Eur J Drug Metab Pharmacokinet DOI 10.1007/s13318-015-0276-3

ORIGINAL PAPER

Population Pharmacokinetics and Pharmacodynamics of Piperacillin/Tazobactam in Patients with Nosocomial Infections Rong Chen1 • Qing Qian1 • Meng-ru Sun1 • Chun-yan Qian1 • Su-lan Zou1 Ming-li Wang1 • Li-ying Wang1



Ó Springer International Publishing Switzerland 2015

Abstract Objective The study was to establish a population pharmacokinetic (PPK) model of piperacillin (PIP) and tazobactam (TAZ) that explain pharmacokinetic variability and to propose optimized dosage regimens in patients with nosocomial infections. Methods In total, 310 PIP and 280 TAZ concentration– time points were collected at steady state over multiple dosing intervals from 50 patients who received PIP/TAZ infused within 30 min or over 3 h. Drug analysis was performed by high-performance liquid chromatography (HPLC). Nonlinear mixed effects modeling was employed to develop PPK model and 1000 Monte Carlo simulation was used to predict the probability of target attainment (PTA) with a target time of non-protein-bound concentration above MIC [ 50 % of the dosing interval. Results A model with one-compartment model had the best predictive performance for the PPK model. The population estimates of PIP were 13.8 L/h (31.1 %) for clearance (CL) and 21.7 L (38 %) for volume of distribution (V). The population estimates of TAZ were 9.3 L/h (29.1 %) for CL and 16 L (35.3 %) for V. Influence of creatinine clearance (CLcr) and body weight were identified as important covariates for PIP/TAZ CL and V, respectively. A 30-min infusion of 4 g every 6 h achieved robust (C90 %) PTAs for MIC B 16 mg/L. As an alternative mode of administration, a 3-h infusion of 4 g every 6 h achieved robust PTAs for Pseudomonas aeruginosa and Klebsiella pneumoniae. & Ming-li Wang [email protected] 1

Department of Pharmacy, The Third Affiliated Hospital of Soochow University, Changzhou, Jiang Su, China

Conclusions Prolonged infusions achieved better PTAs compared with shorter infusions at similar daily doses. This benefit was most pronounced for MICs between 16 and 40 mg/L.

1 Introduction PIP/TAZ is an intravenously administered combination of a b-lactam antibiotic with a b-lactamase inhibitor. This drug combination is frequently used in the treatment of infectious disease because of its good bactericidal activity against microorganisms. Resistance among common bacteria is increasing throughout the world. Of particular concern, China has observed a dramatic rise in the frequency of multidrug-resistant Pseudomonas aeruginosa, fluoroquinolone-resistant Enterobacteriaceae and Enterobacteriaceae harboring extended-spectrum b-lactamases [1]. In vitro and in vivo response–exposure studies have demonstrated that both dose and dosing schedules may influence the effect of an antimicrobial. An appropriate regimen should attain a satisfactory PD endpoint and at the same time lowers the drug toxicity. However, it is not unusual that a fixed regimen in the target population can produce clinical failures to some patients because of under-dosage while causing toxicity to the other patients due to excessive dosing. The diversity of drug effects in an infection population comes from the hierarchies of pharmacokinetic (PK) variability and the hierarchies of the bactericidal effects of the antimicrobial against different pathogens. PK/PD relationships have become available as tools for individualizing antimicrobial therapy, and have led to the optimization of dosage regimens to improve the outcome and reduce the selection of resistant mutants [2]. PIP, as b-lactam antibiotics, which demonstrate time-dependent killing, and the

R. Chen et al.

parameter for the bactericidal effect is the time during which the concentration of the free antibiotic exceeds the drug’s minimum inhibitory concentration (MIC) for approximately 40–50 % of the dosing interval [3]. The primary objective of this study was to evaluate the PPKs of PIP/TAZ in hospitalized patients and determine the influence of covariates on PPK. The secondary objective was to investigate the prevalence of healthcare-associated infection (HCAI). Lastly, the probability of target attainment (PTA) was compared between various dosage regimens using simulation to propose optimal dosage regimens for PIP/TAZ.

2 Materials and Methods 2.1 Patients Patients aged 18 years or older who were hospitalized in the Third Affiliated Hospital of Soochow University known or suspected to have nosocomial infections were enrolled in this multiple-dose, open-labeled study. The study protocol and informed consent were approved by the independent ethics committees and institutional review boards of the Third Affiliated Hospital of Soochow University (ethics approval number was 2012–52). Written informed consent was obtained from all patients at the time of enrollment. All donors freely gave written informed consent to donate their blood sample. The following information was recorded for each patient: gender, age, weight (WT), height (HT), body surface area (BSA), current diagnoses; renal function [serum creatinine (SCR), blood urea nitrogen (BUN), creatinine clearance (CLcr) were estimated for each patient with the Cockcroft–Gault Eq. [4]; hematology [hematocrit (HT), hemoglobin (HGB), albumin (ALB), total protein (TP)]. Patients were excluded if they had any of the following: concurrent hemodialysis, peritoneal dialysis, indwelling peritoneal catheters or shunts, plasmapheresis, hemoperfusion, or severe organ failure; known or suspected hypersensitivity or adverse reaction to PIP/TAZ or blactamase inhibitors; albumin \20 g/L; mental disorder and could not cooperate. 2.2 Therapy Regimen and Blood Sampling Patients who met inclusion criteria were assigned to receive PIP/TAZ either by traditional 0.5-h infusion therapy (TIT), or prolonged over 3-h infusion therapy (PIT) for a minimum of 4 days and not more than 14 days according to the pathogenetic condition. Blood samples were drawn from the arm opposite to the infusion site after subjects reached the steady state in

regimens. For TIT, samples (5 mL) were received at the following sampling time points: 0 (prior to the next dose), 0.25, 0.5, 1, 2 and 4 h. For PIT, blood samples were collected immediately prior to drug administration and 1, 2, 3 (end of infusion), 4, 6 and 8 h after the start of the infusion [5]. Samples were centrifuged, separated and frozen at -70 °C immediately until the time of analysis. 2.3 Bioassay Methodology A validated technique [6] was used for preparing the plasma samples. Briefly, after plasma was separated by centrifugation at 3000g for 10 min, 500 lL aliquots of plasma deproteinization with acetonitrile and the removal of lipids with dichloromethane. Separation and quantitation are achieved with an eluent mixture composed of acetonitrile– potassium phosphate based on ion-suppression chromatography on a C18 reversed-phase column with ultraviolet detection at 220 nm. A linear relationship was obtained between peak area and drug concentration in the range studied (0–400 mg/L for PIP and 0–50 mg/L for TAZ). The intra- and inter-day precision of the assays, expressed as coefficients of variation, was less than 5.4 and 8.2 %, respectively, and the accuracy was more than 95 %. The lower limit of quantification (LLOQ) for standard samples was about 0.5 mg/L for PIP and 0.1 mg/L for TAZ. 2.4 Population Pharmacokinetic Analysis Pharmacokinetic analysis was conducted using the nonlinear mixed effects modeling program, PDx-Pop 5 and NONMEM program (Version 7.1; ICON Development Solutions, Ellicott City, MD), to estimate the population mean parameters, interindividual (g) and residual (e) random effects. The structural and stochastic models were developed first. One- or two-compartment models with linear elimination were selected based on goodness-of-fit plots, precision of estimates and the likelihood ratio test within NONMEM. The first-order conditional estimation method (FOCE) [7] implemented in NONMEM was used throughout this analysis. 2.5 Model Development Initially, the base model was characterized by simple description of the key pharmacokinetic parameters without fixing with clinical covariates. The minimum objective value (OFV) calculated during this step was used as a standard for assessing the impact for the inclusion and exclusion of different covariates in the following models. The interindividual variability (IIV) of each parameter of the model was modeled using the following exponential error model (Eq. 1). The value of a parameter in an

PPK/PD of PTZ

individual (Pi) is a role of the parameter value in the typical _

individual (P) and an individual deviation represented by gi. It was assumed that gis in the population were symmetrically distributed, zero-mean random variables with a variance that was estimated as part of the model estimation from Eq. 1: _

Pi ¼ P  expðgi Þ:

ð1Þ

The intrasubject variability (ISV) was evaluated (i.e., additive error, Eq. 2; proportional error, Eq. 3; combined additive and proportional error, Eq. 4) to describe the ISV. Y ¼ IPRED þ e

ð2Þ

Y ¼ IPRED  expðeÞ

ð3Þ

Y ¼ IPRED  expðe1 Þ þ e2 ;

ð4Þ

where Y represented the observation, IPRED was the individual predicted concentration and en was the residual ISV with means of zero and variances of r2. In the second step, the influence of each subject covariate on the PK parameters was investigated. Covariate influences on PK parameters were examined by plotting empirical Bayesian estimates of individual parameters against covariates. Covariates identified as potentially influencing pharmacokinetic parameters were then tested formally in NONMEM. Significant covariates were cumulatively added to the model in a stepwise, descending order of potential influence on model parameters in accordance with their contribution to the reduction in OFV in the initial analysis, until there was no further significant reduction. The results were considered statistically significant if the decreases of OFV was greater than 3.84 units (P \ 0.05, 1° of freedom), and this difference in OFV (between two nested models) was assumed to be asymptotically v2 distributed. Relationships between continuous covariates and pharmacokinetic parameters were modeled using linear (Eq. 5), linear proportional (Eq. 6) and power models (Eq. 7) with the covariate (Cov) normalized to the population median for the dataset. Categorical covariates were modeled as shown in Eq. 8.   Covi P ¼ h1  ð5Þ Covm

Covariates were selected according to the metabolism characteristic of PIP/TAZ [8, 9, 10, 11]. The continuous covariates evaluated were demographic parameters, hematology and renal function. The examined categorical covariates included gender. In the final step, a stepwise backward elimination technique was performed in which the influence of each covariate was removed from the model by setting the appropriate coefficient to zero and re-estimating parameters in the model. During this step, an increase of OFV from the full model of at least 10.8 units (P \ 0.001, 1° of freedom) was used as the chosen criterion for retaining the covariate in the model. 2.6 Model Evaluation Finally, model performance was assessed by graphical analysis (goodness-of-fit plots). Basic goodness-of-fit plots include observed (DV) vs. population predictions (PRED), as well as weighted residuals (WRES) vs. PRED. Nonparametric bootstrap analysis was conducted using the bootstrap option [12]. The 1000 replicates were generated by repeated random sampling with replacement from the original dataset. The final model parameter estimates were compared with the mean and 95 % confidence intervals (Cls) of the bootstrap replicates of the final model. The simulation method was conducted by the visual predictive check (VPC). A visual predictive check was performed by simulating 1000 datasets with identical design to the original dataset using the parameter estimates from the final model, including the intersubject and residual variability. The simulated median and 5th, 95th percentiles of the concentrations predicted from the simulations were plotted against time with the observed PIP/TAZ concentrations. 2.7 Microbiology

P ¼ h1 þ h2  ðCovi  Covm Þ   Covi h2 P ¼ h1  Covm

ð6Þ ð7Þ

The point prevalence surveys of healthcare-associated infection (HCAI) were carried out in the Third Affiliated Hospital of Soochow University in 2013 and 2014. The susceptibilities of the bacteria to PIP/TAZ were determined by the microdilution method according to the criteria of the Clinical and Laboratory Standards Institute (CLSI) at the microbiology labs. Conditions were controlled by using Escherichia coli (E.coli) ATCC 25922 and Pseudomonas aeruginosa (P. aeruginosa) ATCC 27853.

P ¼ h1  hCov 2 ;

ð8Þ

2.8 Pharmacodynamic Analysis

where the hs are the parameters to be estimated, and h1 represents the typical value of a pharmacokinetic parameter in an individual with the median value for the covariate.

PTAs with targets fT[MIC C 50 % representing nearmaximal killing for PIP was used to predict the microbiological. The protein binding of PIP is between 20 and

R. Chen et al.

30 % [13]. PKPD profiles of various dosage regimens include short (30 min) and prolonged (3 h) infusion. fT C MIC was calculated using the following onecompartment intravenous infusion Eq. 9 [1]:      R0 =CL Vd fT [ MIC ¼ TINF  ln R0 =CL  MIC CL     R0 =CL  R0 =CL  eðCLTINF =Vd Þ Vd þ ln MIC CL ð9Þ % fT [ MIC ¼ fT [ MIC  100=DI,

3.1 Patient Demographic and Clinical Data Fifty patients were enrolled. Their demographic characteristics are presented in Table 1. In total, 310 and 280 serum samples were collected for PIP and TAZ analysis, respectively. 3.2 Population Pharmacokinetic Analysis

ð10Þ

where TINF is the infusion time (h), ln the natural logarithm, R0 the infusion rate calculated as (dose in mg 9 fraction unbound/TINF), CL the plasma clearance (L/h), MIC the minimum inhibitory concentration (mg/L), Vd the volume of distribution (L), e the exponent and DI the dosing internal (h). Table 1 Demographics of patients included in analysis (mean ± SD) Categorical covariate

Value

Number of patients (male/female)

31/19

Age (years)

57 ± 16

Weight (kg)

61.1 ± 10.1

Height (cm)

160.3 ± 7.9

Body surface area (m2)

0.90 ± 0.06

Urea nitrogen (mmol/L)

4.4 ± 0.90

Hematocrit (%)

39.8 ± 8.40

Hemoglobin (g/L)

115 ± 28.7

Albumin (g/L)

36.3 ± 3.25

Total protein (g/L) Serum creatinine (lmol/L)

65.3 ± 5.60 119 ± 146.9

Creatinine clearance (mL/min)*

68.7 ± 32.25

* According to the Cockcroft–Gault formulation

Table 2 Summary of the covariate model-building steps of piperacillin

3 Results

Parameter

Model 1

The serum concentration–time was well described by the one-compartment model for PIP/TAZ which was similar to previous study [14, 15]. This model was implemented in the PREDPP library subroutine ADVAN1 and TRANS2. The PK parameters obtained from the model were CL and V. Based on the minimum OFV and the distribution of residuals in the diagnostic plots of the base model, proportional error model was selected for the residual variability. 3.3 Covariates Analysis During the forward inclusion step, CLcr was shown to have an effect on CL and WT had an effect on V. After a backward elimination investigation, CLcr and WT were retained in the model. Removal of each of these two covariates resulted in significant increases in OFV (?284.5, ?258.44) for removal of CLcr and WT. Compared with the base model, the covariate effects in the final model explained approximately 138.9 % of the IIV in CL, 78.6 % of the IIV in V, the final models are expressed in CL = 9.14 ? 4.6(CLCR/68.7), V = 12.2 ? 9.49(WT/61.1). The population estimates of PIP were 13.8 L/h for CL and 21.7 L for V, the IIV were 31.1 % for CL and 38 % for V,

Model 2

Model 3

Model 4*

PIP OFV 

1511.82

1227.32

1253.38

1165.73

DOFV (P valueà)



-284.5 (P \ 0.001)

-258.44 (P \ 0.01)

-346.09 (P \ 0.01)

CL (% RSE§)

2.72 (10.4)

8.99 (4.94)

14.8 (4.01)

9.14 (4.89)

V (% RSE§)

21.7 (11.2)

22.0 (2.89)

12.1 (7.59)

12.2 (7.80)

CL, hCLcr



4.63 (5.83)



4.60 (5.07)

V, hWT





9.57 (8.84)

9.49 (9.11)

xCL (%)

170

81.3

48.4

31.1

xV (%)

116.6

41.7

57.3

38

r (%)

5.36

9.14

9.55

9.32

* Final model  

Objective function value

à

Calculated by the log-likelihood ratio test

§

Percent relative standard error (SE/estimate 9 100 %)

PPK/PD of PTZ Table 3 Summary of the covariate model-building steps of tazobactam

Parameter

Model 1

Model 2

Model 4*

Model 3

OFV 

829.31

775.90

336.03

241.96

DOFV (P valueà)



-53.41 (P \ 0.001)

-493.28 (P \ 0.01)

-587.35 (P \ 0.001)

CL (% RSE§)

7.99 (15.3)

6.9 (5.72)

9.71 (3.92)

5.80 (4.75)

V (% RSE§)

172 (34.1)

14.1 (5.56)

7.88 (6.40)

7.88 (6.54)

CL, hCLcr



3.81 (6.31)



3.52 (4.89)

V, hWT





8.12 (5.07)

8.12 (5.20)

x CL (%)

66.9

40.5

45.8

29.1

xV (%)

64.2

34.4

35.4

35.3

r (%)

31.4

55.4

5.25

5.32

* Final model   à §

Objective function value Calculated by the log-likelihood ratio test Percent relative standard error (SE/estimate 9 100 %)

Table 4 Population pharmacokinetic parameter estimates Parameter

Symbol

NONMEM mean

95 % Cl*

Bootstrap mean

95 % Cl 

Bias (%)

7.82–10.0

-2.4

11.1–14.5

4.9

PIP CL (L/h)

h1

V (L)

h2

Impact of CLcr on CL

h3

4.60

4.09–5.11

4.82

3.92–5.72

4.8

Impact of WT on V

h4

9.49

7.79–11.2

9.13

8.03–10.2

-3.8 3.9

9.14 12.2

8.26–10.0 10.3–14.1

8.92 12.8

IIV-CL (%)

xCL

31.1

22.1–40.1

32.3

21.3–43.3

IIV-V (%)

xV

38.0

27.0–48.9

36.8

29.9–43.7

3.2

RSV (%)

d

9.33

8.0–10.4

9.38

6.86–11.4

1.14

CL (L/h)

h1

5.80

5.28–6.32

6.11

5.52–6.69

5.3

V (L) Impact of CLcr on CL

h2 h3

7.88 3.52

6.87–8.89 3.18–3.86

7.54 3.65

6.54–8.54 3.24–4.06

-4.3 3.7

Impact of WT on V

h4

8.12

7.29–8.95

7.72

6.72–8.72

-4.9

IIV-CL (%)

xCL

29.1

22.2–35.9

27.7

18.9–36.5

-4.8

IIV-V (%)

xV

35.3

26.5–44.1

33.5

23.5–43.5

-5.0

RSV (%)

d

3.88–6.24

-3.6

TAZ

5.32

4.79–5.83

5.19

* 95 % confidence interval (mean ± 1.96 9 standard error of the estimate)  

The 2.5th and 97.5th percentiles of 960 and 935 successful bootstrap distribution of parameter estimates for PIP and TAZ, respectively. Bias %, relative bias of estimates by NONMEM to the mean estimates by bootstrap procedures

the ISV was 9.32 %, respectively. A summary of covariate model-building steps for PIP is shown in Table 2, which represents the steps that resulted in statistical significance in the OFV during the development of the PPK model. After a stepwise screening procedure, CLcr on CL and WT on V were identified as important covariates according to the OFV value. Inclusion of CLcr and WT decreased the OFV by 53.41 and 493.28, respectively, and reduced IIV of CL and V by 37.8 and 28.9 %, respectively. The final models were expressed in CL = 5.80 ? 3.52(CLCR/68.7),

V = 7.88 ? 8.12(WT/61.1). The population estimates of TAZ were 9.3 L/h for CL and 16 L for V, the IIV was 29.1 for CL and 35.3 for V, and the ISV was 5.32 %. A summary of covariate model-building steps for PIP is shown in Table 3. The population parameter values and inter- and intraindividual variability estimated by the final model are shown in Table 4. The PK parameters were generally well estimated, with the relative standard error (%RSE) around 4.89–7.8 and 4.75–6.54 % for PIP and TAZ, respectively.

R. Chen et al.

3.4 Model Evaluation From the original dataset, 1000 replicate datasets were generated and used for the evaluation of the stability of the full model and accuracy of parameter estimates. Following a single attempt of 1000 bootstrap analyses, minimization terminated due to rounding errors were excluded from the analysis. Among the 1000 bootstrap samples, 960 and 935 samples were converged for PIP and TAZ, respectively. The summary of parameter estimates from the bootstrap procedure is presented in Table 4. No difference[5 % in the parameter estimates was observed when compared with the corresponding NONMEM results. The symmetric 95 % CIs were also congruent with the 95 % bootstrap percentile CIs. It demonstrated that the population estimates of the final model for both PIP and TAZ were not statistically different from the parameters estimated during the bootstrap process. There was good agreement between the individual predicted and observed values in the final model. The goodness-of-fit plots from the base and final model are presented in Fig. 1. The weighted residuals of predictions for the final population PK model were generally distributed around zero and were relatively symmetric ranging from -4 to 4, as shown in Fig. 2. The predicted concentrations for 1000 simulated datasets by the final model are depicted in Fig. 3. In spite of a

little underestimation of observed Cmax for the PIP/TAZ, the majority of the observed PIP/TAZ plasma concentrations were within 95 % percentiles of the simulated concentrations. 3.5 Microbiology Respiratory tract infection was the most common HCAI, followed by surgical site infection, urinary tract infection and gastrointestinal tract infection. Gram-negative bacteria were isolated most frequently and the most frequently isolated causative pathogens were E.coli followed by P. aeruginosa, Klebsiella pneumoniae (K. pneumoniae) and Acinetobacter baumannii (A. baumannii). Detailed MIC distributions for 140 E. coli, 113 P. aeruginosa, 84 K. pneumoniae and 62 A. baumannii were extrapolated from HCAI studies during 2012 and 2013. MIC data for PIP/TAZ against these bacteria are shown in Table 5. 3.6 Pharmacodynamic Target Attainment Against E. coli, the regimen of 0.5-h infusion of 4 g every 8 h only achieved the target attainment (fT[MIC C 50 %) of 32 %, but higher daily dose (12 or 16 g/d) and prolong

Fig. 1 The observed (DV) vs. population predicted (PRED) concentrations of the base model (A and B for PIP and TAZ, respectively) and final model (a and b for PIP and TAZ, respectively); where solid lines represent line of identity

PPK/PD of PTZ

Fig. 2 Weighted residuals (WRES) vs. PRED of the base model (A and B for PIP and TAZ, respectively) and final model (a and b for PIP and TAZ, respectively); where solid lines represent y = 0; dashed lines represent tendency

infusion resulted in acceptable probabilities (94–97 %). Against K. pneumoniae, target attainments were reduced; however, this was improved to 90 % with a PIP regimen of 2 every 6 h, 8 g/d, PIT. Increasing the dose or extend infusion time (4 every 6 h) did significantly elevate target attainment against P. aeruginosa (PTA C 90 %). Prolonged infusion time could significantly increase the value of PTA, and the results are shown in Table 6 and Fig. 4. PTAs of PIP for the PKPD targets were explored based on the final PPK model for full range of MIC values deemed to be susceptible by the HCAI studies (Fig. 5). PIP administrated 30-min infusion of 4 g every 8 h (daily dose 12 g) demonstrated excellent attainment percentages (PTA = 97 %) for the target of fT[MIC C 50 % by the PKPD breakpoint of 8 mg/L. Administration of 30-min infusion of 4 g every 6 h (daily dose 16 g) and 3 g every 4 h (daily dose 18 g) improved the breakpoint to 16 and 24 mg/L (PTA = 93.1 and 86.7 %, respectively). PIT, extending infusions to 3–5 h as an alternative dosage regimen, was optimized for the PKPD target fT[MIC C 50 % for PIP. As compared with 0.5-h infusion therapy, 3-h infusion of 4 g every 8 h (daily dose 12 g) increased the PKPD breakpoint from 8 to 16 mg/L (PTA = 97 and

96.3 %, respectively); 3-h infusion of 4 g every 6 h (daily dose 16 g) improved the PKPD breakpoint from 16 to 24 mg/L (PTA = 93.1 and 96.9 %, respectively); 5-h infusion of 6 g every 8 h (daily dose 18 g) elevated the PKPD breakpoint from 24 to 32 mg/L (PTA = 86.7 and 91.3 %, respectively). Compared with 30-min infusion of 3 g every 4 h (daily 18 g), PIP administrated 3-h infusion of 4 g every 6 h (daily dose 16 g) demonstrated more excellent attainment percentages (PTA = 86.7 and PTA = 96.9 %, respectively) for the target of fT[MIC C 50 % by the PKPD breakpoint of 24 mg/L. Therefore, comparisons of the prolonged infusion with therapy from traditional may be instructive. The daily dose was reduced from 10 % (2/18 g) to 25 % (4/16 g) in patients receiving continuous infusion compared with intermittent infusion.

4 Discussion This clinical trial was conducted in hospitalized patients with nosocomial infection who received multiple dose PIP–TAZ via intermittent or prolong infusion. Base on the final model, the pharmacokinetics of PIP–TAZ was well

R. Chen et al.

Fig. 3 Visual predictive check of the final pharmacokinetic model (A piperacillin, B tazobactam), a total of 1000 datasets were simulated, (—) showed the 5th and 95th percentile, (– –) showed 10th and 90th percentile, (- -) showed mean predictions

Table 5 MIC distributions for various species tested during the research Bacteria

MIC10

MIC50

MIC90

MICrange

E.coli (n = 140)

1

4

16

0.25–128

K.pneumoniae (n = 84)

1

4

32

0.25–128

P.aeruginosa (n = 113)

2

16

128

A.baumannii (n = 62)

4

128

[256

2–256 2– [256

MIC10/50/90 MIC for 10, 50 and 90 % of the organisms, respectively

fitted by the one-compartment model. Typical value of CL was calculated to be 13.8 and 9.3 L/h for PIP and TAZ. The typical CL value of current study cohort lies within the range of previously reported from 9 to 16.6 L/h for PIP and 8.4 to 11.1 L/h [10, 15, 16, 17, 18] for TAZ. Compared with the difference observed between the elimination CL and V values in these two infusion time regimens did not achieve statistical significance. Thus, it could be indicated that the infusion method had no effect on the metabolism of PIP/TAZ. As shown in PPK model, PIP and TAZ elimination halflife values progressively increased with decreasing CLcr, and plasma clearance decreased with decreasing CLcr. When CLcr was 10 mL/min, compared with 50 or 80 mL/ min, the drug clearance decreased 15 and 20 % estimated using Bayesian method with NONMEN program. This study confirmed previous results and indicated the need to adjust PIP dosage on the renal function [8]. Based on the above study, when 4 g of PIP every 8 h, 12 g/d, TIT was simulated, PTAs [ 90 % for fT[MIC targets of C30 % were obtained, which was reported from a similar study but conducted mainly in America [19]. Although the breakpoint for P. aeruginosa is 64/4 mg/L for PIP/TAZ, the majority of hospital-acquired infection patients have higher MIC90 during this study, under these circumstances, PIP 4 g every 6- by 3-h infusion appears to provide sufficient drug exposure (PTA = 91 %) to ensure clinical success with this dosing technique. The results support the notion that nosocomial patients who have P. aeruginosa infection are most dependent upon drug exposure for good clinical outcomes, which is also consistent with the findings in previous report [20]. Of the A.baumannii isolates, which are known to harbor more resistance, target attainment was generally too low to justify the use of any of above regimens as monotherapy. While larger or more frequent doses of antibacterial against A.baumannii resulted in greater probabilities of target attainment. Moreover, successful clinical outcome via continuous infusion of PIP/TAZ was observed by previously published study [18], and PTA of CSS/MIC C 2 was calculated as the PKPD target attainment. Although continuous infusion is a rational method to optimize antibiotics regimens, it is not always realistic, especially for patients who have limited intravenous access or patients who require multiple daily infusions. In summary, estimates of PIP/TAZ PPK were obtained from the population of patients with nosocomial infections. The influence of CLcr and body weight were identified as important covariates for PIP/TAZ CL and V. Compared

PPK/PD of PTZ

Fig. 5 PTA vs. MIC for various PIP dosage regimens at steady state for fT[MIC target of C50 %. 30-min infusion of 4 g ); every 8 h, 12 g/d ( 30-min infusion of 4 g every ); 30-min 6 h, 16 g/d ( infusion of 3 g every 4 h, 18 g/d ); 3-h infusion of 4 g ( ); 3-h every 8 h, 12 g/d ( infusion of 4 g every 6 h, 16 g/d ); 5-h infusion of 6 g ( ) every 8 h, 18 g/d (

Probability of target attainment% (fT[MIC C 50 %) E. coli

K. pneumoniae

P. aeruginosa

A.baumannii

4 every 6 h, TIT

58

58

31

6

2 every 8 h, PIT

68

69

41

10

2 every 6 h, PIT

88

90

77

67

3 every 8 h, PIT

76

76

53

34

3 every 6 h, PIT

94

97

88

78

4 every 6 h, PIT

97

97

91

82

PTA (fT!MIC≥50%, %)

Fig. 4 PTA vs. four Gramnegative bacteria for various PIP dosage regimens at steady state for fT[MIC target of C 50 %. 30-min infusion 4 ); 3-h every 6 h, 16 g/d ( infusion 2 every 8 h, 6 g/d ); 3-h infusion 2 every ( ); 3-h infusion 6 h, 8 g/d ( ); 3-h 3 every 8 h, 9 g/d ( infusion 3 every 6 h, 12 g/d ); 3-h infusion 4 every ( ) 6 h, 16 g/d (

Antibiotic regimen

PTA (%)

Table 6 Probability of target attainment (fT[MIC C 50 %) with PIP at different dosage and infusion time for treatment of four Gram-negative bacteria

MIC (mg/L)

with intermittent infusion, the use of continuous infusion may be a more efficient means to maximize the time above MIC and to improve efficacy. In addition, this report has shown that the use of patient data during Monte Carlo simulation is predictive of target attainment in varying patient populations. Conflict of interest of interest.

The authors declare that they have no conflict

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Tazobactam in Patients with Nosocomial Infections.

The study was to establish a population pharmacokinetic (PPK) model of piperacillin (PIP) and tazobactam (TAZ) that explain pharmacokinetic variabilit...
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