule and dose should be adequately adjusted depending on the degree of renal function. The results of the present quantitative simulations show practical guidance for dosing regimen of meropenem in patients with reduced renal function. Within the same total daily dose, 0.5 g q6h (total, 2 g/day) by 0.5-h infusion achieved higher %T [ MIC at MIC of 8 lg/ml than 1 g q12h (total, 2 g/day) by 0.5-h infusion (see Table 6). The present results indicated that 0.5 g q6h can be a therapeutic option as more effective dosage than 1 g q12h within a total dose of 2 g/day. According to a Japanese nationwide surveillance study in 2006 [14], the MIC90 of meropenem for common pathogenic bacteria, such as E. coli, MSSA, and BLNAR, was below or equal to 0.5 lg/ml. The intermediately sensitive pathogens were Acinetobacter spp. (MIC90 1 lg/ml, 110 strains) and Bacteroides fragilis group (MIC90 2 lg/ml, 71 strains). P. aeruginosa (MIC90 16 lg/ml, 322 strains) was a comparatively low sensitivity pathogen. Because only a small number of pathogens were microbiologically documented from febrile neutropenic patients, the previous surveillance results were used for the PK–PD simulations. Most of the pathogens in febrile neutropenic patients reported by Ariano et al. [13] and observed in this study (see Table 7) were comparable with the surveillance results. Target attainment rates for achieving 50%T [ MIC against E. coli, MSSA, and BLNAR were more than 95.2% at 0.5 g q8h by 0.5-h infusion (see Fig. 4a). The PK–PD simulations in this study indicated that the optimal dosage of meropenem was 0.5 g q8h by 0.5-h infusion against highly susceptible pathogens, such as E. coli, MSSA, and BLNAR. Target attainment rates against Acinetobacter spp. and Bacteroides fragilis group were 88.5% and 84.8% at 0.5 g q8h by 0.5-h infusion, respectively (see Fig. 4b). PK–PD simulations in this study revealed that 0.5 g q8h by 0.5-h infusion is useful against intermediately sensitive pathogens, such as Acinetobacter spp. and Bacteroides fragilis group. On the other hand, target attainment rate against P. aeruginosa was only 60.1% at 0.5 g q8h by 0.5-h infusion (Fig. 4b). At 1 g q8h by 0.5-h infusion, the probability of target attainment for bactericidal exposure (target: 50%T [ MIC) achieved 71.8% against P. aeruginosa. Higher dosage (1 g q8h) along with prolonged infusion time (4 h) resulted in a high attainment rate
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J Infect Chemother (2011) 17:831–841
(88.4%). Prolonged infusion time from 0.5 to 4 h at 1 g q8h increased %T [ MIC at every MIC; e.g., patients with normal renal function at MIC of 4 lg/ml, from 46.5% (see Table 6) to 72.4%. The optimal dosage of meropenem was 1 g q8h or by longer infusion against comparatively low sensitivity pathogens, such as some strains of P. aeruginosa (especially in the case of MIC C2 lg/ml). Many studies already reported that prolonged infusion time is an alternative method to optimize meropenem pharmacodynamics [1–3, 7, 10]. The overall eradication rate was 89% (eight of nine patients; Table 7). According to the microbiological tests, MIC values of eight strains in nine patients were B1 lg/ml, except for one strain (MIC = 16 lg/ml). The probability of achieving 75%T [ MIC in these nine patients was also 89%. Although there were only a small number of patients for whom the causative organisms were identified, the target PK–PD attainment was comparable to the microbiological response. In febrile neutropenic patients, an 80% clinical response rate was evident when the %T [ MIC for meropenem exceeded 75% of the dosing interval [13]. After dosing of 0.5 g q8h by 0.5-h infusion to patients with normal renal function, a high target attainment rate ([80%) at 75%T [ MIC was achieved at MIC of 0.12 lg/ml or less (see Fig. 3c). By dose escalation, target attainment rates for achieving 75%T [ MIC with MIC of 0.25 lg/ml were increased from 63.8% (0.5 g q8h) to 82.7% (1 g q8h). After dosing of 1 g q8h by 2-h infusion, a high target attainment rate ([80%) at 75%T [ MIC was achieved at MIC of 0.5 lg/ml or less (data not shown). Because some febrile neutropenic patients may suffer from bacteria with relatively high MICs, it is tempting to speculate that larger dose amount and/or longer infusion time would be necessary for febrile neutropenic patients. Careful attention should be paid when the causative pathogen is unknown and the site of infection is suspected to be apart from blood, because the present PK–PD simulations were based on plasma concentrations of meropenem. It was reported that the concentration–time profile in cerebrospinal fluid (CSF) is distinct from that in plasma; Cmax in CSF is lower and t1/2 is longer than those in plasma [23]. An additional possibility is that undetectable pathogens may have high MIC value. Considering the foregoing, a higher dose may be required for febrile neutropenic patients when the causative pathogen is not identified. Hathorn and Lyle [24] reported in a review article that the ratio of febrile neutropenic patients microbiologically infected was between 30% and 50%. In the current study, defervescence was achieved in 40 patients among 100 patients (40%) within 4 days after starting the treatment with dose of 1 g q8h, suggesting comparable ratios of antibiotic treatment to effective patient population.
J Infect Chemother (2011) 17:831–841
Because many febrile episodes in neutropenic patients can be treated successfully with early initiation of empirical antimicrobial therapy, PK–PD simulations in this study using %T [ MIC as a PD index for meropenem could suggest useful information for meropenem dosing when bacterial infection is suspected as the disease cause. In conclusion, this article reports that microbiological efficacy of meropenem correlated with PK–PD parameters based on a developed population PK model in febrile neutropenic patients from a Phase 3 study conducted in accordance with GCP standards in Japan. Additionally, the effect of meropenem against various bacteria was predicted with various dosing regimens and infusion durations. The population PK parameters in this study were useful in simulating PK of meropenem at various dosage regimens to calculate %T [ MIC. The present PK–PD analyses under various conditions such as dosage methods and MICs of meropenem against causative pathogens will be useful in the treatment of febrile neutropenia.
841
10.
11.
12.
13.
14.
15.
16.
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