Relationship between Fluoroquinolone Area under the Curve

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
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
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.
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. 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
Vol, L
Kcp, h⫺1
Kpc, h⫺1
CL, L/h
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].
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).
No. of isolates
MIC, range,
Pseudomonas aeruginosa
Staphylococcus aureus
Enterobacter species
Hemophilus influenzae
Klebsiella species
Serratia marcescens
Acinetobacter species
0.06 –0.125
Stenotrophomonas maltophilia
Streptococcus pneumoniae
Citrobacter kosei
All pathogens
0.125 to 116.0
Table 3. Demographic values of the 47 patients contributing
to the efficacy analysis.
Characteristic (no. of patients
for whom data were available)
Sex, no. of patients (47)
Race, no. of patients (43)
Weight, median (range), kg (47)
Height, median (range), in (45)
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)
69.4 ( 10.7–159.0)
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.
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
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
of pathogen
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)
AUC:MIC ratio greater or equal to breakpoint
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)
Data are no. (%) of patients.
1594 • JID 2004:189 (1 May) • Drusano et al.
of pathogen
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
P. aeruginosa, %
E. cloacae, %
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. In this way, relatively large numbers of patients could be entered into clinical
trials quickly and relatively inexpensively, allowing the documentation of both efficacy and a lack of toxicity.
Such mathematical modeling techniques can and should be
applied to the drug-development process, to optimize the availability of new agents for resistant pathogens. The availability of
computers in the ICU and elsewhere in the hospital will allow
quantitative probability relationships, such as the one developed
in the present study, to be accessed by physicians in a transparent
way. Hopefully, this will lead to good outcomes for seriously
infected patients.
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