MAJOR ARTICLE Relationship between Fluoroquinolone Area under the Curve:Minimum Inhibitory Concentration Ratio and the Probability of Eradication of the Infecting Pathogen, in Patients with Nosocomial Pneumonia George L. Drusano,1 Sandra L. Preston,1 Cynthia Fowler,2 Michael Corrado,3 Barbara Weisinger,4 and James Kahn4 1 Ordway Research Institute, Albany, New York; 2Robert Wood Johnson Pharmaceutical Research and Development and 3Advanced Biologics, Lambertville, and 4Ortho-McNeil Pharmaceutical, Raritan, New Jersey Our objective was to prospectively determine the factors influencing the probability of a good microbiological or clinical outcome in patients with nosocomial pneumonia treated with a fluoroquinolone. Levofloxacin was administered as an infusion of 500 mg/h for 1.5 h (total dose, 750 mg). For patients with Pseudomonas aeruginosa or methicillin-resistant Staphylococcus aureus, a second drug was added (ceftazidime or piperacillin/ tazobactam for P. aeruginosa and vancomycin for methicillin-resistant S. aureus). Population pharmacokinetic studies of 58 patients demonstrated that this population handled the drug differently from populations of volunteers. Multivariate logistic regression analysis (n p 47 patients) demonstrated that only the age of the patient and the achievement of an area under the curve:minimum inhibitory concentration ratio of ⭓87 had a significant effect on eradication of the pathogen (P ! .001 ). Achieving the breakpoint made the patient 4 times more likely to achieve eradication. The effect was greatest in patients ⭓67 years old. Nosocomial pneumonia remains a major cause of mortality and morbidity. Because of increasing bacterial resistance, there is a need for potent therapeutic alternatives. Furthermore, because many of these patients are in intensive care units (ICUs), the possibility of drug toxicity is an important consideration. For example, although aminoglycosides are potent agents and are relatively inexpensive, there is still a risk of nephrotoxicity attendant to their use, even when administered once daily [1]. Because the usual duration of therapy Received 28 May 2003; accepted 7 November 2003; electronically published 16 April 2004. Potential conflicts of interest: C.F., B.W., and J.K. are employees of Johnson and Johnson. Ortho-McNeil Pharmaceutical is a wholly owned subsidiary of Johnson and Johnson. Financial support: Ortho-McNeil Pharmaceutical. Reprints or correspondence: Dr. G. L. Drusano, Ordway Research Institute, New York State Department of Health, 150 New Scotland Ave., Albany, NY 12208 ([email protected]). The Journal of Infectious Diseases 2004; 189:1590–7 2004 by the Infectious Diseases Society of America. All rights reserved. 0022-1899/2004/18909-0006$15.00 1590 • JID 2004:189 (1 May) • Drusano et al. is in the range of 10–14 days, the risks of aminoglycoside-related nephrotoxicity are magnified. Such loss of organ function in the ICU setting also increases the probability of mortality [2]. Consequently, many intensive care physicians prefer not to use aminoglycosides, even as adjunctive agents, particularly in patients with high APACHE II scores. Levofloxacin is a fluoroquinolone antimicrobial with a broad spectrum of activity that encompasses the pathogens most frequently seen in the setting of nosocomial pneumonia [3]. Also of major importance, this fluoroquinolone has an impressive safety record developed over the course of 10 years, with many millions of prescriptions in use worldwide. Previous in vitro data, animal model data, and clinical trial information indicate that the area under the curve (AUC):MIC ratio is the best measure of drug exposure to use when attempting to link exposure to response in patients being treated with fluoroquinolone antimicrobials [4–7]. Consequently, this is the measure of drug exposure relative to MIC that, in the present analysis, we have attempted to link to the probability of successful therapy. PATIENTS AND METHODS Patients. Patients in this analysis were recruited from a randomized trial of levofloxacin (750 mg intravenous [iv] once daily) versus imipenem/cilastatin (500 mg–1.0 g iv every 6–8 h) [8]. In this trial, patients infected with Pseudomonas aeruginosa had a second drug added (ceftazidime or piperacillin/ tazobactam for patients randomized to receive levofloxacin and amikacin for patients randomized to receive imipenem), and patients infected with methicillin-resistant Staphylococcus aureus had vancomycin added. The results of the randomized trial have been reported elsewhere [8]. The inclusion and exclusion criteria have been reported in that publication. There were 58 patients with interpretable plasma concentrations who were receiving levofloxacin. Of these patients, 47 had plasma concentration–time data, an identified pathogen with a levofloxacin MIC, and a microbiological outcome. A number of patients had an identified pathogen without a levofloxacin MIC. MICs. MICs from recovered pathogens were determined for levofloxacin by use of standard NCCLS microtiter MIC methods [9]. Determination of plasma concentration of levofloxacin. Plasma concentrations of levofloxacin were determined by use of a sensitive and specific high-performance liquid chromatographic assay [10]. The sensitivity of the assay was 0.0864 mg/ L. The assay was linear over the range of 0.0864–10.202 mg/ L. Concentrations exceeding this value were diluted into the linear range. The between-day coefficient of variation of the assay was 9.7% at 0.0864 mg/L and 4.7% at 10.202 mg/L. Stochastic optimal design theory for sampling schedule design. The approach used here has been described elsewhere [11]. In brief, the design of the sampling schedule was determined by performing a D-optimal design calculation (minimization of the determinant of the inverse Fisher Information Matrix) for each of the points in the joint probability density, which were determined by a population pharmacokinetic analysis of the data on plasma concentration of levofloxacin, by use of the nonparametric adaptive grid (NPAG) program [12]. The peaks of system information for each of the sampling times were determined, and the choice of sampling times were made on this basis. Patients had 6 plasma samples obtained over 24 h at steady state (predose, ∼5 min after the end of a 1.5-h drug infusion of 750 mg, and at 2.25, 4.5, 5.5, and 7.5 h of the dosing interval). Population pharmacokinetic analysis. An NPAG program with adaptive g, developed by Leary et al. [12], was used for the present analysis. A 2-compartment open model with a time- limited zero-order iv input and first-order output was used. The base-weighting scheme was determined by use of a polynomial function relating drug concentration to the SD of the observation, using the between-day assay-variability data. This function was scaled by a factor g that was determined iteratively as part of the optimization process. Maximal a posteriori probability (MAP)–Bayesian parameter estimates were determined for each patient in the data set by use of the “population of one” utility within NPAG. Statistical analysis. The outcomes of examined patients included clinical outcome (success vs. failure of treatment) and microbiological outcome (eradication vs. persistence of the pathogen), as described by West et al. [8]. The ability of covariates to influence the probability of success of treatment or eradication of the pathogen was examined by use of logistic regression analysis. The covariates examined were the following: demographic (age, race, sex, height, and weight), physiologic (shock, vasopressor use, respiratory difficulty, PaO2, APACHE II score, intubation status, ICU status, and site of acquisition of pneumonia [ICU vs. not ICU]), microbiologic (MIC breakpoint of pathogen and infection with specific pathogens [P. aeruginosa, Enterobacter species, and S. aureus]), and drug exposure (AUC:MIC ratio breakpoint). Since 17 of 47 patients received a second drug that was active against an infecting pathogen (ceftazidime or piperacillin/tazobactam for P. aeruginosa and vancomycin for methicillin-resistant S. aureus), we created a dichotomous variable for patients receiving “other active drugs” and tested it as a categorical variable to ascertain whether it significantly altered the probability of a good microbiological outcome. For covariates that, when tested univariately, were determined to significantly influence outcome, model building was performed by use of backwards stepwise regression, with entry/exit criteria of 0.15/0.15. For calculation of the AUC:MIC ratio, the highest MIC value was used for patients infected with 11 pathogen. Likewise, for calculation of the MIC breakpoint, the highest MIC value was used for patients in whom multiple pathogens were recovered. After the final model was determined, the addition of the “other active drugs” variable was retested to determine whether it added to the final model. The likelihood ratio test was used to test for model expansion for the final model plus “other active drugs.” The final model was used as the base model. “Other active drugs” was added as a categorical covariate, and the significance of model expansion was determined by comparing 2 times the log-likelihood difference against a x2 distribution with 1 df. Determination of breakpoints. Breakpoint values for age, MIC, and AUC:MIC ratio, for both clinical and microbiological outcome, were determined by use of classification and regression tree (CART) analysis. A receiver operating characteristic (ROC) curve was also constructed for AUC:MIC ratio break- Quinolone Dynamics in Nosocomial Pneumonia • JID 2004:189 (1 May) • 1591 Table 1. Population pharmacokinetic parameter values derived from 58 patients with nosocomial pneumonia who were receiving levofloxacin (750 mg intravenous) as a 1.5-h constant-rate infusion. Unit Vol, L Kcp, h⫺1 Kpc, h⫺1 CL, L/h Mean Median 34.4 23.3 7.65 2.66 6.07 0.924 7.24 6.24 SD 33.5 9.59 12.0 4.36 NOTE. CL, total clearance of levofloxacin; Kcp and Kpc, the first-order intercompartmental transfer rate constants connecting the central and peripheral compartments; Vol, volume of the central compartment. point. All statistical analyses were performed with the program SYSTAT for Windows (version 10.0; SPSS). Monte Carlo simulation and target attainment. A 10,000subject Monte Carlo simulation was performed by use of the simulation module of ADAPT II (Biomedical Simulations Resource). A log-normal distribution was used, since this best recaptured the starting parameter values and their dispersions. Target attainments were determined for the simulated subjects, from MIC values of 0.06–8.0 mg/L. Populations of both P. aeruginosa and Enterobacter cloacae with levofloxacin MICs were provided by The Surveillance Network (Reston, VA). The overall target attainment for different AUC:MIC ratio targets was determined by expectation, as described elsewhere [13, 14]. RESULTS Pharmacokinetic profile of levofloxacin in patients with nosocomial pneumonia. There were 58 patients receiving levofloxacin for whom interpretable plasma concentration–time data were available, including data on 327 plasma specimens (mean, 5.6 specimens/patient). The mean, median, and SD population parameter values are displayed in table 1. The predicted versus observed plot, after the MAP-Bayesian step, demonstrated the following relationship: observed p 0.997 ⫻ predicted + 0.271 (r2, 0.948; P ! .001). Measures of bias and precision were acceptable: bias (mean weighted error) was ⫺0.245 mg/L; precision (biasadjusted mean weighted squared error) was 1.126 (mg/L)2. After the MAP-Bayesian step, the estimated clearances were used to calculate the estimates of AUC for each of the 58 patients. The mean and median AUC and the SD were 147.1, 117.4, and 116.8 mg ⫻ h/L, respectively. Fifty-four patients had a drug concentration obtained within 1 h of the end of infusion (Cmax). The mean, median, and SD values for Cmax were 15.0, 15.3, and 7.5 mg/L, respectively. These latter values were directly observed. Population of patients. Of the 58 patients for whom pharmacokinetic parameter values were available, 47 had an identified pathogen with a levofloxacin MIC and both clinical and microbiological outcomes. Table 2 displays the primary path1592 • JID 2004:189 (1 May) • Drusano et al. ogens isolated and their levofloxacin MICs. There were 83 pathogens recovered from 47 patients. As stated above, for calculation of the AUC:MIC ratio, the highest MIC value was used. In table 3, the demographic values of the 47 patients contributing to the efficacy analysis are displayed. Clinical outcome. Logistic regression analysis of clinical outcomes revealed only site of acquisition of infection (ICU vs. not ICU) and age (!70 years) as having an effect on the probability of a good clinical outcome. Both relationships were borderline in significance (P p .06). Microbiological outcome. The first step in the analysis was to determine whether there was a breakpoint in the value of the AUC:MIC ratio that predicted a higher probability of eradication of the pathogen. CART analysis demonstrated that the range of AUC:MIC ratio of 87.0–109.8 contained the breakpoint, and it found the value of the independent variable (AUC: MIC ratio) that optimally separates the proportion of patients with a good outcome (eradication of the pathogen) into 2 groups (high and low proportions of a good outcome). Consequently, this value is dependent on the data set evaluated. The breakpoint could be validly set at any value in the range between these values. Three reasonable values are 87.0 (lower end), 98.5 (middle), and 109.8 (higher end). In the present analysis, total drug concentrations were used. Over the concentration range seen in the present study, the protein binding of levofloxacin is ∼30%. All exposure targets can be simply multiplied by use of the free fraction (0.7), to obtain the free drug–exposure target. For microbiological outcome, when tested univariately, a number of covariates had a significant effect on the probability of eradication of the pathogen. In descending order of significance, these were the following: AUC:MIC ratio breakpoint, age, MIC breakpoint, and vasopressor use. The most significant covariate was the AUC:MIC ratio breakpoint. Of interest, infection with Table 2. Pathogens recovered from 47 patients with nosocomial pneumonia who were receiving levofloxacin (750 mg intravenous once daily). Pathogen No. of isolates MIC, range, mg/L Pseudomonas aeruginosa Staphylococcus aureus 8 17 Enterobacter species Hemophilus influenzae 6 10 Klebsiella species Serratia marcescens Acinetobacter species 7 5 3 0.015–0.06 0.06 –0.125 0.06–0.125 Stenotrophomonas maltophilia Streptococcus pneumoniae Citrobacter kosei 2 1 1 0.25–0.5 1.0 0.125 Others All pathogens 23 83 0.25–6.0 0.125 to 116.0 0.06 !0.015–0.125 Table 3. Demographic values of the 47 patients contributing to the efficacy analysis. Characteristic (no. of patients for whom data were available) Value Sex, no. of patients (47) Male 35 Female Race, no. of patients (43) White 12 3 39 Nonwhite Weight, median (range), kg (47) Height, median (range), in (45) 4 72.7 (50–124) 69 (54–77) Age, median (range), years (47) APACHE II score, median (range) (47) Estimated CLcra, median (range), mL/min (43) ICU residence, no. of patients (47) 53 (19–93) 16 (5–28) NOTE. a 69.4 ( 10.7–159.0) 46 CLcr, creatinine clearance; ICU, intensive care unit. Estimated by use of the Cockcroft and Gault method [24]. S. aureus, P. aeruginosa, or E. cloacae did not have a significant effect on the probability of a successful microbiological outcome. However, it should be remembered that, when P. aeruginosa was recovered, ceftazidime or piperacillin/tazobactam was added to the regimen. Likewise, some patients had vancomycin added for methicillin-resistant S. aureus. The “other active drugs” covariate was not significant when tested univariately and did not add significantly to the final model. Further attempts at model building demonstrated that only age added any explanatory value to the AUC:MIC ratio breakpoint. The final model is displayed in figures 1 and 2. CART analysis revealed the breakpoint for age as 67 years. Eradication occurred in 34 (91.9%) of 37 patients !67 years old and in 13 (61.9%) of 21 patients ⭓67 years old. A ROC curve was constructed for an AUC:MIC ratio breakpoint of 87. The ROC area was 0.849. The predictive performance was significant (P p .011, Fisher’s exact test). The model performance for all patients, for attainment of the AUC:MIC ratio breakpoint, stratified by age !67 years or ⭓67 years, is displayed in table 4. Overall (over both strata), an AUC:MIC ratio of ⭓87 has a positive predictive value (90%) for eradication of the pathogen, whereas a value !87 has a negative predictive value of 57.1% (this is without reference to age; for the effect of age, see table 4). Target-attainment analysis. Since we used a population pharmacokinetic analysis, it was straightforward to perform a Monte Carlo simulation, to determine the target-attainment rate for a range of MIC values. This approach has elsewhere been shown to be predictive of outcome [13, 14]. The targetattainment analysis for an AUC:MIC ratio breakpoint of 87 is displayed in figure 3. MIC distributions for 404 strains of P. aeruginosa and for 297 strains of E. cloacae are also displayed. To determine an estimate of how the drug dose will perform clinically, one can use the distribution of MIC values for the target pathogen and take an expectation (obtain a weighted average) over the MIC distribution, with respect to the targetattainment rate [13, 14]. The data for breakpoint values of 87, 98.5, and 109.8 are displayed in table 5. DISCUSSION Nosocomial pneumonia remains a major therapeutic challenge. Fluoroquinolones are attractive agents for therapy in this sit- Figure 1. Probability of eradication of the pathogen, as a function of age and whether the patient achieves an area under the curve (AUC):MIC ratio of ⭓87. Patients with younger age and who achieve an AUC:MIC ratio of ⭓87 have a significantly higher probability of achieving eradication of their pathogen. Classification and regression tree analysis identified the breakpoint for age as 67 years. Quinolone Dynamics in Nosocomial Pneumonia • JID 2004:189 (1 May) • 1593 Figure 2. Final model for microbiological outcome, for patients with nosocomial pneumonia receiving levofloxacin (750 mg intravenous once daily). P p .001; McFadden’s r2 p 0.31. CI, confidence interval; OR, odds ratio. uation because of their rapid, concentration-dependent rate of kill and because of their safety profile. It is important, however, to recognize that fluoroquinolone exposure, as indexed to AUC: MIC ratio or Peak:MIC ratio [4–7], has an important effect on the probability of a good outcome. In a retrospective evaluation of patients from multiple studies who were receiving different doses of ciprofloxacin for nosocomial infections, Forrest et al. [6] demonstrated a relationship between AUC:MIC ratio and the probability of a good clinical or microbiological outcome. The first prospective study of the exposure-response relationship for a fluoroquinolone was published by Preston et al. [15], who followed 134 patients receiving levofloxacin (500 mg once daily) for community-acquired infections. In that study, either AUC:MIC ratio or Peak:MIC ratio could be linked to outcome. Again, in a retrospective evaluation, Ambrose et al. [16] examined patients with lower– respiratory tract S. pneumoniae infection who were receiving gatifloxacin or levofloxacin and found that the AUC:MIC ratio was linked to outcome. These clinical studies have been supported by both in vitro and animal model investigations, in which either AUC:MIC Table 4. ratio or Peak:MIC ratio can be shown to be linked to microbiological outcome [4, 5, 7, 17]. Because of the abundance of data supporting AUC:MIC ratio as being the covariate most closely linked to outcome, we decided to seek a breakpoint for AUC:MIC ratio that was related to outcome. To attempt to link exposure to outcome, it is necessary to first have a robust estimate of drug exposure for each patient. The use of stochastic design techniques and population pharmacokinetic modeling, followed by MAP-Bayesian estimation, allowed a good estimate of the important pharmacokinetic parameters (table 1). The observed-predicted regression indicates that the estimates were robust and that the model explained almost 95% of the variability in the drug concentrations over time. Calculation of bias and precision demonstrated that the estimates were acceptably unbiased and precise. It is also important to recognize that the drug concentration– time profile observed for these patients differs substantially from that seen for populations of volunteers [18]. The most important parameter, drug clearance, was lower in the 58 patients in the present study (median, 6.24 L/h; mean, 7.24 L/h) than in a population of volunteers with normal renal function (mean, 11.2 L/h) [18]. Also, the variability in clearance is substantially greater, with an SD of 4.36 L/h, a value 60%–70% (mean vs. median) of that of the measure of central tendency. Given the age and illness of the population studied, this is not a surprise. Certainly, drug disposition in target populations of ill patients differs substantially from that seen in populations of volunteers. We were unable to link AUC:MIC ratio to clinical outcome. Indeed, only the site of acquisition of the nosocomial pneumonia (ICU vs. not ICU) and age had an influence on the probability of a good clinical outcome. Given the physiological Model performance. Characteristic, parameter Age !67 years AUC:MIC ratio less than breakpoint AUC:MIC ratio greater or equal to breakpoint Eradication of pathogen 3 26 Sensitivity Specificity Positive predictive value (predicting eradication) 26/29 (89.6) 1/2 (50.0) 26/27 (96.3) Negative predictive value (predicting persistence) Age ⭓67 years AUC:MIC ratio less than breakpoint 1/4 (25.0) 0 AUC:MIC ratio greater or equal to breakpoint Sensitivity Specificity 10 10/10 (100.0) 3/6 (50) Positive predictive value (predicting eradication) Negative predictive value (predicting persistence) 10/12 (76.9) 3/3 (100.0) NOTE. Data are no. (%) of patients. 1594 • JID 2004:189 (1 May) • Drusano et al. Persistence of pathogen 1 1 3 3 Figure 3. Ability of a dose of drug to attain an area under the curve (AUC):MIC ratio of 87 (the target ratio for eradication of the pathogen) is affected by the distribution of the drug’s clearance in the population and by the MIC of the pathogen. The fractional target attainment for 10,000 simulated subjects is displayed as a function of MIC values for levofloxacin (750 mg intravenous once daily; A) and for ciprofloxacin (400 mg iv every 8 h; B). In addition, the fraction of isolates at a specific MIC value for levofloxacin is displayed for Pseudomonas aeruginosa and Enterobacter cloacae. For ciprofloxacin, these data are provided for P. aeruginosa. This allows placing the target-attainment fractions in perspective. The drug dose will be expected to perform better if the fraction of isolates is high, whereby the MIC values give a high level of target attainment. The pharmacokinetic data used for the analysis in panel B are from Forrest et al. [19]. alterations in patients suffering from this pathological process, it is not surprising that a signal for drug exposure, of appropriate strength, could not be detected. Indeed, microbiological outcome likely is the more important and sensitive measure of outcome. In contradistinction to the case with all other drugs, with antimicrobials, one is trying to dock a molecule not in a human receptor, but rather in the target receptor in the pathogen (in this instance, Topoisomerase II and IV). Consequently, the clearest signal of effect would be expected in the microbiological response. Furthermore, given the physiological state of these patients, the eradication of the infecting pathogen is a necessary but insufficient condition for a good clinical outcome. Patients could have a poor clinical outcome even with microbiological eradication in the lungs, if the damage to their gas-exchange mechanisms was sufficient before the initiation of therapy. The AUC:MIC ratio had a significant effect on the probability of eradicating the infecting pathogen. Indeed, no other factor identified had a more significant effect. Also, only age significantly added to the explanatory power of the model. Achieving an AUC:MIC ratio at the breakpoint or higher conferred a 90% probability of eradicating the infecting pathogen (36/40 patients), whereas failing to achieve the breakpoint resulted in only a 43% probability of eradication (3/7 patients). Quinolone Dynamics in Nosocomial Pneumonia • JID 2004:189 (1 May) • 1595 Table 5. Target-attainment rates for a 750-mg intravenous dose of levofloxacin, for distributions of Pseudomonas aeruginosa (n p 404) and Enterobacter cloacae (n p 297) isolates, by use of a 10,000-subject Monte Carlo simulation. AUC:MIC ratio breakpoint P. aeruginosa, % E. cloacae, % 87.0 98.5 72.4 70.5 91.7 89.4 109.8 68.7 89.1 It should be noted that free drug is the active moiety in killing bacteria. Levofloxacin is ∼30% protein bound. The AUCs were corrected for protein binding, and the AUC:MIC ratio breakpoint, as expected, shifted to 70% of the previous values (i.e., from 87–108.9 to 60.9–76.2). Because free-drug determinations are not easily available, we have chosen to concentrate on the total drug value for the breakpoint. When age is taken into account (table 4), it is clear that elderly patients are most at risk for not eradicating their infecting pathogen. Of patients !67 years old in the present study, 29 (93.5%) of 31 had eradication as their outcome. Of patients ⭓67 years old, 10 (62.5%) of 16 had eradication as their outcome. Achieving the AUC:MIC ratio breakpoint of 87 has the greatest effect in this group, in which none of the 3 patients not achieving the breakpoint eradicated the pathogen; 10 (76.9%) of 13 patients eradicated their pathogen when the breakpoint exposure was achieved. A question that arises once a breakpoint exposure value associated with an acceptable probability of eradication is identified is how adequate is the drug dose approved for clinical use in achieving that breakpoint value? This question can be addressed by the use of Monte Carlo simulation. This technique has been used elsewhere for evaluation of drug dose [13, 14], with the predictions from the technique prospectively validated. Monte Carlo simulation allows the full range of drug exposure that will occur in the target population to be estimated. For a specific drug dose, the ability to attain the target exposure value can be determined as a function of MIC. This allows the clinician to explicitly determine whether the probability of exposure-target attainment is acceptable for a specific MIC at a specific drug dose. If the distribution of MICs for the drug for the target pathogen is available, it is then possible to estimate the rate of target-value attainment for the population by obtaining a weighted average over the range of the MIC distribution. Figure 3 displays the target-attainment rate by MIC, and table 5 displays the target-attainment rate for E. cloacae and P. aeruginosa, as well as the difference in the target attainment by the possible range of target values. Clearly, irrespective of the possible range of target values, a 750-mg dose of levofloxacin has an excellent target-attainment 1596 • JID 2004:189 (1 May) • Drusano et al. rate for E. cloacae (89%–92%). However, for P. aeruginosa, the target-attainment rate was 69%–72%. Although this value is reasonable and, for target attainment for this pathogen, is at least as good as that for any other licensed fluoroquinolone, it indicates that it would be prudent to add an additional active agent when P. aeruginosa is identified as the infecting pathogen, as was done in the present study. Indeed, we performed a 10,000-subject Monte Carlo simulation using the pharmacokinetic data of Forrest et al. [19], who followed patients receiving ciprofloxacin (400 mg iv every 8 h) for P. aeruginosa (figure 3B), and demonstrated target-attainment rates of 69.8%, for a target AUC:MIC ratio of 87, and 67.3%, for a target AUC: MIC ratio of 108.9. A question arises about the predictive ability of Monte Carlo simulation in this circumstance. Our group has recently defined a target AUC:MIC ratio for suppression of emergence of resistance in P. aeruginosa for fluoroquinolones as 157 [20]. Using this as an exposure target, we performed a 10,000-subject simulation for ciprofloxacin that was similar to the one performed here. Again, we used the data of Forrest et al. [19], which resulted in a target-attainment rate of 61.8%. This is in excellent concordance with the observed emergence of resistance rate of 33% published by Fink et al. [21]. This indicates that the outcomes of the Monte Carlo–simulation analysis from the present study are reasonable. Also, the increased rate of emergence of resistance, as seen in the study by Fink et al. [21], reminds us that it is unwise to depend on any current single agent for the therapy of P. aeruginosa pneumonia. In summary, we have been able to study patients with nosocomial pneumonia and ascertain that an AUC:MIC ratio of 87–110 will provide a high probability of eradication of the pathogen. Furthermore, for E. cloacae, we have been able to demonstrate that the dose used in the present clinical trial will attain the exposure target with a high probability. It would be prudent to add a second agent if P. aeruginosa is identified as the infecting pathogen. Over the course of the past decade, we have seen an unprecedented ability to generate relationships between drug exposure (normalized to pathogen susceptibility) and the probability of some measure of a good outcome [6, 15, 16, 22]. The likely explanation rests in the wider availability of mathematical modeling tools, which allow estimation of informative sampling times (stochastic optimal design theory), the ability to estimate pharmacokinetic parameter values for a population of patients (population pharmacokinetic modeling), and the ability to use these estimates to obtain robust estimates of pharmacokinetic parameter values for individual patients (MAP-Bayesian estimation). These, together with traditional statistical techniques, have allowed the development of these relationships. Given that we are seeing rapid emergence of resistance to multiple antimicrobial classes among nosocomial as well as community pathogens, such techniques may best be applied to the problem of suppression of emergence of resistance. Finally, such techniques can also be applied to the development of new agents specifically targeted at resistant pathogens. At a recent meeting of the Anti-Infective Drug Products Committee of the Food and Drug Administration [23], 2 main issues regarding the development of such agents were examined. The first was the difficulty of enrolling sufficient numbers of patients with such pathogens, to demonstrate the activity of the agent. The second is that the number of patients would be inadequate to document the safety of the study agent. In the present study, we were able to demonstrate that a relatively small number of patients (∼50) can be studied to determine the relationship between exposure and response. This was done in the setting of an active control (imipenem/cilastatin), to document that the outcome was credible and that the expected control response was attained. This indicates that such development programs are possible. The safety issues could be addressed by controlled trials performed without respect to the ability to evaluate microbiological outcome. 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