European Journal of Clinical Pharmacology
https://doi.org/10.1007/s00228-018-2454-0
PHARMACOKINETICS AND DISPOSITION
Population pharmacokinetics of vancomycin and AUC-guided
dosing in Chinese neonates and young infants
Yewei Chen 1 & Dan Wu 1 & Min Dong 2 & Yiqing Zhu 1 & Jinmiao Lu 1 & Xiaoxia Li 1 & Chao Chen 3 & Zhiping Li 1
Received: 7 November 2017 / Accepted: 19 March 2018
# Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Objectives To develop a population pharmacokinetic (PK) model for vancomycin in Chinese neonates and infants less than
2 months of age (young infants) with a wide gestational age range, in order to determine the appropriate dosing regimen for this
population.
Methods We performed a retrospective chart review of patients from the neonatal intensive care unit (NICU) at Children’s
Hospital of Fudan University to identify neonates and young infants treated with vancomycin from May 2014 to May 2017.
Vancomycin concentrations and covariates were utilized to develop a one-compartment model with first-order elimination. The
predictive performance of the final model was assessed by both internal and external evaluation, and the relationship between
trough concentration and AUC0–24 was investigated. Monte Carlo simulations were performed to design an initial dosing
schedule targeting an AUC0–24 ≥ 400.
Results The analysis included a total of 330 concentration–time data points from 213 neonates and young infants with gestational
age (GA) and body weight of 25–42 weeks and 0.88–5.1 kg, respectively. Body weight, postmenstrual age (PMA) and serum
creatinine level were found to be important factors explaining the between-subject variability in vancomycin PK parameters for
this population. Both internal and external evaluation supported the prediction of the final vancomycin PK model. The typical
population parameter estimates of clearance and distribution volume for an infant weighing 2.73 kg with a PMA of 39.8 weeks
and serum creatinine of 0.28 mg/dL were 0.103 L/h/kg and 0.58 L/kg, respectively. Although vancomycin serum trough
concentrations were predictive of the AUC, considerable variability was observed in the achievement of an AUC0–24/MIC of
≥400. For MIC values of ≤0.5 mg/L, AUC0–24/MIC ≥400 was achieved for 95% of the newborn infants with vancomycin troughs
of 5–10 mg/L. When the MIC increased to 1 mg/L, only 15% of the patients with troughs of 5–10 mg/L achieved AUC0–24/MIC
≥400. For MIC values of 2 mg/L, no infants achieved the target. Simulations predicted that a dose of at least 14 and 15 mg/kg
every 12 h was required to attain the target AUC0–24 ≥ 400 in 90% of infants with a PMA of 30–32 and 32–34 weeks,
respectively. This target was also achieved in 93% of simulated infants in the oldest PMA groups (36–38 and 38–40 weeks,
respectively) when the dosing interval was extended to 8 h. For infants with a PMA ≥44 weeks, a dose increase to 18 mg/kg every
8 h was needed. The trough concentrations of 5–15 mg/L were highly predictive of an AUC0–24 of ≥400 when treating invasive
MRSA infections with an MIC of ≤1 mg/L.
Conclusions The PK parameters for vancomycin in Chinese infants younger than 2 months of age were estimated using the
model developed herein. This model has been used to predict individualized dosing regimens in this vulnerable population in our
hospital. A large external evaluation of our model will be conducted in future studies.
Keywords Vancomycin . Pharmacokinetics . NONMEM . AUC . Infants
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00228-018-2454-0) contains supplementary
material, which is available to authorized users.
* Zhiping Li
zplifudan@126.com
1
Department of Pharmacy, Children’s Hospital of Fudan University,
399 Wanyuan Road, Shanghai 201102, China
2
Division of Clinical Pharmacology, Cincinnati Children’s Hospital
Medical Center, Cincinnati, OH, USA
3
Department of Neonatology, Children’s Hospital of Fudan University,
Shanghai, China
Eur J Clin Pharmacol
Introduction
Vancomycin is the first-choice antibiotic for newborn infants
with suspected or confirmed β-lactam-resistant gram-positive
bacterial infections, such as methicillin-resistant
Staphylococcus aureus (MRSA) [1]. Although it has been prescribed for more than 50 years and has been widely studied,
many questions remain about the optimal and safe use of vancomycin in newborn infants [2]. Underestimating true vancomycin exposure may lead to an increased risk of renal toxicity,
while overestimation may be associated with treatment failure,
which underscores the importance of optimizing vancomycin
dosing in neonates and young infants to rapidly achieve adequate drug exposure. However, this has been challenging, as the
pharmacokinetics of vancomycin are highly variable in this population due to differences in maturation and development [3, 4].
Although clinicians still rely on trough concentration monitoring, which is dependent on the dosing interval, data from experimental and clinical studies have shown that the best metric for
optimization of vancomycin dosing in this population is the ratio
of the area under the 24-h concentration–time curve to the minimum inhibitory concentration (AUC0–24/MIC) [5–8].
Targeting an AUC0–24/MIC ≥400 is recommended by the
Infectious Disease Society of America when treating invasive
MRSA infections, and a trough concentration of 15–20 mg/L
is generally required to achieve this target when the MIC is
≤1 mg/L (as is common for many strains of MRSA) in adults
[9]. This strong correlation between trough and AUC0–24/MIC
to some extent reduces the reliance on calculating AUC0–24 in
adults. However, the relationship between trough and AUC0–
24/MIC in adults may not extrapolate to pediatrics [10–12].
Lanke et al. reported that troughs ranging from 10 to
12.5 mg/L were highly predictive of achieving an AUC0–24/
MIC ≥400 in adolescents when the MIC was ≤1 mg/L [13].
Frymoyer et al. suggest that a vancomycin trough concentration of 15–20 mg/L is unnecessary, and that lower trough
concentrations of 7–10 mg/L should be sufficient for treatment of invasive MRSA infection in >90% of neonates when
the MIC of vancomycin is 1 mg/L [12, 14]. Le et al. found that
an AUC0–24/MIC of ≥400 correlated with a similar trough
concentration of 8–9 mg/L in 75% of pediatric patients [15].
The lower target trough concentration raises important topics
for discussion. Hahn et al. performed a validation study of a
pediatric population pharmacokinetic (PK) model of vancomycin at their institution, and they do not recommend a lower
trough concentration, but advise individual AUC estimation
using Bayesian approaches [16]. Although researchers have
recently proposed novel dosing guidelines for vancomycin in
neonates based on AUC targets [17], it is still unknown
whether the previous dosing advice is applicable to Chinese
populations.
Population PK models are a powerful tool that can aid
clinicians and facilitate individualized drug therapy. By
incorporating patient-specific characteristics, dosing information, drug concentrations, and consideration of intra- and
inter- patient variability, population PK models can provide
a more personalized approach to therapeutic decisionmaking [18, 19]. The purpose of this investigation was to
establish a population PK model of vancomycin in Chinese
neonates and infants less than 2 months of age (Byoung
infants^). In addition, an external evaluation was performed
to test the predictive performance in an independent dataset.
Additionally, the obtained population PK parameters were
used to predict dosing regimens in neonates and young infants that generated a high likelihood of achieving an
AUC0–24/MIC ≥400.
Methods
Patients and data collection
To construct the model, data were collected from neonates
and young infants in the neonatal intensive care unit (NICU)
at Children’s Hospital of Fudan University from May 2014
to May 2017. Additional patients with similar characteristics in our NICU from June 2017 to December 2017 were
enrolled for model evaluation. The study protocol was approved by the ethics committee of our hospital. Inclusion
criteria were as follows: age <61 days, sufficient intravascular access (either peripheral or central) to receive the
study drug, and suspected or confirmed gram-positive infection that necessitated treatment with vancomycin as part
of the standard of care following the decision of the attending physician. Participants were excluded if a complete vancomycin dosing history was not available, a diagnosis of
congenital kidney disease or major congenital heart disease
was made, a concentration was below the lower limit of
quantification (LLOQ), or any condition (receiving hemodialysis or extracorporeal membrane oxygenation during
vancomycin administration) was present which, in the opinion of the investigator, made the subject unsuitable for
enrollment.
For those meeting the enrollment criteria, the following
demographic and biochemical factors were collected: gestational age (GA), postnatal age (PNA), postmenstrual age
(PMA), birth body weight (BBW), body weight (BW), height
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Þ*BW ðkgÞ
(HT), body surface area [BSA, BSAðm2 Þ ¼ HT ðcm3600
],
serum creatinine (Scr), creatinine clearance rate [CLcr,
CLcr(mL/ min /1.73m2) = k ∗ HT(cm)/Scr(mg/dL), where k =
0.45 for term infants throughout the first year of life] [20],
and concomitant drug therapy.
For the representative MIC of MRSA isolates, pediatric
cultures at our hospital from 2014 to 2017 were reviewed.
Eur J Clin Pharmacol
Dosing regimen and sampling
The initial vancomycin dosage regimen for neonates and
young infants was 10 or 15 mg/kg (for bacteremia or meningitis, respectively) every 12 or 8 h, based on combinations of
PMA and PNA as listed in the Neofax® manual [21], with
further dosing guided by therapeutic drug motoring (TDM).
PMA is the primary determinant of dosing interval, with PNA
the secondary qualifier. Vancomycin was administered intravenously over 60 min. Blood samples were collected from all
patients for TDM as part of routine medical care. The trough
concentration was obtained 30 min prior to the fourth dose,
and the peak concentration was obtained 1 h after the initiation
of the 1-h infusion.
Assay of serum vancomycin
The serum vancomycin trough and peak concentrations were
determined using an enzyme-multiplied immunoassay method with the Viva-E System (ver. 2.014; Siemens Healthcare
Diagnostics, Eschborn, Germany). The linear range for the
assay was 2.0–50 mg/L, and the LLOQ was 2 mg/L.
Pharmacokinetic analysis
Data analysis was performed with the NONMEM software
program (ver. VII; Icon Development Solutions, Ellicott
City, MD, USA), in the R programming environment (ver.
14.2, http://www.r-project.org/). The first-order conditional
estimation (FOCE) method with interaction option was used
to estimate PK parameters and their variability. A onecompartment model with first-order elimination was used as
the PK base model. The one-compartment PK parameters
were clearance (CL) and apparent volume of distribution
(V). Interindividual variability was evaluated on CL and V
using an exponential model. To model the residual variability,
both additive and proportional error models were evaluated.
The demographic information was used to perform an initial selection of covariates. The selection was carried out by
plotting the parameter estimates against demographic factors,
and retaining those with statistical significance as initial covariates. It is well known that size (weight) and maturation
(age) have a significant impact on the CL of vancomycin in
newborn infants [22, 23]. Therefore, weight and age were
included in the model first. Because weight was proven to
be superior to age as a covariate for CL, models based on
BW scaling of the intrinsic clearance were tested using Eq. 1.
BW k1
CL ¼ CLstd ⋅
ð1Þ
70
where CLstd represents the clearance in an adult with a body
weight of 70 kg, and k1 is the exponent. Allometric scaling is
the most widely used to describe size differences and has a
strong theoretical basis with an exponent of 0.75 for clearance.
The exponent k1 was fixed to 0.75. Gestational age (GA),
postnatal age (PNA), and postmenstrual age (PMA) with linear, exponential and sigmoid Emax maturation function were
tested to explore the effect of maturational changes on vancomycin CL [23]. The effects of serum creatinine and creatinine
clearance on vancomycin CL were modeled, respectively, assuming an exponential relationship. The covariate model of
CL with creatinine clearance was finalized with the addition of
only PMA (not weight). It is assumed that weight is already
included in creatinine clearance.
The selection of covariates was determined using a forward
selection process and a backward elimination process. Nested
models were statistically compared using a likelihood ratio
test on the differences in the objective function value (OFV).
A reduction in OFV of 3.84 (p < 0.05) for forward inclusion
and an increase in OFV of 10.83 (p < 0.001) for backward
elimination were the criteria for retaining a covariate in the
model. The Akaike information criterion (AIC), calculated
using Pirana software (ver. 2.7.1; Pirana Software &
Consulting BV, http://www.pirana-software.com/), was used
to select competing, non-nested models. Models with lower
AICs were considered superior.
Model evaluation
The goodness of fit was evaluated using several diagnostic
scatter plots, as follows: (1) observed versus populationpredicted concentrations (DV vs. PRED); (2) observed versus
individual predicted concentrations (DV vs. IPRED); (3) conditional weighted residuals versus time (CWRES vs. TIME);
(4) conditional weighted residuals versus populationpredicted concentrations (CWRES vs. PRED).
The accuracy and stability of the final model was assessed
by means of an internal evaluation method involving a
non-parametric bootstrap [24]. Re-sampling of the data set
was carried out 1000 times using NONMEM in the final model. The values of estimated parameters such as the median and
SE from the bootstrap procedure were compared with those
estimated from the original data set. The model was proven to
be stable if the values of the parameters were not significantly
different.
The normalized prediction distribution error (NPDE) was
calculated to evaluate the predictive performance of the model
[25]. One thousand data sets were simulated based on the final
model. NPDE results were summarized graphically using the
NPDE R package. The NPDE distribution was expected to
follow a normal distribution (mean, 0; variance, 1).
To perform an external evaluation for the final vancomycin
model, we enrolled an additional 57 neonates and infants in
the study. The population-predicted concentrations were compared to the observed concentrations using the MAXEVAL =
Eur J Clin Pharmacol
0 option in NONMEM. The predictive performance of the
final model was evaluated by means of precision and bias.
The mean prediction error (MPE) and mean absolute prediction error (MAPE) were used as measures of precision and
bias [26]. These are calculated by the following equations:
concentrations were predicted when ≥90% of predicted
AUC0–24 was ≥ 400.
1 PREDi −OBS i
100%
∑
N OBS i
1 PREDi −OBS i
MAPE% ¼ ∑
100%
OBS i
N
Data analysis
MPE% ¼
where OBSi represents the observed concentration of the ith
subject, and PREDi represents the population-predicted concentration of the ith subject. In addition, the final model was
used to calculate the number of patients with MPE within
±20% and ±30%. The final model with low MPE and MAE
values and a high number of prediction errors within ±20%
and ±30% was considered acceptable.
Assessment of trough concentration and AUC0–24
relationship
One thousand data sets were simulated based on the final
model. Serum concentrations were obtained every 1 h after
steady-state achievement for each patient (after four doses had
been administered). The AUC0–24 was estimated from 0 to
24 h using the trapezoidal rule in GraphPad Prism (ver. 4.0;
GraphPad Software Inc., San Diego, CA, USA). The relationships between observed trough concentration and the proportion of subjects who achieved an AUC0–24/MIC ≥400 (for
0.5 mg/L, 1 mg/L or 2 mg/L MIC) were then examined.
Dosing optimization
The final PK model was utilized to identify a dosing regimen
that could reliably achieve the targeted ratio of an AUC0–24/
MIC ≥400. Monte Carlo simulations were used to simulate
vancomycin exposure for 1000 subjects randomly selected
from a database. The database contains information for all
neonates and young infants with suspected gram-positive infection (n = 21,078) discharged from our NICU from 2014 to
2017. The analysis was approved by the ethics committee of
our hospital without the need for written informed consent,
since the data were collected without patient identifiers.
Demographic ranges for BW, PMA, and SCR were included
when generating the random sample to match demographic
distribution of study subjects. Random numbers were generated from the uniform distribution using Excel (ver. 2013;
Microsoft Corporation, Redmond, WA, USA). The proportion
of simulated subject profiles that met an AUC0–24 ≥ 400 was
calculated for each dosing recommendation. In view of the
efficacy and toxicity in relation to concentrations, trough
Results
A total of 213 patients were enrolled in the study. The patient
characteristics are presented in Table 1. The final PK database
consisted of 330 vancomycin concentrations. Nine patients
were excluded due to LLOQ.
Population PK modeling
Preliminary analysis for the base model showed that the OFV
of the one-compartment model was 1545.04. Residual variability was best described by a combined proportional and
additive error model.
To account for size and maturation, six equations for clearance were evaluated (Table 2). Among the six models examined, the 0.75 allometric and sigmoidal model had the lowest
AIC (1288.637) and was employed for further covariate analysis. Postmenstrual age was superior to postnatal age. The
inclusion of weight and PMA for the prediction of CL in the
population PK model resulted in an ΔOFV of −270.403
(p < 0.01). The addition of weight scaled by allometry to V
also improved the model (ΔOFV, −34.98; p < 0.01). Although
weight and PMA were included in the model, the serum creatinine level was a significant predictor of vancomycin
(ΔOFV, −13.716; p < 0.01). The inclusion of weight, PMA
and serum creatinine to CL and weight to V produced the most
significant decrease in the OFV (ΔOFV, −319.099) and
between-subject variability (BSVS) for CL (ωCL decreased
from 0.61 to 0.27). All covariates passed the backward elimination criteria. The final vancomycin population PK model,
including parameter estimates and their relative standard errors (RSE), are given in Table 3.
Model evaluation
Diagnostic plots for the final vancomycin model showed a
good model fit (Fig. 1). The results of 1000 bootstrap replicates for vancomycin are summarized in Table 3. The number
of runs successfully converged was 951. The median parameter estimates from the bootstrap procedure were very close to
the values in the final population model. In addition, the parameters from the bootstrap procedure followed a normal distribution and contained all of the parameter estimates from the
final population model. The results indicate that the estimates
for the population PK parameters in the final model were
precise and that the model was stable. An internal model evaluation also demonstrated that the final model performed well
Eur J Clin Pharmacol
Table 1
Demographic data of patients
a
b
Characteristic
Model-building
data setb
External data set
No. of patients/samples
213/330
57/64
No. of trough/peak
concentrations
GA (weeks)
PNA (days)
213/117
57/7
36.9 [25–42]
26 [6–59]
38.7 [26.9–42]
14 [0–43]
PMA (weeks)
BBW (kg)
39.8 [28–47.9]
2.53 [0.7–4.7]
40.7 [29.2–46.4]
3 [1.1–5]
BW (kg)
2.73 [0.88–5.1]
2.9 [0.8–6]
HT (cm)
BSA (m2)
Scr (mg/dL)
CLcr (mL/min/1.73 m2)
49 [28–57]
0.19 [0.09–0.28]
49 [27–57]
0.20 [0.09–0.27]
0.28 [0.11–0.72]
68 [17–219]
0.34 [0.15–0.75]
57 [22–153]
a
GA, gestational age; PNA, postnatal age; PMA, postmenstrual age;
BBW, birth body weight; BW, body weight; HT, height; BSA, body
surface area; Scr, serum creatinine; CLcr, creatinine clearance rate,
CLcr(mL/ min /1.73m2 ) = k ∗ HT(cm)/Scr(mg/dL)
b
The data are presented as median [range]
in describing the observed data (Fig. 2). The mean NPDE was
0.07 (theoretical mean is zero), and there were no trends in
NPDE across time or predicted vancomycin concentrations.
Table 2
The external evaluation suggested that the final model accurately characterized the PK profile of vancomycin in the
population. Demographic and clinical characteristics of the
neonates and young infants in the external evaluation are
shown in Table 1. The plot of observed vancomycin concentrations versus population-predicted concentrations is shown
in Fig. 3. The MPE and APE were 12.3% and 31%, respectively. The percentage of population prediction error within
±20% and ±30% was 42.2% and 62.5%, respectively. All
validation parameters indicated good predictive performance
of the model in new patients.
Assessment of trough concentration and AUC0–24
relationship
Across the 213 neonates and young infants, the median
AUC0–24 was 299 (range, 142 to 659) mg·/h/L. The simulation
results indicate that the current dosing regimens based on
Neofax® produced effective therapeutic exposure for patients
in our hospital (where the MIC values for MRSA are 0.5 mg/L
or less). A comparison was performed between trough concentrations and AUC0–24 (Fig. 4). For MIC values of 0.5 mg/
L, AUC0–24/MIC ≥400 was achieved for 88% (56/64), 95%
(111/117) and 100% (32/32) of the newborn infants with
Effect of covariate analysis—impact of each covariate when added sequentially to the model
Covariates
Model description
OFV
AIC
ΔAIC
ωCL
η-Shrinkage (%)
Base model
Step 1: Size
3/4 Allometric model
BW exponential model
Maturation
One-compartment
1545.04
1563.04
0.000
0.61 (12.3)
7.2
CLstd ⋅ (BW/70)0.75
CLstd ⋅ (BW/70)k1
1358.671
1311.317
1376.671
1339.317
−186.369
−223.723
0.34 (14.8)
0.54 (19.6)
12.6
17.7
1274.637
1291.792
1354.676
1288.637
1303.792
1366.676
−274.403
−259.248
−196.364
0.27 (19.7)
0.28 (19.0)
0.28 (15.7)
17.8
17.5
12.5
CLstd ⋅ (BW/70)k1 ⋅ FMAT
CLstd ⋅ (BW/70)k1 ⋅ (PMA/39.8)EXP
CLstd ⋅ (BW/70)k1 ⋅ (PNA/26)EXP
1285.336
1291.790
1300.833
1301.336
1333.79
1314.833
−261.704
−229.25
−248.207
0.28 (19.3)
0.28 (18.7)
0.29 (19.5)
17.7
17.5
17.8
CLstd ⋅ (BW/70)0.75 ⋅ FMAT
V ⋅ (BW/70)
1239.657
1253.657
−309.383
0.28 (16.2)
12.5
CLstd ⋅ (BW/70)0.75 ⋅ FMAT ⋅
(SCR/0.28)EXPV ⋅ (BW/70)
1225.941
1241.941
−321.099
0.27 (16.6)
12.8
Step 2: 3/4 Allometric model based
Sigmoidal model
CLstd ⋅ (BW/70)0.75 ⋅ FMAT
PMA exponential model
CLstd ⋅ (BW/70)0.75 ⋅ (PMA/39.8)EXP
PNA exponential model
CLstd ⋅ (BW/70)0.75 ⋅ (PNA/26)EXP
BW exponential model based
Sigmoidal model
PMA exponential model
PNA exponential model
Step 3: V
3/4 Allometric model
Step 4: Renal function
SCR exponential model
OFV, objective function value; AIC, Akaike information criterion; ΔAIC, change in Akaike information criterion; ωCL, inter-subject variability of
clearance; CLstd, clearance in an adult with a body weight of 70 kg; BW, body weight; PMA, postmenstrual age; PNA, postnatal age; SCR, serum
creatinine; k1, exponent coefficient of BW; EXP, exponential function
FMAT = PMAHillCL /(PMAHillCL + TM50HillCL ), where TM50 is the PMA at which clearance is 50% of that of the mature value, and HillCL is the Hill
coefficient for clearance
Eur J Clin Pharmacol
Table 3 Parameter estimates of
the final vancomycin model and
bootstrap validation
Parameter
Final model
Population estimate
Bootstrap n = 1000
RSE (%)
Median
95% CI
Clearance (L/h): CL = θ1*(BW/70)**0.75*(PMA**θ4/(PMA**θ4 + θ3**θ4))*(SCR/0.28)** θ5
θ1
4.87
19.5
4.86
3.82–11.7
θ3
θ4
34.5
4.61
9.4
27.1
34.4
4.66
31.1–59.6
2.45–7.41
θ5
−0.221
Volume of distribution (L): V = θ2*(BW/70)
27.8
−0.223
−0.345 to −0.099
θ2
Inter-individual variability
40.7
4.6
40.8
37.2–44.8
26.8
16.6
26.4
22.1–31
23.9
0.688
12.4
44.9
23.6
0.659
20.8–26.4
0.397–1
CL (%CV)
Residual error model
Proportional (%CV)
Additive (mg/L)
RSE, relative standard error; CI, confidence interval
Fig. 1 Diagnostic plots of the vancomycin final model. a The observed
versus population-predicted concentration. b The observed versus
individual-predicted concentration. c Conditional weighted residual
versus time. d Conditional weighted residual versus the predicted
concentration
Eur J Clin Pharmacol
Fig. 2 Normalized prediction
distribution errors (NPDE) of the
vancomycin final model. a Q–Q
plot of the NPDE. b Histogram of
the NPDE. c NPDE versus time
after first dose. d NPDE versus
population-predicted concentration (PRED)
vancomycin troughs <5 mg/L, 5–10 mg/L and > 10 mg/L,
respectively. When the MIC increased to 1 mg/L, target attainment dropped to 6% (4/64), 15% (17/117), 14% (3/21) and
55% (6/11) of patients with trough concentrations of <5 mg/L,
5–10 mg/L, 10–15 mg/L and > 15 mg/L, respectively. For
MIC values of 2 mg/L, no infants achieved AUC0–24/MIC
≥400. It is worth noting that the lower vancomycin MIC in
our hospital helped to account for the increased numbers of
patients achieving an AUC0–24/MIC ≥400.
Discussion
In our study, vancomycin was well described by a
one-compartment model. This is explained in part by the fact
that the available data were scarce, since they derived from
routine TDM. For a patient population of neonates and infants, growth (body mass) and maturation (age) are linked
highly-related processes which may both influence the PK
Dosing optimization
Monte Carlo simulations performed with virtual patients randomly selected from a database showed that the AUC-guided
dosing regimen achieved AUC0–24 of ≥400 in ≥90% (a few
close to 90%) of simulated neonates and young infants
(Fig. 5a). The results indicated that a dose of at least 14 and
15 mg/kg every 12 h was required to attain the target AUC0–
24 of ≥ 400 in 90% of infants with a PMA of 30–32 and 32–
34 weeks, respectively. This target was also achieved in 93%
of simulated infants in the oldest PMA groups (36–38 and 38–
40 weeks, respectively) when the dosing interval was extended to 8 h. For infants with a PMA ≥44 weeks, an increase in
the dose to 18 mg/kg every 8 h was needed. The trough concentrations corresponding to an AUC0–24 of ≥400 were 5–
15 mg/L (Fig. 5b).
Fig. 3 Observed vancomycin concentrations versus population-predicted
concentrations for the external evaluation data set
Eur J Clin Pharmacol
Fig. 4 Scatter plot of initial vancomycin serum trough concentrations and
AUC0–24 (blue, black, pink and green represent troughs of <5 mg/L, 510 mg/L, 10-15 mg/L and > 15 mg/L, respectively)
parameters [27]. In this case, it is reasonable to first use allometric scaling to account for the influence of body size, and to
then conduct a covariate analysis using age-related factors to
explore the impact of maturation on PK parameters [28].
Although serum creatinine levels in the first few days of life
reflect maternal levels more than neonatal renal function [29],
based on principles of developmental pharmacology, covariates should include factors of size, maturational processes that
can affect drug transporters, and factors affecting kidney function [18]. In our study, BW, PMA and Scr were highly significant in the model. The typical population parameter estimate
of clearance for an infant weighing 2.73 kg with a PMA of
39.8 weeks and serum creatinine of 0.28 mg/dL was 0.103 L/
h/kg. This is very similar to the values of 0.110 and 0.118 L/h/
kg observed by Frymoyer et al. and Capparelli et al., respectively [14, 30]. The distribution volume found in our study
(0.58 L/kg) was also similar to that in previous reports [23,
30]. Both internal and external evaluation supported the prediction of the final vancomycin PK model.
However, in the external evaluation, the population prediction errors were a bit large. This can be explained by various
factors. First, the between-subject variability in clearance was
still significant (ωCL = 0.27) after inclusion of weight, PMA
and serum creatinine. Studies have postulated an impact of
protein binding and transporters on the large variability in
the extent of renal clearance [31, 32]. In addition, the unexplained variability remained significant, as the residual variability was 23.9%. This is likely because the data were based
on routine TDM.
A recent multicenter retrospective study by Ringenberg
et al. [33] investigated the achievement of therapeutic vancomycin trough serum concentrations (Ctrough) with dosing
guidelines from the NeoFax® guide. Of the 171 vancomycin
serum trough concentrations, only 25% of the neonates studied
achieved a target Ctrough of 10–20 mg/L with empiric dosing.
Similarly, in our hospital, based on Neofax® dosing recommendations, we found that only 12.7% of neonates and young
infants had troughs in the range of 10–20 mg/L, and 30% were
less than 5 mg/L, with most in the range of 5–10 mg/L, which
is consistent with our local trough target. Although an increased Ctrough is usually associated with a higher AUC0–24,
it is not always a good predictor of the AUC0–24, as it is highly
dependent on the dosing interval. Daily doses with longer dosing intervals were found to result in lower troughs. Therefore,
dose adjustments could not be predicted with precision for a
young infant based solely on Ctrough values. This highlights the
importance of considering the AUC0–24 and MIC when
treating invasive MRSA infections.
Effective vancomycin dosing is essential to providing optimal treatment and limiting the spread of resistance. Higher
Fig. 5 Monte Carlo simulation analysis examining AUC0–24 and trough achievement in neonates and young infants. a AUC-guided dosing regimen
achieved AUC0–24 of ≥400 in ≥90% (a few close to 90%) of patients. b Trough concentration corresponding to an AUC0–24 of ≥400
Eur J Clin Pharmacol
vancomycin doses have been recommended for invasive
MRSA infections in adults to maximize the odds of achieving
an AUC0–24/MIC of ≥400 [9]. Most hospitals have developed
local dosing recommendation in neonates. In the present
study, the vancomycin model that was developed was then
used to simulate dosing regimens in order to determine which
of these regimens could reliably generate the targeted AUC0–
24. This allows for more precise estimation of vancomycin
exposure against the MIC, providing for more accurate dose
adjustments to optimize vancomycin treatment. For neonates
and young infants in our hospital, attempts to increase vancomycin doses may not result in additional clinical benefit and
may increase the likelihood of toxicity. But it is clear that to
achieve the adequate AUC0–24/MIC target, the dosage of vancomycin should be increased if bacterial MIC increases. The
trough concentrations corresponding to an AUC0–24 of ≥400
were also examined across neonates and young infants of
different sizes, developmental ages and serum creatinine
levels. Our study suggests that Ctrough values of 5–15 mg/L
are highly predictive of an AUC0–24 ≥400 when treating invasive MRSA infections with an MIC of ≤1 mg/L, while the
Ctrough of 15–20 mg/L, which is recommended in adults, is
not necessary to achieve this target. This understanding would
be helpful for framing target trough concentrations in this
population, in whom MRSA infection is a concern.
One limitation of our study is that all the data were derived
from a single center, which could limit the generalizability of
the results. However, the clinical benefits of personalized
medical treatment for newborns are clear: the exposure target
can be reached earlier, and the number of TDM samples can
be potentially reduced. The next step is to encourage other
institutions to validate our final model at their institution with
their patient population, enabling a better understanding of
vancomycin exposure in each individual patient to allow for
dose optimization. In addition, the population model relied on
1–2 samples to determine the therapeutic AUC0–24 target. It is
critically important to collect a sufficient number of samples to
reliably calculate vancomycin AUC0–24.
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Conclusion
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In summary, vancomycin PK was well described by a onecompartment model, with size, PMA and renal function as
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to predict vancomycin exposure for this population. The model developed in this work can be used in hospitals to predict
individualized vancomycin dosing regimens for patient populations with similar characteristics based on AUC0–24/MIC.
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