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Population Pharmacokinetics of Polymyxin B
AQ1 Pooja Manchandani
1
,Visanu Thamlikitkul
2
,Yanina Dubrovskaya
3
,Jessica T. Babic
4
,David C. Lye
5,6
,
Lawrence S. Lee
6
and Vincent H. Tam
1,7
Polymyxin B is used as a last treatment resort for multidrug-resistant Gram-negative bacterial infections. The objectives of
this study were to examine the population pharmacokinetics of polymyxin B and investigate factor(s) influencing pharmaco-
kinetic variability. Four serial blood samples each were collected from 35 adult patients at steady state. The concentrations
of individual polymyxin B components were analyzed using a validated liquid chromatography / tandem mass spectrometry
assay and combined to derive total concentrations. A maximum likelihood expectation maximization approach was used to
fit the data. Various demographic variables were investigated as potential covariates for clearance and volume of distribu-
tion (V
d
) using linear regression analysis. A one-compartment model fit to the data satisfactorily (r
2
50.96). The best-fit
mean 6SD for clearance and V
d
were 2.5 61.1 L/h and 34.3 616.4 L, respectively. Creatinine clearance was found to be
a statistically significant covariate of clearance, but the magnitude was deemed clinically insignificant.
Study Highlights
WHAT IS THE CURRENT KNOWLEDGE ON THE
TOPIC?
þDespite being the last treatment resort for resistant Gram-
negative bacterial infections, the clinical dosing of parenteral
polymyxin B is still not well established. This is partly attrib-
uted to the paucity of published reports on the clinical pharma-
cokinetics of polymyxin B. Furthermore, several studies in the
past have reported that polymyxin B is predominantly cleared
by nonrenal pathways. In addition to this, we have previously
demonstrated that polymyxin B exposures in patients with nor-
mal and impaired renal function after receiving standard dosing
of polymyxin B are comparable.
WHAT QUESTION DID THIS STUDY ADDRESS?
þThe study examined the population pharmacokinetics of
polymyxin B in patients with Gram-negative infections.
Moreover, the study investigated factors influencing the phar-
macokinetic variability of polymyxin B.
WHAT THIS STUDY ADDS TO OUR KNOWLEDGE
þWe showed that the total body clearance of polymyxin B
was not well correlated with the creatinine clearance of the
patients. In addition, the actual body weight of patients was
a poor predictor for the volume of distribution. Covariates
such as creatinine clearance and actual body weight of
patients might not be an accurate predictor of polymyxin B
pharmacokinetics.
HOW THIS MIGHT CHANGE CLINICAL PHARMA-
COLOGY OR TRANSLATIONAL SCIENCE
þThis population pharmacokinetic model would be a useful
tool in predicting the polymyxin B pharmacokinetic exposure
after standard clinical doses.
The increasing rate of antimicrobial resistance has posed a serious
threat to the clinical management of multidrug-resistant Gram-
negative bacterial infections.
1,2
This situation has led to the emer-
gence of parenteral polymyxins as a last-resort treatment option
against these challenging infections.
3–6
Both polymyxin B and
colistin (polymyxin E) are available and have been used clinically.
In view of its overall superior clinical pharmacological properties,
polymyxin B is expected to be more commonly used in the future.
7
Although polymyxin B is a potent antimicrobial agent, its clinical
utility is largely limited by its potential for nephrotoxicity.
8
Despite being available for clinical use for several decades, there is
still a great paucity of pharmacokinetic (PK) data guiding the opti-
mal dosing of polymyxin B in patients. Reports delineating the influ-
ence of various factors on the PK variability of polymyxin B in the
patients are scarce. As a result of these significant knowledge gaps,
the clinical dosing strategies of polymyxin B may not be fully opti-
mized. A thorough understanding of the clinical PKs of polymyxin B
is pivotal to maximize efficacy and minimize toxicity associated with
therapy. Therefore, the objectives of this study were to assess the PKs
of polymyxin B in a patient population and to investigate the factors
influencing PK variability. Combining relevant patient-specific sus-
ceptibility data, we anticipate that our findings would be a robust
tool in guiding the optimal polymyxin B treatment dosing strategy.
RESULTS
Patient demographics
Thirty-five patients (23 males, 4 Caucasians) were enrolled in the
study. The mean 6SD age, actual body weight, and creatinine
1
Department of Pharmacological and Pharmaceutical Sciences, University of Houston College of Pharmacy, Houston, Texas, USA;
2
Faculty of Medicine Siriraj
Hospital, Mahidol University, Bangkok, Thailand;
3
Department of Pharmacy, New York University Langone Medical Center, New York, New York, USA;
4
Department of Pharmacy, Baylor St. Luke’s Medical Center, Houston, Texas, USA;
5
Institute of Infectious Diseases and Epidemiology, Tan Tock Seng
Hospital, Singapore;
6
Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore;
7
Department of Pharmacy
Practice and Translational Research, Houston, Texas, USA. Correspondence: Vincent H. Tam (vtam@uh.edu)
Received 25 July 2017; accepted 8 December 2017; advance online publication 00 Month 2017. doi:10.1002/cpt.981
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 00 NUMBER 00 | MONTH 2017 1
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clearance of the patients included were 58.7 615.1 years, 57.7 6
15.6 kg, and 67 642 mL/min, respectively. Other demographic
details of the patients are summarized in TableT1 1.
Population PK modeling
A total of 139 data points were included in the analysis (Figure
S1, available online). The one-compartment model was chosen as
the best-fit model. The overall model fitting to the data was satis-
factory (r
2
50.96) and unbiased, as shown in FigureF1 1. The best-
fit PK parameter estimates and covariance matrix are shown in
Tables T22and T33. The mean elimination half-life was 10.1 h. Cre-
atinine clearance (but not age) was a statistically significant covar-
iate of clearance, but the magnitude was deemed clinically
insignificant (Figure S2). Volume of distribution (V
d
) was poorly
predicted by actual body weight (Figure S2). Furthermore, the
gender of the patients was not found to have a major effect on
the PK parameter estimates (data not shown). The PK profiles of
polymyxin B from selected dosing regimens were simulated based
on the best-fit model parameter estimates (Figure F22), and are
summarized in Tables T44and T55.
Correlation of drug exposure to outcome
Nephrotoxicity outcomes were available in 26 patients. In this
subcohort, the duration of therapy was 12.0 64.9 days. Overall,
the prevalence of nephrotoxicity was 26.9% (7 out of 26 patients;
Table 1 Demographic characteristics of patients
Characteristics
No. of patients 35
Male (%) 23 (65.7 %)
Age (years); mean 6SD (range) 58.7 615.1 (25–89)
Actual body weight (ABW) (kg); mean 6SD (range) 57.7 615.6 (36–112)
Total daily dose (mg); mean 6SD (range) 119.0 636.3 (65–240)
Dose/ABW (mg/kg); mean 6SD (range) 2.1 60.4 (1.3–2.8)
Baseline creatinine clearance (mL/min); mean 6SD (range) 66.8 642.4 (15–175)
Isolated micro-organisms
Acinetobacter baumannii 14
Pseudomonas aeruginosa 6
Klebsiella spp. 3
E. coli 2
Enterobacter spp. 1
Others 9
Infection site
Sputum 10
Blood 7
Abdomen 2
Urine 4
Others 12
Figure 1 Pred: predicted; Obs: Observed.
Table 2 Best-fit pharmacokinetic parameter estimates
Clearance (L/h) Volume of distribution (L)
Mean 2.5 34.3
Median 2.6 35.2
SD 1.1 16.4
% CV 43.8 47.8
% RES 9.0 11.3
SD, standard deviation; CV, coefficient of variation; RES, relative standard error.
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two risk, five injury). The area under the curve (AUC) observed
in patients with and without nephrotoxicity were 52.3 6
14.8 mg.h/L and 45.1 617.3 mg.h/L, respectively (P50.31).
DISCUSSION
Parenteral polymyxin B has gained a revived interest with the
increasing prevalence of multidrug-resistance in Gram-negative
bacteria over the past few decades. Despite escalating clinical use,
the PKs of polymyxin B in humans is still not thoroughly
characterized.
Several studies have been conducted over last decade to bridge
the knowledge gap in our understanding of polymyxin B PKs.
Our research group published the first paper focusing on the pop-
ulation PKs of polymyxin B close to 10 years ago.
9
However,
only polymyxin B1 (the most abundant component in the poly-
myxin B USP mixture) was examined in a handful of patients
(n59) with relatively normal renal function. Another small
study (n58) provided additional insights that drug elimination
was predominantly via nonrenal clearance pathways.
10
More
recently, a larger population PK study examined 24 critically ill
patients with a range of renal function.
11
The authors reported
that total body clearance of polymyxin B was poorly predicted by
creatinine clearance.
To best of our knowledge, the study is the largest study con-
ducted to date, aimed at evaluating the population PKs of poly-
myxin B and identifying the patient factors influencing the PK
variability of the drug. In contrast to the previous studies men-
tioned above, the present study is multicentered, with the inclu-
sion of a wide range of subject ethnicity, renal function, and
standards of practice. The major components of commercially
available polymyxin B are polymyxin B1, B2, B3, and isoleucine
B1, which were reported to constitute 73.5%, 13.7%, 4.2%, and
8.6% of the mixture, respectively.
12
While individual polymyxin
B components were assayed individually, they were combined as
total polymyxin B concentrations for the purpose of assessing the
PKs. We have previously examined the relative concentration–
time courses of the major components of polymyxin B, and
found individual components of polymyxin B mixture exhibited
comparable PK profiles.
13
Similarly, no considerable difference in
in vitro potency was observed among the polymyxin B compo-
nents against several clinically important bacterial strains.
14
Our results showed that the mean polymyxin B clearance in
our diverse patient cohort was 2.5 L/h, which was not drastically
different from those reported previously (range 1.9–2.4 L/h).
9,11
In addition, the intersubject variability (% CV) observed was also
comparable to the recent study by Sandri et al. (43.8% vs.
32.4%).
11
However, the mean elimination half-life (10.1 h) was
shorter than those reported previously (range 11.9–13.6 h), but it
was not skewed predominantly by patients with augmented renal
function (i.e., creatinine clearance >140 mL/min) (data not
shown). Of note, observations were only made over one dosing
interval for each subject.
We evaluated both age and creatinine clearance as covariates of
total polymyxin B clearance, as we reasoned that the variables
Figure 2 Simulation profiles based on best-fit population PK model; total
daily dose of 100 mg administered at different dosing frequencies: Q24h,
Q12h and Q8h (a); different daily doses (100, 150, and 200 mg) adminis-
tered Q12h (b).
Table 3 Best-fit pharmacokinetic parameter covariance matrix
Clearance (L/h) Volume of distribution (L)
Clearance (L/h) 1.2
Volume of distribution (L) 12.8 269.8
Table 4 Simulation profiles based on best-fit pharmacokinetic
model when total daily dose of 100 mg of polymyxin B was
administered at different dosing frequencies
Concentration (mg/ml)
Polymyxin B dosing frequency
Q8h Q12h Q24h
Css trough 0.7 1.1 0.6
Css max 2.1 2.4 3.4
Css avg 1.3 1.6 1.6
Table 5 Simulation profiles based on best-fit pharmacokinetic
model when different daily dose was administered every 12 h
Concentration (mg/ml)
Total daily dose of polymyxin B
100 mg 150 mg 200 mg
Css trough 1.1 1.6 2.2
Css max 2.4 3.6 4.8
Css avg 1.6 2.5 3.3
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CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 00 NUMBER 00 | MONTH 2017 3
were likely correlated. Despite a low coefficient of determination
(r
2
<0.3), creatinine clearance (but not age) was found to be a
statistically significant covariate of clearance. Since the intersub-
ject variability of drug clearance in critically ill patients is
expected to range from 35–50%, a 2-fold change in AUC is com-
monly accepted as the threshold justification for dosing adjust-
ment. Using the best-fit regression equation (Figure S2), a 2-fold
change in AUC would be expected only when there is a greater
than 120 mL/min decrease in creatinine clearance. In clinical
practice, patients with such a level of deteriorating renal function
would likely have been placed on renal replacement therapy.
Consequently, the magnitude of association was deemed clinically
insignificant and the impact of these variables on clearance vari-
ability was not further pursued. The conclusion is in general
agreement with our previous observations excluding patients with
augmented renal function.
15
The patients in this study had a
wide range of body weight, but the best-fit volume of distribution
could not be predicted by any of the demographic variables
examined.
The overall nephrotoxicity prevalence rate observed in this
study was 26.9%, which was consistent with our previous findings
in the US of 21.1%.
8
A study conducted by Elias et al. correlated
the impact of polymyxin B dosage on the clinical outcomes (i.e.,
in-hospital mortality and severe renal impairment) of the
patients.
16
In contrast, we were unable to demonstrate a statisti-
cal difference in AUC among patients with and without nephro-
toxicity; a larger patient cohort might be necessary. Alternatively,
given the heterogeneous distribution of polymyxin B in different
organs,
17
drug concentration in renal tissues could be more infor-
mative than systemic drug exposure in predicting the likelihood/
onset of nephrotoxicity.
18
There are several limitations to this study. First, the dosing
guidelines were different for each clinical site and the dosing regi-
mens used for specific patients were not standardized. Second,
the renal function of the patients was not directly measured for
all patients. Third, the sample size was relatively small and might
have limited us in establishing a robust relationship between
polymyxin B exposures and nephrotoxicity. Finally, we did not
account for concomitant administration of other antibiotics and
their influence on the treatment efficacy/nephrotoxicity.
In conclusion, the PKs of polymyxin B in a patient population
were characterized. In conjunction with relevant patient-specific
data, we anticipate that our model could be extended as a tool to
predict the PK exposure of polymyxin B in patients.
METHODS
Study design and sites
This study was a prospective, multicenter, observational study conducted
at four clinical sites: Siriraj Hospital (a 2,300-bed academic tertiary care
hospital) in Bangkok, Thailand; Tan Tock Seng Hospital (a 1,400-bed
acute-care general hospital) in Singapore; New York University Langone
Medical Center (a 800-bed academic medical center) in New York, NY;
and Baylor St. Luke’s Medical Center (a 850-bed teaching hospital) in
Houston, TX. Institutional Review Board (IRB) approval at each clinical
site and the University of Houston was obtained prior to the initiation
of this study. Written informed consent was obtained from each patient
(or their legal representative) prior to study enrollment.
Inclusion/exclusion criteria for patient enrollment
Adult patients (age 18 years) who were given at least 48 h of intrave-
nous polymyxin B (USP) daily for suspected/documented Gram-
negative bacterial infections were included in the study. Patients on any
form of renal replacement therapy or with fluctuating renal function
(increase or decrease in serum creatinine of more than 50% from the first
day of polymyxin therapy) were excluded.
Drug administration
Polymyxin B dosing regimens (daily dose, dosing interval, duration of
therapy, and duration of intravenous administration) at different clinical
sites were at the discretion of their attending medical teams. Polymyxin
B was administered as an intermittent intravenous infusion over 60–180
minutes, and every 12 h to 24 h.
Data collection
Patient data collected included demographics (e.g., age, ethnicity, and
gender) and pertinent laboratory findings (e.g., isolated micro-organism,
infection site, and serum creatinine). Creatinine clearance was estimated
from serum creatinine using the Cockcroft–Gault equation or urine col-
lection (by each clinical site). The prevalence of nephrotoxicity, as
defined according to the RIFLE (risk, injury, failure, loss, endstage kid-
ney disease) criteria
19
was tracked in selected patients for the duration of
polymyxin B therapy.
PK sampling schedule and polymyxin B assay
Four serial blood samples were collected from each patient at steady state
over the fourth or greater dosing interval (e.g., immediately prior to dos-
ing, 1–2 h, 8–12 after the end of drug infusion, and prior to the next
dose). Plasma samples were obtained by centrifugation within 30 min of
blood collection and were stored at –808C until analysis. Major compo-
nents of polymyxin B (e.g., polymyxin B1, polymyxin B2, polymyxin B3,
and isoleucine polymyxin B1) were quantified using a validated UP liq-
uid chromatography / tandem mass spectrometry (LC-MS/MS) method
with few modifications: Acquity UPLC HSS C
18
column (50 32.1 mm
internal diameter, 1.7 lm) from Waters (Milford, MA); mobile phase A,
0.1% formic acid in water; and mobile phase B, 0.1% formic acid in ace-
tonitrile. The samples were analyzed as described previously.
20
Population PK modeling and statistical analysis
The plasma concentration of each component (polymyxin B1, B2, B3,
and isoleucine B1) was quantified individually, and the concentrations
were summed to derive the total polymyxin B concentrations for esti-
mating the PK parameters. A maximum likelihood expectation maximi-
zation approach (MLEM) using one- and two-compartment models
with log-normal distributions was used to fit the PK profiles.
21–23
A
change of <0.001% in the likelihood function was set as the convergence
criterion for each structural model. The structural models were discrimi-
nated using the log-likelihood ratio test, adjusted for the difference in
the degrees of freedom. All PK modeling was performed using ADAPT
5 (University of Southern California, Los Angeles, CA).
The best-fit population PK parameters, clearance (in liters per hour)
and volume of distribution (V
d
, in liters), are reported as mean 6SD.
Using these best-fit parameter estimates and different dosing regimens,
the corresponding C
max
and C
min
were determined. Also, the area under
the concentration–time profile (AUC) of each subject was determined
by patient-specific daily dose/clearance. Various demographic variables
such as age, gender, ethnicity, actual body weight, and creatinine clear-
ance were investigated as potential covariates for the best-fit clearance
and V
d
. Continuous variables were compared using Student’s t-test. Cat-
egorical variables were compared using Fisher’s exact test. The influence
of potential covariates on the variability of PK parameters was estimated
by linear regression analysis. Pvalues of 0.05 or less were considered sig-
nificant. All the statistical analysis was performed using SYSTAT v. 12.0
(SYSTAT Software, Chicago, IL).
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AUTHOR CONTRIBUTIONS
P.M. and V.H.T. wrote the article; V.H.T. designed the research; V.T.,
Y.D., J.T.B., and D.C.L. performed the research; P.M. and V.H.T. analyzed
the data; P.M. and L.S.L. contributed new reagents/analytical tools.
FUNDING
The study was supported in part by the Health Systems Research and
Development Project (Faculty of Medicine Siriraj Hospital, Thailand), the
Thai Health Promotion Fund, the Health Systems Research Institute
(Thailand), Government Pharmaceutical Organization (Thailand), the Sin-
gapore Ministry of Health Communicable Diseases Public Health
Research Grant (CDPHRG/12NOV015) and the Singapore National Medi-
cal Research Council Clinician Scientist Award grant (NMRC/CSAINV/
0005/2016). The funders had no role in study design, data collection
and interpretation, or the decision to submit the work for publication.
Additional Supporting Information may be found in the online version of
this article.
CONFLICT OF INTEREST
The authors report no conflicts of interest.
V
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