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Preston SL, Drusano GL, Berman AL, et al. Pharmacodynamics of LevofloxacinA New Paradigm for Early Clinical Trials. JAMA. 1998;279(2):125–129. doi:10.1001/jama.279.2.125
Context.— One purpose of early clinical trials is to establish the appropriate
dose of an antibiotic for phase 3 trials. Development of a relationship between
the ratio of drug exposure to organism minimum inhibitory concentration (MIC)
and therapeutic response early in the development process would allow an optimal
choice of dose to maximize response.
Objective.— To prospectively quantitate the relationship between plasma levels of
levofloxacin and successful clinical and/or microbiological outcomes and occurrence
of adverse events in infected patients.
Design.— Multicenter open-label trial.
Setting.— Twenty-two enrolling university-affiliated medical centers.
Patients.— A total of 313 patients with clinical signs and symptoms of bacterial
infections of the respiratory tract, skin, or urinary tract.
Main Outcome Measures.— Clinical response and microbiological eradication of pathogenic organisms.
Results.— Of 313 patients, 272 had plasma concentration-time data obtained. Of
these, 134 patients had a pathogen recovered from the primary infection site
and had an MIC of the pathogen to levofloxacin determined. These patients
constituted the primary analysis group for clinical outcome. Groups of 116
and 272 patients, respectively, were analyzed for microbiological outcome
and incidence of adverse events. In a logistic regression analysis, the clinical
outcome was predicted by the ratio of peak plasma concentration to MIC (Peak/MIC)
and site of infection (P<.001). Microbiological
eradication was predicted by the Peak/MIC ratio (P<.001).
Both clinical and microbiological outcomes were most likely to be favorable
if the Peak/MIC ratio was at least 12.2.
Conclusions.— Levofloxacin generated clinical and microbiological response rates of
95% and 96%, respectively. These response rates included fluoroquinolone "problem
pathogens," such as Streptococcus pneumoniae and Staphylococcus aureus. Exposure to levofloxacin was significantly
associated with successful clinical and microbiological outcomes. The principles
used in these analyses can be applied to other classes of drugs to develop
similar relationships between exposure and outcome. This pharmacokinetic modeling
could be used to determine optimal treatment dose in clinical trials in a
shorter time frame with fewer patients. This modeling also should be evaluated
for its potential to improve outcomes (maximizing therapeutic response, preventing
emergence of resistance, and minimizing adverse events) of patients treated
with this drug.
CLINICAL TRIALS of drug therapy are performed to answer a number of
questions. One question often considered early in the drug development process
relates to determining the "correct" dose of drug to use in large, multicentered
randomized controlled trials. The correct dose is that for which a large proportion
of the population intended to take the drug will have a high probability of
a successful outcome and a low probability of developing drug-related adverse
events. In classic trial designs, phase 2 studies often include evaluation
of several different doses of drug. However, these studies are often small,
so that little real difference in efficacy can be ascertained. Phase 2 studies
are limited in predicting the correct dose in part because dose is a poor
index of drug exposure. Estimates of drug disposition in individual patients
would theoretically allow definitive exposure-response relationships to be
During the last decade, a number of pharmacological tools, such as optimal
sampling theory, population pharmacokinetic modeling, and Bayesian estimation,
have become widely available to the clinical investigator to allow determination
of estimates of drug exposures in individual patients.1-7
When investigating anti-infective agents, in addition to a measure of
drug exposure, a measure of the potency of the drug for the pathogen infecting
an individual patient is required. In developing the therapeutic response
relationships, one may take both into account by forming ratios between measures
of exposure (peak concentration, area under the plasma concentration vs time
curve [AUC]) and measures of drug potency (minimum inhibitory concentration
[MIC])or by examining the time that drug concentrations remain above the MIC.
Because there is a wide range of MICs in different organisms causing infection,
the ratio will have a much broader range than the exposure variables (eg,
peak, AUC) or MICs alone. Consequently, it may be easier to determine these
exposure-effect relationships for anti-infective agents relative to other
We conducted a multicenter, noncomparative trial to assess the safety
and efficacy of levofloxacin for the treatment of a variety of community-acquired
infections. Our objective was to prospectively link a measure of exposure
to an outcome, such as clinical efficacy, microbiological efficacy, and/or
development of an adverse event, with the hypothesis that the use of a combination
of newer modeling methods would allow delineation of such relationships.
A total of 313 adult patients (18 years or older), from 22 university-affiliated
medical centers, with clinical signs and symptoms of bacterial infections
of the respiratory tract, skin, or urinary tract that were of significant
severity to require at least 3 days of intravenous antibiotic therapy were
Inclusion criteria were presence of signs and symptoms of acute bacterial
sinusitis, acute bacterial exacerbation of chronic bronchitis, community-acquired
bacterial pneumonia, complicated or uncomplicated bacterial skin infection,
acute pyelonephritis, or complicated urinary tract infection, and the ability
to give informed consent.
Exclusion criteria were (1) infection due to a levofloxacin-resistant
organism; (2) requirement for additional systemic antibacterial therapy; (3)
previous allergic or serious adverse reaction to a quinolone; (4) presence
of a seizure disorder or unstable psychiatric disorder; (5) recent history
of head trauma; (6) cystic fibrosis; (7) severe renal failure (creatinine
clearance <0.33 mL/s) or oliguria (urine output <20 mL/h); (8) shock
(supine systolic blood pressure <80 mm Hg) due to any cause or high likelihood
of death during the course of the study; (9) hemoglobin level less than 80
g/L or platelet count less than 50×109/L; (10) human immunodeficiency
virus infection and CD4 cell count 0.2×109/L or less, organ
transplantation, or neutropenia (absolute neutrophil count ≤0.5×109/L); (11) PCO2 greater than 55 mm Hg; and (12) pregnant
or nursing women.
Patients with respiratory tract or skin infections received 500 mg of
levofloxacin every 24 hours intravenously for at least 3 doses. Patients with
complicated urinary tract infections or acute pyelonephritis received 250
mg intravenously every 24 hours for at least 3 doses. Patients with moderate
renal impairment (creatinine clearance 0.33-0.83 mL/s as calculated by the
method of Cockcroft and Gault8) received 500
mg every 48 hours. No dosage changes were made for renally impaired patients
receiving the 250-mg dose. Following the 3 intravenous doses, all patients
were allowed to complete their course of therapy with oral levofloxacin, if
The duration of therapy for community-acquired bacterial pneumonia,
skin infections, and acute bacterial sinusitis was 10 to 14 days; acute exacerbation
of chronic bronchitis, 5 to 7 days; and urinary tract infections, 7 to 10
Clinical response was determined by comparing the patient's baseline
signs and symptoms of infection with those after therapy. Patients' response
to therapy was classified as follows: (1) cure was
defined as resolution of clinically significant signs and symptoms associated
with admission (baseline) bacterial infection along with stability (no change)
or improvement or resolution of x-ray findings; (2) improvement was defined as partial resolution of clinical signs and symptoms of
admission (baseline) bacterial infection without further antibiotic treatment
and stability (no change) or improvement or resolution of x-ray findings;
(3) failure was defined as no response to therapy;
and (4) indeterminate was defined as unable to evaluate
because patient was unavailable for follow-up. Cure and improvement were both
considered a successful response. Failure was considered an unsuccessful response.
If a patient died of a cause other than infection, an impression of the status
of the infection at the time of death was rendered.
All pathogens isolated from the appropriate specimens responsible for
the admission diagnosis of bacterial infection were evaluated for microbiological
response to treatment as follows: (1) Eradicated—eradication of admission
pathogen in the posttherapy cultures. If a patient's infection had improved
to the point where no material was available for culture, the admission pathogen
was presumed eradicated. (2) Persisted—continued presence of the admission
pathogen in the posttherapy cultures. If the patient had a clinical failure
and no posttherapy culture was taken while the patient was not receiving antibiotics,
then the admission pathogen was presumed to persist. (3) Persisted with acquisition
of resistance—continued presence of the admission pathogen in the posttherapy
cultures with documented emergence of resistance. (4) Unknown—no test-of-cure
culture available because patient was unavailable for follow-up. Eradicated
and presumed eradicated were considered successful responses. Persisted, presumed
persisted, and persisted with emergence of resistance were considered unsuccessful
All patients were evaluated for treatment-emergent adverse events. A
treatment-emergent adverse event was defined as an adverse event that was
new in onset or aggravated in severity or frequency following administration
of levofloxacin regardless of relationship to drug. However, investigators
provided an assessment of the drug-relatedness of the adverse event.
The levofloxacin plasma sampling schedule was designed using the SAMPLE
module (optimal sampling theory) of the ADAPT II package of programs of D'Argenio
and Schumitzky,9 extended for the case of population
kinetic modeling, using prior data.10 The levofloxacin
plasma sampling schedule included 6 time points (trough; end of infusion;
and 2, 6.75, 7.75, and 9.25 hours following dosing) after the third intravenous
dose. Plasma concentrations were measured by the sponsor using a high-performance
liquid chromatographic assay.11
Plasma concentrations were analyzed using the NPEM2 program to obtain
population pharmacokinetic parameters using a 1- and 2-compartment open model
with first-order elimination from the central compartment.6
Parameters estimated included clearance (CL, as liters per hour), volume of
distribution (VS, as liters per kilogram), and the intercompartmental transfer
rate constants (KCP and KPC, as hours−1).
The assay variance was estimated using regression modeling based on the observed
variance at 4 different concentrations throughout the range. The inverse of
the estimated assay variance was used as the weighting in the pharmacokinetic
Bayesian pharmacokinetic parameter estimates were then determined for
each patient using the population parameters obtained from NPEM2 and using
the "population of one" utility within this program. The individual Bayesian
parameter estimates were then used in the simulation module of ADAPT II9 to allow calculation of an AUC and to simulate individual
peak and trough concentrations for each patient. Other derived parameters
included ratio of peak plasma concentration to MIC (Peak/MIC), AUC /MIC ratio,
and time plasma concentrations of the drug that remained above the MIC (Time>MIC),
as a fraction of the dosing interval.
Analysis of patient data included the categorical variables of sex,
race, organism isolated (each species of organism was treated as a separate
category; if more than 1 organism was present, the organism that was most
resistant to levofloxacin by MIC was used in the analysis), site of infection,
and occurrence of bacteremia, as well as the continuous variables of age,
MIC of organism, and the derived pharmacokinetic parameters of peak, trough,
AUC, Peak/MIC, AUC/MIC, and Time>MIC. These variables were analyzed with logistic
regression using the LOGIT module of SYSTAT (Evanston, Ill) to evaluate their
effect on clinical outcome. Patient outcomes classified as cured and improved
were coded together (successful outcome), and those classified as failed were
coded separately (unsuccessful outcome). Only patients with estimated pharmacokinetic
parameters and an isolated organism were included in the analysis. This last
requirement was a prospectively determined part of the analysis. Significance
of the variable's impact on the probability of a successful clinical outcome
was determined by the log-likelihood ratio test. In this test, twice the log-likelihood
difference of the expanded model from the constant-only model was determined
and compared against a χ2 distribution with 1 df or the appropriate number of degrees of freedom. An α of less
than .05 was deemed significant. Predictor variables that were significant
were tested in the same way for model expansion using the log-likelihood ratio
test for the significance of model expansion, starting with the most significant
predictor variable in the model and attempting to expand the model with other
variables in their order of significance.
The patient data listed above was analyzed to examine the effect of
predictive variables on microbiological outcome. Patient's pathogens were
classified as eradicated or persisted. Only patients with estimated pharmacokinetic
parameters, an organism isolated, an MIC value, and data on organism eradication
or persistence were included. Logistic regression was used as described previously.
Using logistic regression, 3 analyses were performed to compare adverse
events of the central nervous system (CNS) (including psychiatric disturbances),
gastrointestinal tract, and skin with sex, race, site of infection, age, peak
and trough plasma concentrations, and AUC. These 3 systems were chosen because
they had an adequate number of events to attempt logistic regression analysis.
Only patients with estimated pharmacokinetic parameters were included (N=272).
These analyses used only patients with treatment-emergent adverse events assessed
by the investigator as definitely, probably, or possibly related to drug.
All other patients were classified as having no adverse event for the particular
system being analyzed.
Break points of pharmacodynamic variables (eg, Peak/MIC ratio, AUC/MIC
ratio) that divided patients into lower and higher probability groups for
successful clinical and microbiological outcome were determined using Classification
and Regression Tree (CART) analysis.12
Of 313 patients, 272 were included in the pharmacokinetic analysis;
36 patients were excluded because of lack of plasma concentration data, 3
because of physiologically impossible plasma concentrations and known sample
acquisition from the infusion line, and 2 because of known infusion time misspecifications.
Of these 272 patients, 134 had clinical outcome determinations and an identified
microorganism with a determined MIC. This group of 134 patients was used for
the primary efficacy analysis in an attempt to link predictor variables to
the probability of a successful clinical outcome. Of these patients, there
were 7 clinical failures.
The 134 patients did not differ from the full population of 272 patients
with regard to sex, race, age, or plasma concentrations of levofloxacin (peak
and trough concentrations as well as the AUC).
For the microbiological outcome analysis, 116 patients had both a microbiological
outcome determined and the data set indicated above for clinical outcome.
This group of 116 patients formed the primary data set for the microbiological
outcome analysis. Of these patients, 5 had persisting organisms.
For the adverse event analysis, all patients with pharmacokinetic parameters
(N=272) were included regardless of whether a pathogen was identified. There
were 8 skin adverse events (2.9%), 16 CNS adverse events (5.9%), and 31 gastrointestinal
adverse events (11.4%). These treatment-emergent adverse events were all thought
to be definitely, probably, or possibly related to drug by the investigators.
It was determined13 that a 2-compartment
pharmacokinetic model best fit the data. The mean, median, and SD of the pharmacokinetic
parameter values for the 2-compartment open model are presented in Table 1. Median values for each of the parameters
agreed well with the means. These parameter values are similar to those estimated
previously using data from a study of healthy volunteers.14
Mean peak concentration and AUC for a levofloxacin dose of 500 mg and dosing
interval of 24 hours were 8.67±3.99 µg/mL and 72.53±51.17
µg·h/mL, respectively. In this analysis, a total of 1528 samples
were analyzed (mean, 5.6 samples per patient). Patients switched to oral from
intravenous therapy at an average of 3.5 days, with a median of 3 days; 67%
(210/313) switched to oral therapy on day 4.
Six variables were significant univariately in affecting the probability
of a successful clinical outcome, including site of infection, which was analyzed
as a categorical variable, as well as MIC, Peak/MIC ratio, AUC/MIC ratio,
Time>MIC, and age, which were analyzed as continuous variables. Peak/MIC ratio,
AUC/MIC ratio, and Time>MIC were virtually indistinguishable in their ability
to alter the probability of a successful outcome (Table 2). This is understandable as, when examined, Peak/MIC and
AUC/MIC ratios were highly correlated, with an r
value of 0.942 (Spearman rank correlation). Peak/MIC ratio and Time>MIC had
a Spearman rank correlation of 0.605. Table
2 shows the final model selection from variables that were significant
univariately for clinical outcome. In this model, Peak/MIC ratio and site
are included. Site of infection may provide important insight into the probability
of obtaining a successful clinical outcome, as there were no clinical failures
among patients with uncomplicated urinary tract infections.
Probability plots, with break point, are illustrated in Figure 1. For Peak/MIC ratio, the break point was determined to
be 12.2. Clinical success rates for patients achieving a ratio of greater
than 12.2 and 12.2 or less were 99.0% and 83.3%, respectively.
Univariately, 5 predictive variables significantly affected the probability
of a successful microbiological outcome, as shown in Table 3. These predictors are the same as those selected for the
clinical outcome analysis, along with AUC. When these were examined for model
expansion, the final model selected by the log-likelihood ratio test included
only Peak/MIC ratio plus AUC (Table 4).
A competing final model included Peak/MIC ratio alone (Table 3). The simpler model was preferred because of its greater
physiologic believability. The break point was 12.2 for Peak/MIC ratio. Microbiological
eradication success rates for patients achieving a Peak/MIC ratio of greater
than 12.2 and 12.2 or less were 100% and 80.8%, respectively. Probability
plots for successful microbiological outcome for Peak/MIC ratio and Peak/MIC
ratio plus AUC are shown in Figure 2.
No pharmacological (drug-related) predictive variables significantly
affected the probability of occurrence of an adverse event when gastrointestinal,
skin, and CNS systems were examined. However, the analysis of definite, probable,
and possible toxic events demonstrated that the probability of a CNS adverse
event was influenced by site of infection and the probability of a skin adverse
event was influenced by race, specifically, patients of Hispanic origin, with
50% of the skin adverse events occurring in this group (Table 5).
The primary hypothesis of this study was that, by using newer mathematical
modeling tools, it is possible to prospectively determine relationships between
measures of drug exposure and measures of patient outcome in relatively small,
multicenter clinical trials. We successfully linked a measure of levofloxacin
exposure (Peak/MIC) to clinical outcome and microbiological outcome. We were
unable to link direct measures of drug exposure (peak concentrations, trough
concentrations, or AUC) to the occurrence of CNS, skin, or gastrointestinal
adverse events, even though the number of adverse events in each system was
greater than the number of clinical or microbiological failures, indicating
that there was most likely a sufficient number of occurrences of the adverse
events to detect an association, if one had existed.
In these analyses, it is clear that pharmacological variables, when
seen relative to a measure of potency of drug for the pathogen in question
(MIC), can have a powerful effect on clinical outcome and microbiological
outcome. These influences are sometimes modulated by the primary infection
site (P=.03 for clinical outcome). Clinical failures
were more likely for skin and soft tissue infections relative to either the
respiratory tract or the urinary tract. Of the 7 clinical failures, 4 were
in skin and soft tissue, 3 were in pulmonary sites, and none were in the urinary
tract, for observed failure rates of 16%, 3%, and 0%, respectively. When examined
more closely, 3 of 4 failures in the skin sites were in patients with complicated
skin and skin structure infections, mostly elderly or diabetic patients with
ulcers. One could speculate that the breakdown of the vascular system in such
a circumstance could decrease the penetration of drug to the primary infection
Of note, none of the species of organisms (n=35) behaved differently
in terms of influencing outcome. This finding was further supported when each
species of organism for which there were 5 or more isolates were analyzed
alone vs all other organisms. Again, no species was shown to behave significantly
different with regard to the probability of a successful clinical outcome.
Of the 134 patients, 21 (15.7%) had infections with Streptococcus
pneumoniae and 15 (11.2%) were infected with Staphylococcus
aureus . Overall, 58% of the isolates were accounted for by 5 different
species. All 7 patients with clinical failures had a different causative microorganism.
A previous single-center, retrospective investigation by Forrest et
al15 with the fluoroquinolone ciprofloxacin
indicated that AUC/MIC ratio was most closely linked to outcome. In our analysis,
AUC/MIC ratio was significant, but on a statistical basis, Peak/MIC ratio
was better than AUC/MIC ratio as a predictive variable explaining clinical
and microbiological outcome. It should be noted that in our analysis, Peak/MIC
ratio and AUC/MIC ratio were highly correlated (r=0.942).
The likely reason that we found Peak/MIC ratio as the most explanatory dynamic
variable is that 82% of our patient population had a Peak/MIC ratio of 10:1
or greater. In the article by Forrest et al,15
Peak/MIC and AUC/MIC ratios were also highly correlated, but only about 50%
of the study population had a Peak/MIC ratio of approximately 10:1 or greater
(median value, 12:1). This is important, as our group has previously shown
in a discriminative animal model that when Peak/MIC ratio is greater than
10:1 for fluoroquinolones, it is the variable most closely linked to clinical
outcome.16 However, when the Peak/MIC ratio
is less than 10:1, AUC/MIC ratio is most closely linked to outcome. These
animal model findings are highly consistent with the 2 clinical databases
of ours and Forrest et al.15
In the microbiological outcome analysis, the same pharmacological variables
were linked to the probability of eradication. The AUC was found to be weakly
significant in this analysis; however, the estimate is negative, meaning that
a higher AUC is associated with lower probability of a successful microbiological
outcome. We believe that the negative correlation with AUC may have been influenced
by chance because the mean AUC in the group of 5 unsuccessful microbiological
outcomes (organism persistence) was 106.54 µg·h/mL vs 60.79 µg·h/mL
in the successful outcome group. The mean AUC/MIC ratio in the unsuccessful
outcome group was 63.38 vs 712.8 in the successful outcome group. This implies
the mean MIC was higher in those with microbiological failures. The patients
in the unsuccessful microbiological outcome group had a geometric mean MIC
of 2.64 vs 0.25 in the successful outcome group, which would obviously lead
to a decreased Peak/MIC ratio for the microbiological outcome group. Patients
with successful outcomes tended to have lower overall AUCs, but higher Peak/MIC
and AUC/MIC ratios. The explanation for the higher AUCs in the unsuccessful
outcome group could be a function of patient status, as the mean age was 63
years vs 41 years in the successful outcome group. Because the finding of
a lower AUC being associated with an improved microbiological outcome is physiologically
improbable and is likely related to patient status, we thought that the final
model of Peak/MIC ratio alone was logically stronger.
The break points for both clinical and microbiological outcome seen
for Peak/MIC ratio (12.2:1) are consistent with the 10:1 Peak/MIC ratio break
point developed by Blaser et al17 in an in vitro
hollow fiber model of fluoroquinolone effect with organism eradication as
the end point, further supporting our results. Attainment of this ratio with
levofloxacin increases the probability of successful clinical and microbiological
outcome and may potentially decrease the probability of emergence of resistance,
as an organism must persist to emerge resistant.
The observed association between the occurrence of CNS adverse events
and site of infection (sinus) is most likely due to the nature of sinus infections
(ie, headaches are frequently associated with sinusitis). The relationship
between skin adverse events and Hispanic origin is unclear.
No relationships were demonstrated between measures of exposure and
the probability of occurrence of treatment-emergent adverse events for any
of the 3 systems examined (gastrointestinal tract, skin, CNS). Clearly, as
there were reasonable number of adverse events observed, we must conclude
that the occurrence of these adverse events is only weakly (if at all) linked
to our measures of exposure to levofloxacin.
In summary, we have prospectively determined relationships between exposure
to levofloxacin and the probabilities of successful clinical outcome and microbiological
outcome. This prospective development of drug concentration effect relationships
can serve as a template for such relationship development in other anti-infective
agents and in other therapeutic drug classes. Such relationships will allow
rational drug therapy, which may result in maximally efficacious and minimally
toxic clinical outcomes for ill patient populations and may allow design of
regimens to minimize the emergence of drug-resistant pathogens.