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Antimicrob Agents Chemother. 2010 January; 54(1): 460–465.
Published online 2009 October 26. doi:  10.1128/AAC.00296-09
PMCID: PMC2798531

Identification of Optimal Renal Dosage Adjustments for Traditional and Extended-Infusion Piperacillin-Tazobactam Dosing Regimens in Hospitalized Patients [down-pointing small open triangle]

Abstract

This study examined the effect of various levels of renal impairment on the probability of achieving free drug concentrations that exceed the MIC for 50% of the dosing interval (50% fT > MIC) for traditional and extended-infusion piperacillin-tazobactam (TZP) dosing strategies. It also identified optimal renal dosage adjustments for traditional and extended-infusion dosing schemes that yielded probability of target attainment (PTA) and exposure profiles that were isometric to those of the parent regimens. Data from 105 patients were analyzed using the population pharmacokinetic modeling program BigNPAG. To assess the effect of creatinine clearance (CLCR) on overall clearance, TZP clearance was made proportional to the estimated CLCR. A Monte Carlo simulation (9,999 subjects) was performed for the traditional dosing scheme (4.5 g infused during 30 min every 6 h) and the extended-infusion TZP dosing scheme (3.375 g infused during 4 h every 8 h). The fraction of simulated subjects who achieved 50% fT > MIC was calculated for the range of piperacillin MICs from 0.25 to 32 mg/liter and stratified by CLCR. The traditional TZP regimen displayed the greatest variability in PTA across MIC values, especially for MIC values exceeding 4 mg/liter, when stratified by CLCR. In contrast, the PTA for the extended-infusion TZP regimen exceeded ≥80% for MIC values of ≤8 mg/liter across all CLCR strata. All regimens were associated with suboptimal PTA for MIC values of ≥32 mg/liter irrespective of the CLCR. The CLCR adjustments for traditional and extended-infusion TZP dosing regimens should be considered at a CLCR of ≤20 ml/min.

Piperacillin-tazobactam (TZP), a combination product of a semisynthetic penicillin and a beta-lactamase inhibitor, exhibits broad-spectrum activity and low toxicity, and it is indicated for a variety of clinical infections (1). TZP is excreted primarily from the body via the kidney, with the majority (~70%) being eliminated as unchanged drug in the urine (1). Because TZP is eliminated primarily by the kidneys, dose alterations are required for patients with renal impairment.

Despite 15 years of clinical experience, the effect of renal impairment on the pharmacodynamic profile of TZP has not been well evaluated. It is well known that beta-lactam drugs such as TZP exert bactericidal activity in a time-dependent manner, with the time the free drug concentrations exceed the MIC during the dosing interval (fT > MIC) being the key pharmacodynamic parameter (4, 6, 7, 11, 12, 22, 27, 29). For beta-lactams like TZP, it appears that bactericidal activity is optimized when fT > MIC exceeds 50% of the dosing interval (designated 50% fT > MIC) (8, 10). Among the studies that have characterized the ability of various TZP dosing strategies to achieve 50% fT > MIC (13, 17, 24), all have reported the overall probability of target attainment (PTA); we are unaware of any previous study that stratified PTA by renal function. Additionally, no study has assessed the effect of renal dose adjustments on the ability to achieve the desired pharmacodynamic target. An understanding of the effects of renal impairment and renal dose adjustment schemes on the pharmacodynamic profile are clinically important, because the majority of patients receiving TZP have some degree of renal impairment and often are administered a TZP regimen that is adjusted accordingly. While effectiveness is often the primary interest, minimizing TZP accumulation or excessive exposure also is of great importance. In the presence of renal dysfunction, TZP accumulation may occur and result in unnecessary toxicity. Quantifying the degree of TZP accumulation that results from diminished renal function will assist in identifying the creatinine clearance (CLCR) breakpoint and dose adjustment that would leave the PTA substantially unaltered and still not result in profound accumulation.

This study had two specific aims. First, we examined the effect of various levels of renal impairment on the probability of achieving 50% fT > MIC for traditional and extended-infusion TZP dosing strategies. Second, we sought to identify renal dosage adjustments for traditional and extended-infusion dosing schemes that yielded PTA and exposure profiles for the TZP renal dosing strategies that were isometric to parent regimens. For the purpose of the analysis, we characterized only the pharmacodynamic profile of piperacillin. We did not examine the pharmacodynamic profile of tazobactam, because current doses of tazobactam in the TZP formulation have been shown to be sufficient for an antibacterial effect when the target is attaining a free-drug concentration exceeding the MIC for 50% of the dosing interval (26).

MATERIALS AND METHODS

Patient population.

Pharmacokinetic (PK) data from 105 hospitalized patients (963 samples in total) were obtained from two sources. First, plasma concentration-time data were obtained from four open-label studies evaluating the pharmacokinetics of TZP (two single-dose studies and two multiple-dose studies). These studies included hospitalized patients with clinical indications for TZP therapy (19). Among the 139 patients from these studies, complete PK data with creatinine clearance (CLCR) information were available for 93 patients (873 plasma drug concentrations). The CLCR values for all subjects were estimated by the Cockcroft-Gault method (5).

The second source of pharmacokinetic data was obtained from 12 patients enrolled in a prospective PK study that was conducted at the Albany Medical Center Hospital, a 651-bed teaching hospital in Albany, NY. This study included patients hospitalized for >48 h and receiving 3.375 g TZP intravenously (i.v.) every 8 h during a 4-h infusion period. In total, 90 plasma drug samples were assessed for these 12 patients (20). All samples for both sets of PK data were analyzed using high-pressure liquid chromatography (HPLC) (14, 15).

Population pharmacokinetic modeling methods for hospitalized patients.

All data were analyzed in a population pharmacokinetic model using the Big Non-Parametric Adaptive Grid with adaptive γ (BigNPAG) program of Leary et al. (18). The pharmacokinetic model was parameterized as a standard two-compartment model with zero-order infusion. Elimination from the central compartment and all intercompartmental distribution processes were modeled as first-order processes. To assess the impact of renal function on overall clearance, piperacillin clearance was made proportional to the estimated CLCR as follows: (CLslope × estimated CLCR) + CLint), where CLslope is the clearance slope and CLint is the clearance intercept.

The inverse of the estimated assay variance was used as the first estimate for weighting in the PK model. Weighting assumed that total observation variance was proportional to assay variance, which was determined on a between-day basis. The analysis was performed with adaptive γ, a scalar that multiplies the polynomial described above and that is optimized with each cycle to produce the best approximation to the homoscedastic assumption. Adaptive γ was fixed at one for the analysis.

Upon attaining convergence, Bayesian estimates for each patient were obtained by using the population-of-one utility in the BigNPAG program. The mean and median values were used as measures of the central tendency of the population parameter estimates and were evaluated in the Bayesian analysis. Scatter plots were examined for individual patients and for the population as a whole. Goodness of fit was assessed by regression with an observed-predicted plot, coefficients of determination, and log-likelihood values. Predictive performance evaluation was based on weighted mean errors and the bias-adjusted weighted mean squared errors.

Model validation.

As an additional evaluation of the predictive performance of the PK model, a validation PK model was developed to assess the validity of the clearance estimate. We assessed the validity of the clearance estimate because clearance is the major determinant of fT > MIC in the model. For the validation model, 80% of subjects included in the overall model were randomly selected, and their TZP serum concentration data were modeled using BigNPAG as described above. This validation model was used to predict the TZP clearance for the remaining 20% of subjects withheld from the validation model. The overall clearance for each of the remaining subjects was predicted using the median parameter estimates for CLINT and CLslope and their individual CLCR values (clearance = CLslope × estimated CLCR + CLINT). These clearance estimates served as the predicted clearance estimates and were compared to the median observed maximum a posteriori (MAP) Bayesian estimate of clearance for each patient in the overall model. Goodness of fit was assessed by regression with an observed-predicted plot, and predictive performance evaluation was based on bias and precision. Bias was calculated as the mean percent error: [(observed clearance − predicted clearance)/observed clearance]. Precision was calculated as the mean percent squared error: [(observed clearance − predicted clearance)2/observed clearance].

MCS. (i) Probability of target attainment stratified by renal function.

Because of its predictive performance in the Bayesian goodness-of-fit assessment, the median parameter vector and covariance matrix from the population PK models were embedded in the PRIOR subroutine of the ADAPT II package of the programs of D'Argenio and Schumitzky (ADAPT II User's Guide, Pharmacokinetics/Pharmacodynamics System Analysis Software; Biomedical Simulations Resource, Los Angeles, CA) (9). The population simulation without the process noise option was used. A 9,999-subject Monte Carlo simulation (MCS) was performed using the median parameter estimates and covariance matrix for the nosocomial pneumonia FDA-approved dosing scheme (4.5 g infused during 30 min every 6 h) and the extended-infusion TZP dosing scheme used at the Albany Medical Center (3.375 g infused during 4 h every 8 h). Since the primary aim of the study was to examine the PTA across various levels of renal function, the estimated CLCR was fixed at values of 20, 40, 60, 80, 100, and 120 ml/min.

Concentration-time profiles for each of these dosing regimens were simulated for 24 h, and these data were used for the PTA analyses. Since early appropriate therapy has been identified as a major determinant of outcomes in patients with serious infections (16), we believe it is more important to simulate at concentration-time profiles at hour 24 rather than at true steady state. The piperacillin PK data were adjusted for 30% protein binding to reflect unbound drug concentrations in the data analysis, and free drug was used for all calculations. For each regimen, the fraction of simulated subjects who achieved 50% fT > MIC was calculated for the range of piperacillin MICs (in the presence of tazobactam) from 0.25 to 32 mg/liter and was stratified by CLCR. Both normal and log-normal distributions were evaluated. These were discriminated on the ability to recreate the original median parameter values and corresponding standard deviations from the population analysis. Systat for Windows (version 10.2) was used for all data transformations, and STATA version 9.2 (College Station, TX) was used for graphing functions.

(ii) Renal dosage adjustments.

For our second aim, we determined the optimal renal dosage adjustments for traditional TZP dosing (TZP 4.5 g i.v. every 6 h as a 30-min infusion) and extended-infusion TZP dosing (TZP 3.375 g i.v. every 8 h as a 4-h infusion). For traditional TZP dosing, a 9,999 MCS (ADAPT II) was performed for the current FDA-approved renal dose adjustment regimen for TZP 4.5 g every 6 h as a 30-min infusion (3.375 g TZP every 6 h [30 min infusion] at a CLCR of 40 ml/min) (1). We also evaluated 3.375 g TZP every 6 h (30 min infusion) at a CLCR of 20 ml/min as an alternative renal dose adjustment scheme. For extended-infusion TZP dosing with a base regimen of 3.375 g every 8 h as a 4-h infusion, a 9,999 MCS (ADAPT II) was performed for the following renal dose adjustment regimens: 3.375 g i.v. every 12 h (4-h infusion) at a CLCR of 40 ml/min and 3.375 g i.v. every 12 h (4-h infusion) at a CLCR of 20 ml/min.

For each of these renal dose adjustment regimens, the Monte Carlo simulation data were used to determine the probability of achieving 50% fT > MIC for MICs from 0.25 to 32 mg/liter at the candidate renal dose adjustment CLCR thresholds. The PTA (50% fT > MIC) were examined at the candidate CLCR dose adjustment thresholds, because these CLCR levels provide the most conservative estimates of PTA for each of the potential renal dose adjustment regimens. To determine the degree of TZP accumulation, we calculated the ratio of the distribution of the area under the concentration-time curves at steady state (AUC24SS) of the parent regimen at the CLCR threshold where renal dosage adjustment would occur (e.g., CLCR ≤ 40 ml/min or CLCR ≤ 20 ml/min) relative to the AUC24SS of the parent regimen at a CLCR of 100 ml/min. We examined the AUC24SS of the parent regimen at the CLCR dose adjustment threshold, because this is the point of maximal exposure for a given TZP renal dosing scheme. Similarly to the stratified renal function PTA analyses, concentration-time profiles for each of these dosing regimens were simulated for 24 h.

RESULTS

Demographic features of the 105 patients who provided plasma samples are displayed in Table Table1.1. Among the 105 patients, 63 (60%) were male. The mean (standard deviations) age of the patients was 53.2 (16.4) years. The mean (standard deviation) CLCR of the population was 91.6 (33.6) ml/min. Only three patients had CLCR values of less than 40 ml/min.

TABLE 1.
Demographic features of modeled subjects receiving TZPa

Population pharmacokinetic analysis. (i) Overall model.

The mean and median population parameter estimates and associated dispersions identified by BigNPAG are provided in Table Table2.2. Prior to the Bayesian analysis, the median and mean best-fit regression lines were 0.87 × predicted + 6.7 and 1.0 × predicted + 3.3, respectively. The R2 values for the median and mean were 0.78 and 0.77, respectively. The predictive performance measures of the mean and median estimates of central tendencies for the population model after the Bayesian step are displayed in Table Table3.3. The model fit was highly acceptable for both the mean and the median. Because of the slightly higher R2, lower bias-adjusted mean weighted square error, and smaller intercept term in the observed-predicted plot, the median parameter estimates were selected for the Monte Carlo simulation analyses.

TABLE 2.
Evaluation of the predictive performance of piperacillin population modelsa
TABLE 3.
Goodness of fit and predictive performances of TZP population models (serum)

(ii) Validation model.

The mean and median population parameter estimates and associated dispersions identified by BigNPAG are provided in Table Table2,2, and the predictive performance measures of the mean and median estimates of central tendencies for the validation model after the Bayesian step are displayed in Table Table3.3. The parameter estimates and predictive performance of the validation model were highly consistent with the overall PK model. The clearance observed versus predicted plot for the 20% of patients withheld from the validation model are displayed in Fig. Fig.1.1. Overall, the observed versus predicted plot was highly acceptable, and its predicted performance is consistent with a previously published population PK model validation (23). The best-fit regression line was observed = 1.014 × predicted + 0.032, and the R2 was 0.493. The mean bias was −0.16, and the mean precision was 1.64.

FIG. 1.
Plot of the predicted median clearance estimates from the validation model versus the observed MAP Bayesian estimate of clearance from the overall model for the 21 patients withheld from the validation model.

(iii) Monte Carlo simulation of hospitalized patients stratified by renal function.

The results of PTA analyses stratified by CLCR are displayed in Table Table4.4. The FDA-approved regimen (4.5 g i.v. every 6 h via 30-min infusion) displayed the greatest variability in PTA across MICs. The PTA was ≥80% for MICs of ≤2 mg/liter in the intermittent infusion regimens at all levels of CLCR. However, the PTA fluctuated considerably for MICs exceeding 4 mg/liter when stratified by CLCR. In contrast, the PTA for the extended-infusion TZP regimen exceeded ≥95% for MICs of ≤8 mg/liter. Both TZP regimens were associated with suboptimal PTA for MICs of ≥32 mg/liter, irrespective of the CLCR.

TABLE 4.
PTA of various TZP dosing regimens stratified by CLCR and MICa

Renal dosage adjustment analysis. (i) FDA nosocomial pneumonia regimen (4.5 g TZP i.v. every 6 h as a 30-min infusion).

The results of the PTA analyses for the renal dose adjustment regimens are displayed in Table Table5.5. At a CLCR of 100 ml/min, the PTA for 4.5 g TZP every 6 h (30-min infusion) sharply decreased (<80%) after MICs exceeded 4 mg/liter for 4.5 g every 6 h via 30-min infusion. The dosage adjustment of the parent regimen to 3.375 g every 6 h as a 30-min infusion at CLCR of 40 and 20 ml/min both resulted in an increased PTA relative to that of the parent regimen among patients with a CLCR of 100 ml/min. The candidate renal dose adjustment regimens had similar PTA for MICs of ≤8 mg/liter, but higher PTA were noted for the candidate regimen renal dose adjusted at a CLCR of 20 ml/min for MICs of ≥16 mg/liter (Table (Table55).

TABLE 5.
PTA for parent regimen and dose-adjusted regimens

Figure Figure2A2A shows the distribution of exposure ratios (AUC24SS fixed at a CLCR of 40 ml/min relative to the AUC24SS distribution fixed at a CLCR of 100 ml/min) and demonstrates that all subjects had an exposure ratio of less than a factor of 3. In contrast, waiting to adjust the dose until the CLCR attained 20 ml/min resulted in some subjects experiencing an exposure ratio of greater than 4, although the majority of subjects had an exposure ratio of less than 3 (Fig. (Fig.2B2B).

FIG. 2.
Distribution of AUC24SS exposure ratios for various TZP dosing regimens. (A) Distribution of the AUC24SS exposure ratio of 4.5 g every 6 h (30-min infusion) at a CLCR of 40 ml/min relative to 4.5 g every 6 h (30-min infusion) at a CLCR of 100 ml/min. ...

(ii) Extended-infusion regimen (3.375 g TZP i.v. every 8 h as a 4-h infusion).

The results of target attainment analyses for the Monte Carlo simulations are displayed in Table Table5.5. The PTA exceeded 90% for a TZP regimen of 3.375 g i.v. every 8 h as a 4-h infusion at a CLCR of 100 ml/min for MICs of <16 mg/liter. When the interval was lengthened from every 8 h to every 12 h at a CLCR of 40 ml/min, a substantial reduction in PTA was observed for MICs of ≥8 mg/liter. In contrast, the PTA profile for 3.375 g i.v. every 12 h at a CLCR of 20 ml/min was identical to the PTA profile for 3.375 g i.v. every 8 h as a 4-h infusion at a CLCR of 100 ml/min.

Figure Figure2C2C shows the distribution of exposure ratios (AUC24SS fixed at a CLCR of 40 ml/min relative to the AUC24SS distribution fixed at a CLCR of 100 ml/min) and demonstrates that all subjects had an exposure ratio of less than a factor of 3. In contrast, waiting to adjust the dose until the CLCR attained 20 ml/min resulted in some subjects experiencing an exposure ratio of greater than 4, although the majority of subjects had an exposure ratio of less than 3 (Fig. (Fig.2D2D).

DISCUSSION

Historically, most pharmacodynamic profiling studies have examined the overall PTA without regard to a patient's renal function. It is well known that the majority of beta-lactam antibiotics are eliminated primarily by the kidneys (3). One would anticipate that the PTA varies as a function of the estimated CLCR. The purpose of our analysis was to evaluate the effect of various CLCR estimates on the observed PTA for the FDA-approved TZP nosocomial pneumonia dosing regimen (4.5 g TZP i.v. as a 30-min infusion every 6 h), as well as the extended-infusion TZP dosing strategy (3.375 g TZP i.v. as a 4-h infusion every 8 h) adopted into practice at the Albany Medical Center Hospital. To the best of our knowledge, our analysis is the first to examine the effect of various levels of renal function on the PTA for traditional and extended-infusion TZP dosing strategies among hospitalized patients.

An inverse relationship was observed between PTA and CLCR for both regimens. The extended-infusion TZP regimen was pharmacodynamically superior to the intermittent-infusion TZP regimen. This finding is consistent with previous evaluations (13, 19). Both TZP regimens were associated with suboptimal PTA for MICs of ≥32 mg/liter irrespective of the CLCR. These findings may partially explain the results observed in the recent study by Tam et al., which examined 30-day mortality rates among patients with Pseudomonas aeruginosa bloodstream infections stratified by the TZP MIC (28). In this study, patients with a P. aeruginosa bloodstream infection for which the TZP MIC was 32 or 64 mg/liter who were empirically treated with intermittent-infusion TZP had a 30-day mortality rate in excess of 80%. In contrast, 30-day mortality was 33% if the TZP MIC for the P. aeruginosa bloodstream isolate was ≤16 mg/liter when empirically treated with intermittent-infusion TZP. Interestingly, treating patients with an alternative (non-TZP) anti-pseudomonal beta-lactam antibiotic for P. aeruginosa bloodstream infections with a TZP MIC of 32 or 64 mg/liter resulted in a 30-day mortality of 22%. These results and data from previous TZP PTA analyses (13, 19) support the lowering of the CLSI and FDA susceptibility breakpoint for TZP against Pseudomonas aeruginosa and other non-Enterobacteriaceae Gram-negative bacteria from ≥64 to ≥16 mg/liter.

The second aim was to determine the optimal renal dosage adjustments for the FDA-approved nosocomial pneumonia TZP dosing regimen and the extended-infusion TZP dosing regimen used at the Albany Medical Center Hospital. The goal was to identify the CLCR breakpoint and dose adjustment for each of the regimens that would leave the PTA substantially unaltered and avoid causing profound accumulation. For extended-infusion TZP, the results support dose adjusting at a CLCR of 20 ml/min. Extending the interval from every 8 h to every 12 h at a CLCR of 40 ml/min resulted in a suboptimal PTA profile, especially for MICs of ≥8 mg/liter (Table (Table5).5). In contrast, dose adjustment at a CLCR of 20 ml/min did not substantially alter the PTA profile (Table (Table5)5) and did not result in profound accumulation (Fig. (Fig.2D2D).

For the FDA-approved nosocomial pneumonia TZP dosing regimen (4.5 g TZP i.v. as a 30-min infusion every 6 h), a case can be made for either candidate renal dose adjustment regimen (3.375 g TZP i.v. for 30 min every 6 h at a CLCR of 40 ml/min or 3.375 g TZP i.v. for 30 min every 6 h at a CLCR of 20 ml/min). Both candidate renal dose adjustment schemes had an improved PTA profile compared to that of the parent regimen at a CLCR of 100 ml/min (Table (Table5).5). However, the candidate regimen dose adjusted at a CLCR of 20 ml/min had a more robust PTA for MICs of ≥16 mg/liter. The risk associated with waiting to dose adjust at a CLCR of 20 ml/min was more accumulation: some subjects experienced an exposure ratio of greater than 4, although the majority of subjects had an exposure ratio of less than 3 (Fig. (Fig.2D).2D). Given the wide safety window and lack of a clearly defined exposure-toxicity threshold, we recommend the candidate regimen dose adjusted at a CLCR of 20 ml/min, especially when empirically or definitively treating serious Gram-negative infections with potentially higher MICs.

Several things should be noted when interpreting these results. First, this analysis included only three patients with CLCR values of less than 40 ml/min. Since we previously demonstrated that piperacillin clearance in the presence of tazobactam is linear, we do not believe this to be a major issue (19). However, there are data that suggest that the clearance of piperacillin is nonlinear in patients with renal dysfunction, and it may compete with tazobactam for tubular secretion (2, 14, 25). While our model fits the data well, future piperacillin PK models should target patients with CLCR values of less than 40 ml/min to test the validity of our PTA estimates. Until this is performed, the PTA and exposure ratios reported in this study should be viewed as conservative estimates.

Second, the Kpc/Kcp ratios (Kcp, transfer rate constant from the central compartment to the peripheral compartment; Kpc, transfer rate constant from the peripheral compartment to the central compartment) observed in this model are twofold higher than those of our previous publication, but we did not find this troublesome and the results were within reason. While similar, these were two distinct analyses. Clearance was modeled differently in this analysis, and this may contribute to the different ratios. Additionally, we included only 67% of the patients from our original publication in this analysis and added 12 patients with robust sampling. Collectively, we believe these differences most likely contribute to the observed differences in Kpc/Kcp ratios between publications.

In summary, we examined the effect of various levels of renal function on the PTA profile for traditional and extended-infusion TZP dosing strategies among hospitalized patients and found an inverse relationship between renal function and PTA. We used these data to determine the optimal candidate renal dose adjustment regimens for both the traditional and extended-infusion dosing schemes. For both regimens, the optimal candidate renal dose adjustment threshold was CLCR ≤ 20 ml/min. While the FDA-approved nosocomial pneumonia dosing regimen recommendation is to dose adjust at a CLCR of 40 ml/min (1), we support our dose adjustment recommendation at the lower CLCR of 20 ml/min due to an improved PTA profile and the lack of a clearly defined exposure-toxicity relationship. Consistently with previous PTA studies (13, 17, 21, 24), extending the infusion time greatly improved the pharmacodynamic profile of TZP, especially for MICs of ≥8 mg/liter. However, the PTA was suboptimal at 32 mg/liter for all intermittent and extended-infusion regimens evaluated. When the TZP MIC is 32 mg/liter, alternative agents should be considered. Because our findings are based on mathematical models, they should be validated in the clinical arena.

Acknowledgments

This study was made possible in part by an American College of Clinical Pharmacy Infectious Diseases Minisabbatical Award.

This article has greatly benefited from the thoughtful editing of Allison Krug.

Footnotes

[down-pointing small open triangle]Published ahead of print on 26 October 2009.

REFERENCES

1. Anonymous. 2009. Zosyn—full prescribing information. Wyeth Pharmaceuticals Inc., Philadelphia, PA.
2. Aronoff, G. R., R. S. Sloan, M. E. Brier, and F. C. Luft. 1983. The effect of piperacillin dose on elimination kinetics in renal impairment. Eur. J. Clin. Pharmacol. 24:543-547. [PubMed]
3. Barza, M., and L. Weinstein. 1976. Pharmacokinetics of the penicillins in man. Clin. Pharmacokinet. 1:297-308. [PubMed]
4. Cars, O. 1997. Efficacy of beta-lactam antibiotics: integration of pharmacokinetics and pharmacodynamics. Diagn. Microbiol. Infect. Dis. 27:29-33. [PubMed]
5. Cockcroft, D. W., and M. H. Gault. 1976. Prediction of creatinine clearance from serum creatinine. Nephron 16:31-41. [PubMed]
6. Craig, W. 1993. Pharmacodynamics of antimicrobial agents as a basis for determining dosage regimens. Eur. J. Clin. Microbiol. Infect. Dis. 12(Suppl. 1):S6-S8. [PubMed]
7. Craig, W. A. 1995. Interrelationship between pharmacokinetics and pharmacodynamics in determining dosage regimens for broad-spectrum cephalosporins. Diagn. Microbiol. Infect. Dis. 22:89-96. [PubMed]
8. Craig, W. A. 1998. Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin. Infect. Dis. 26:1-12. [PubMed]
9. D'Argenio, D. Z., and A. Schumitzky. 1979. A program package for simulation and parameter estimation in pharmacokinetic systems. Comput. Programs Biomed. 9:115-134. [PubMed]
10. Drusano, G. L. 2004. Antimicrobial pharmacodynamics: critical interactions of ‘bug and drug’. Nat. Rev. Microbiol. 2:289-300. [PubMed]
11. Hanberger, H. 1992. Pharmacodynamic effects of antibiotics. Studies on bacterial morphology, initial killing, postantibiotic effect and effective regrowth time. Scand. J. Infect. Dis. Suppl. 81:1-52. [PubMed]
12. Hyatt, J. M., P. S. McKinnon, G. S. Zimmer, and J. J. Schentag. 1995. The importance of pharmacokinetic/pharmacodynamic surrogate markers to outcome. Focus on antibacterial agents. Clin. Pharmacokinet. 28:143-160. [PubMed]
13. Kim, A., C. A. Sutherland, J. L. Kuti, and D. P. Nicolau. 2007. Optimal dosing of piperacillin-tazobactam for the treatment of Pseudomonas aeruginosa infections: prolonged or continuous infusion? Pharmacotherapy 27:1490-1497. [PubMed]
14. Kim, M. K., B. Capitano, H. M. Mattoes, D. Xuan, R. Quintiliani, C. H. Nightingale, and D. P. Nicolau. 2002. Pharmacokinetic and pharmacodynamic evaluation of two dosing regimens for piperacillin-tazobactam. Pharmacotherapy 22:569-577. [PubMed]
15. Kinzig, M., F. Sorgel, B. Brismar, and C. E. Nord. 1992. Pharmacokinetics and tissue penetration of tazobactam and piperacillin in patients undergoing colorectal surgery. Antimicrob. Agents Chemother. 36:1997-2004. [PMC free article] [PubMed]
16. Kollef, M. H., G. Sherman, S. Ward, and V. J. Fraser. 1999. Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest 115:462-474. [PubMed]
17. Kuti, J. L., C. H. Nightingale, R. Quintiliani, and D. P. Nicolau. 2002. Pharmacodynamic profiling of continuously infused piperacillin/tazobactam against Pseudomonas aeruginosa using Monte Carlo analysis. Diagn. Microbiol. Infect. Dis. 44:51-57. [PubMed]
18. Leary, R. J., A. Schumitzky, and M. van Guilder. 2001. An adaptive grid, non-parametric approach to pharmacokinetic and dynamic (PK/PD) models, p. 389-394. In Proceedings, Fourteenth IEEE Symposium on Computer-Based Medical Systems. IEEE Computer Society, Bethesda, MD.
19. Lodise, T. P., Jr., B. Lomaestro, K. A. Rodvold, L. H. Danziger, and G. L. Drusano. 2004. Pharmacodynamic profiling of piperacillin in the presence of tazobactam in patients through the use of population pharmacokinetic models and Monte Carlo simulation. Antimicrob. Agents Chemother. 48:4718-4724. [PMC free article] [PubMed]
20. Lodise, T. P., B. Lomaestro, A. Kwa, and G. L. Drusano. 2007. Pharmacodynamics (PD) of extended infusion piperacillin/tazobactam (EI-PIP) against Pseudomonas aeruginosa, abstr. 449. Abstr. 45th Annu. Meet. Infect. Dis. Soc. Am. Infectious Disease Society of America, San Diego, CA.
21. Lodise, T. P., B. M. Lomaestro, and G. L. Drusano. 2006. Application of antimicrobial pharmacodynamic concepts into clinical practice: focus on beta-lactam antibiotics: insights from the Society of Infectious Diseases Pharmacists. Pharmacotherapy 26:1320-1332. [PubMed]
22. Masuda, G., S. Tomioka, H. Uchida, and M. Hasegawa. 1977. Bacteriostatic and bactericidal activities of selected beta-lactam antibiotics studied on agar plates. Antimicrob. Agents Chemother. 11:376-382. [PMC free article] [PubMed]
23. Preston, S. L., G. L. Drusano, A. L. Berman, C. L. Fowler, A. T. Chow, B. Dornseif, V. Reichl, J. Natarajan, F. A. Wong, and M. Corrado. 1998. Levofloxacin population pharmacokinetics and creation of a demographic model for prediction of individual drug clearance in patients with serious community-acquired infection. Antimicrob. Agents Chemother. 42:1098-1104. [PMC free article] [PubMed]
24. Reese, A. M., C. R. Frei, and D. S. Burgess. 2005. Pharmacodynamics of intermittent and continuous infusion piperacillin/tazobactam and cefepime against extended-spectrum beta-lactamase-producing organisms. Int. J. Antimicrob. Agents 26:114-119. [PubMed]
25. Sörgel, F., and M. Kinzig. 1994. Pharmacokinetic characteristics of piperacillin/tazobactam. Intensive Care Med. 20(Suppl. 3):S14-S20. [PubMed]
26. Strayer, A. H., D. H. Gilbert, P. Pivarnik, A. A. Medeiros, S. H. Zinner, and M. N. Dudley. 1994. Pharmacodynamics of piperacillin alone and in combination with tazobactam against piperacillin-resistant and -susceptible organisms in an in vitro model of infection. Antimicrob. Agents Chemother. 38:2351-2356. [PMC free article] [PubMed]
27. Sullivan, M. C., C. H. Nightingale, R. Quintiliani, and K. Sweeney. 1993. Comparison of the pharmacokinetic and pharmacodynamic activity of piperacillin and mezlocillin. Pharmacotherapy 13:607-612. [PubMed]
28. Tam, V. H., E. A. Gamez, J. S. Weston, L. N. Gerard, M. T. Larocco, J. P. Caeiro, L. O. Gentry, and K. W. Garey. 2008. Outcomes of bacteremia due to Pseudomonas aeruginosa with reduced susceptibility to piperacillin-tazobactam: implications on the appropriateness of the resistance breakpoint. Clin. Infect. Dis. 46:862-867. [PubMed]
29. Van der Auwera, P., V. Duchateau, C. Lambert, M. Husson, M. Kinzig, and F. Sorgel. 1993. Ex vivo pharmacodynamic study of piperacillin alone and in combination with tazobactam, compared with ticarcillin plus clavulanic acid. Antimicrob. Agents Chemother. 37:1860-1868. [PMC free article] [PubMed]

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