Population pharmacokinetic model of Vancomycin based on therapeutic drug monitoring data in critically ill septic patients

Journal Pre-proof Population pharmacokinetic model of Vancomycin based on therapeutic drug monitoring data in critically ill septic patients

Tijana Kovacevic, Branislava Miljkovic, Pedja Kovacevic, Sasa Dragic, Danica Momcicevic, Sanja Avram, Marija Jovanovic, Katarina Vucicevic PII:

S0883-9441(19)30813-5

DOI:

https://doi.org/10.1016/j.jcrc.2019.10.012

Reference:

YJCRC 53405

To appear in:

Journal of Critical Care

Please cite this article as: T. Kovacevic, B. Miljkovic, P. Kovacevic, et al., Population pharmacokinetic model of Vancomycin based on therapeutic drug monitoring data in critically ill septic patients, Journal of Critical Care(2018), https://doi.org/10.1016/ j.jcrc.2019.10.012

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© 2018 Published by Elsevier.

Journal Pre-proof Population Pharmacokinetic Model of Vancomycin based on Therapeutic Drug Monitoring Data in Critically Ill Septic Patients Tijana

Kovacevic1,2* ,

Miljkovic3

Branislava

[email protected],

Pedja

Kovacevic1,2 [email protected], Sasa Dragic1,2 [email protected], Danica Momcicevic1,2 [email protected], Sanja Avram1 sanja.avram@ kc-bl.com, Marija Jovanovic3

University Clinical Centre of the Republic of Srpska, Dvanaest beba bb, Banja Luka 78000,

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1

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marijaj@ pharmacy.bg.ac.rs, Katarina Vucicevic3 katarina.vucicevic@ pharmacy.bg.ac.rs

Faculty of Medicine, University of Banja Luka, Save Mrkalja 14, 78000, Banja Luka, Bosnia

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Bosnia and Herzegovina

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and Herzegovina

Department of Pharmacokinetics and Clinical Pharmacy, Faculty of Pharmacy, University of

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Belgrade, Vojvode Stepe 450, 11221 Beograd, Republic of Serbia

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*Corresponding author:

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Tijana Kovacevic, MPharm, MSc., Ph.D. University Clinical Centre of the Republika Srpska Dvanaest beba bb.

78000 Banja Luka, Bosnia Herzegovina [email protected]

Conflict of Interest None. Funding sources None Word count: 2655

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Journal Pre-proof

Journal Pre-proof Abstract

Purpose: The present study aimed to establish a population pharmacokinetic model of vancomycin, including adult critically ill septic patients, with normal and impaired renal function. Materials and Methods: A prospective analysis of 146 concentrations from 73 adult critically

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ill septic patients treated with 1-h intravenous infusion of vancomycin were included in the

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study. A nonlinear mixed effects modelling (NONMEM) approach was applied for data analysis

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and evaluation of the final model. The influence of creatinine clearance calculated by the

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Cockcroft-Gault equation (CrCl), and other potential covariates on vancomycin clearance (CL) were evaluated.

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Results: The final one-compartment pharmacokinetic model includes the effect of CrCl on CL.

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Population pharmacokinetic values for a typical subject were estimated at 0.024 l/h for CL dependent on renal function (CLCrCl), 1.93 l/h for residual portion of CL (not dependent on

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renal function), and 0.511 l/kg for volume of distribution (V). According to the final model, for

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patients with CrCl=120 ml/min, the median vancomycin total CL is 4.81 l/h, while CrCldependent fraction accounts for approximately 60% of CL. Conclusions: The developed population vancomycin model may be used in estimating individual CL for adult critically ill septic patients, and could be applied for individualizing dosage regimens taking into account the continuous effect of CrCl.

Keywords: creatinine clearance, NONMEM, renal function, antibiotics, TDM.

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Journal Pre-proof Introduction Vancomycin, a glycopeptide antibiotic, is used to treat systemic infections, especially caused by methicillin resistant Staphylococcus aureus (MRSA)1 . The drug shows bactericidal activity against Gram positive bacteria, inhibiting cell wall synthesis. It is indicated for complicated infections, community and hospital acquired pneumonia, infective endocarditis, as well as intrahospital sepsis. Since inappropriate dosing may lead to treatment failure and

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increased risk of resistance, therapeutic drug monitoring (TDM) of vancomycin represents an

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integral part of medication therapy management especially in critically ill septic patients 2 .

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However, blood samples not obtained at the anticipated time(s) after dosing and inadequate

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interpretation of the measured concentrations can lead to unnecessary dose changes causing a suboptimal response or adverse reactions. Consequently, this results in increased healthcare costs

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as well3 . Therefore, uniform guidelines regarding TDM and vancomycin dosing are needed.

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Current TDM guidelines indicate that adequate vancomycin efficacy is reached if the value of the area under the concentration-time curve for 24 h divided by the minimum inhibitory

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concentration (AUC24/MIC) is equal to or greater than 400 4,5 . However, in hospital settings,

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especially in intensive care units, a more practical way of vancomycin monitoring is using trough concentrations, since monitoring AUC 24 requires multiple blood samples per patient6 . In addition, it is found that trough concentration of 15-20 mg/l is surrogate marker of the parameter AUC24/MIC targeting value ≥400, when MIC≤1 mg/l. 3. Vancomycin is administered intravenously and shows linear pharmacokinetics in the therapeutic dose span. The volume of distribution (V) is in the range of 0.4-1 l/kg, while protein binding ranges from 10 to 50%. More than 80% of a vancomycin dose is excreted unchanged in urine within 24 hours after administration7,8 . In adults with normal renal function, elimination

Journal Pre-proof half-life (t1/2 ) is from 4 to 6 hours, while in patients with severe renal impairment, it could be prolonged up to 7 days. The median reported value of plasma clearance (CL) is 0.058 l/h/kg9 . However, its pharmacokinetic profile in elderly, paediatric, critically ill and renal impairment patients is highly variable and differs from adults with normal renal function9 . Critically ill patients are at an increased risk of life-threatening infections; hence, the

pathophysiological

changes

regimen

and

broad

is

required variability

as in

soon as possible. this

vulnerable

of

optimal antimicrobial dosage

In addition,

population

further

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complicates the optimisation of the vancomycin dosage regimen. Hence, increased V and altered

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CL of hydrophilic antibiotics have been previously observed, leading to lower drug levels and potentially sub-dosing of vancomycin10,11 . Because vancomycin is predominantly eliminated via

glomerular

rate

(estimated

from routine

serum creatinine measures).

an acute kidney injury often accompanies sepsis in intensive care units.

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Additionally,

filtration

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reduced

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kidneys, the patient’s renal function has to be carefully considered. Aging is associated with a

Vancomycin itself can cause decrease in renal function as well. Therefore, vancomycin TDM is

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highly recommended in critically ill patients in order to ensure maximum efficacy and safety2 .

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However, to the best of our knowledge, there is no critically ill-specific vancomycin TDM guidelines. Although there are a number of population pharmacokinetic models of vancomycin, a recent review indicates rather scarce models in critically ill patients with, interindividual pharmacokinetic variability2 . Therefore, the main goal of this study was to create a population pharmacokinetic model of vancomycin for adult critically ill septic patients, with normal and impaired renal functions. More specifically, the aim was to quantify the effect of creatinine clearance (CrCl) as a continuous covariate and other potential factors on the pharmacokinetic parameters for the importance of optimisation of vancomycin dosing in routine patient care.

Journal Pre-proof

Material and methods Patients and blood sampling This was a prospective study, which included adult critically ill septic patients, not requiring renal replacement therapy, to whom administration of vancomycin was indicated.

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Exclusion criteria were: age<18 years, pregnancy, non-septic patients, acute or chronic renal

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insufficiency treated with renal replacement therapy, and termination of vancomycin therapy

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before a steady-state was reached. Acute kidney injury and chronic kidney disease were defined

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using RIFLE, AKIN and KDIGO classifications while surviving sepsis campaign was the main

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tool for identification of septic patients. The study was performed at the Medical intensive care unit, in the University Clinical Center of Republika Srpska (UCC RS), Bosnia and Herzegovina

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during a 10-month period. The study protocol and patient information were approved by the Ethics Committee of the UCC RS (01-9-282.2/13).

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Patients were treated with 1 g of vancomycin every 12 hours as 1-h IV infusion. A fixed

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vancomycin loading dose of 2g was recommended by a clinical pharmacist involved in morning rounds at MICU since it was suitable for most patients. Patients’ weight cannot be precisely measured at our clinic. The average weight of patients obtained from family physicians’ electronic medical records

and patients’ closest family members was 78.11 kg. Given the fact

that the recommended vancomycin loading dose for the treatment of critically ill patients is 2530mg/kg, the loading dose of 2g is in the range of 1.937 -2.325g, as calculated for the average weight of studied patients. Physicians who were treating these patients decided on whether or not the vancomycin loading dose should be administered. Blood samples were taken from patients

Journal Pre-proof during the routine TDM and laboratory personnel recorded exact sampling times. Two blood samples (3-5 ml) were obtained from each patient: trough samples prior to the 4 th dose and 1 h after the administration of the 4th dose of the drug (1g of vancomycin). Both samples were immediately (within 2 h of obtaining the specimen), transferred to the central laboratory of the UCC RS. Samples (plasma) were frozen until analysis. Vancomicyn concentration was measured on Cobas® analyzer (Roche/Hitachi Cobas c systems, Cobas c 501) with a calibration range

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between 1.7-80 mg/l. Blood samples exceeding the upper limit of quantification were diluted

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according to the manufacturer’s protocol.

(gender,

age,

comorbidities,

biochemical

body

weight,

parameters

height),

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characteristics

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Medical records were used to collect all the necessary patient data: demographic

for

assessing

diagnosis liver

and

and

history kidney

of disease, function,

and

Pharmacokinetic analysis

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characteristics of vancomycin therapy and concomitant medications.

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Data analysis was performed by a nonlinear mixed-effects modeling approach using

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NONMEM® software (version 7.3, Icon Development Solutions, Ellicott City, MD, USA). Model building steps and graphical presentation were performed by Perl speaks NONMEM ® (version

4.6.0,

https://uupharmacometrics.github.io/PsN/),

Xpose ®

(version

4,

http://xpose.sourceforge.net/), R® (version 3.3.1, http://r-project.org/) and Pirana® (version 2.9.7, http://www.pirana-software.com/). First, analysis was performed in order to develop the base model of vancomycin pharmacokinetics. Therefore, one- and two-compartment models were tested for describing the concentration-time data. Parameter estimation was assessed by the first-order conditional

Journal Pre-proof estimation method with interaction (FOCEI), while interindividual variability in pharmacokinetic parameters was described by an exponential model. For residual intraindividual variability of vancomycin concentration additive, proportional and combination error models were tested. Once the base model was developed, the influence of covariates on vancomycin CL was evaluated. Covariates considered for inclusion were patients’ creatinine clearance calculated by Cockcroft-Gault equation (CrCl), liver enzymes (aspartate and alanine aminotransferase), total proteins,

and

co-therapy

with

nonsteroidal

anti-inflammatory

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plasma

drug.

Statistical

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significance of the covariates was evaluated based on the objective function value (OFV). Each

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covariate was tested against the base/previous model and the covariate with the highest drop in

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the OFV [of at least 3.84 (p<0.05, df =1)] was included. The full model was obtained when the effects of all the remaining covariates were insignificant, while the final model was determined

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by backward elimination of covariates from the full model. The criterion for retaining a

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covariate(s) was an increase in OFV of at least 6.63 (p<0.01, df=1) at removal. Models were considered to be acceptable when minimization and covariance steps were

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successful, number of significant digits greater than 3, and gradients in the last iteration were

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between 10-3 and 102 . Additionally, the absence of substantial η- and ε-shrinkage was considered12 . Model appropriateness was evaluated using several diagnostic scatter plots, including the agreement between the observed

and individual or population predicted

concentration, and uniformity of the distribution of conditional weighted residuals (CWRES) vs the predicted concentration13,14 . Moreover, the nonparametric bootstrap technique including 1,000 bootstrap replicates was used to assess the precision of the final model parameter estimates15,16 . Finally, a prediction- and variability-corrected visual predictive check (pvcVPC)

Journal Pre-proof was performed in order to compare observed data and prediction intervals based on the simulated data17 .

Results The study included 73 (out of 80 screened) patients hospitalised in the UCC RS. The characteristics of patients are presented in Table 1. In 16 patients, vancomycin loading dose of

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2,000 mg was administered as a 2-h infusion and the others received 1g of vancomycin.

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Maintenance dose was 1g every 12h administered as a 1-h IV infusion for all patients. A total of

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146 vancomycin concentrations were within range 0.74-105.4 mg/l. The mean value of trough

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levels was 16.41±1.72 mg/l with 52.05% being between 10-20 mg/l. In total, 45.95% of

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vancomycin trough levels were below 10 mg/ml.

A one-compartment model with first order elimination was used as a structural model

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(ADVAN1/TRANS2 PREDPP subroutine). The estimated pharmacokinetic parameters were CL and V. The interindividual variability of CL was described by an exponential model, whereas the

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proportional error model was selected for describing residual intraindividual variability of

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vancomycin concentration.

The developed base model was further used to assess the influence of covariates on vancomycin CL. The inclusion of CrCl resulted in a statistically significant decrease in OFV (ΔOFV=15.532, p<0.001) compared to the base model. In addition, the inclusion of covariates into the base model decreased the interindividual variability in CL. Effects of all other tested covariates were insignificant and were not integrated in the final model.

Journal Pre-proof Parameters’ estimates of the final model and the results of the bootstrap analysis based on successful runs are presented in Table 2. Population pharmacokinetic model of vancomycin is described by the following equations: .

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V(l)=0.511∙WT

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CL(l/h)=0.024∙CrCl+1.93

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Based on the results, for median CrCl of 75 ml/min, vancomycin CL is 3.73 l/h, while CL CrCl-

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dependent portion equals 1.8 l/h.

Diagnostic plots were used as a tool in assessing the predictive performance of the final

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model. Scatter plots of the observed vs individual/population predicted concentrations for the

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final model are presented on Figures 1 and 2. The plot of CWRES vs predicted concentration (Figure 3) showed that the CWRES were mostly within ±2 units of the null ordinate and

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uniformly distributed. Finally, the performance of the final model was also evaluated by the

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pvcVPC presented on Figure 4. Although minor overlaps in confidence intervals based on simulations may be noticed, the observed median and percentiles were mostly within the 95% confidence intervals of the simulations. The Vancomycin “concentrations vs time” profile after 1-h infusion of 1,000 mg/12 h based on typical population parameters for patients with mild, moderate, and severe reduction in estimated glomerular filtration rate is given in Figure 5.

Journal Pre-proof Discussion A mono-exponential model was suitable for describing our “concentration vs. time” data of vancomycin. It is in compliance with previously reported models11,18,19 . The effect of body weight on V was included at the beginning of the modeling process during the development of the base model. The final pharmacokinetic model includes the influence of CrCl on vancomycin CL. Population parameters’ values according to the final model for a typical subject were

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estimated at 0.024 for CLCrCl, 1.93 for residual portion of CL (not dependent on renal function)

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and 0.511 l/kg for V (Table 2). Observed values are in line with ranges of CL (0.031 - 0.086

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l/h/kg) and V (0.388 - 2.040 l/kg) previously reported in adults20 .

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Previous pharmacokinetic population models predominantly included CrCl as the most influential covariates on vancomycin CL2,11,18,20 . Accordingly, our analysis showed a reduction

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of vancomycin CL with a decrease of CrCl calculated using the Cockcroft-Gault equation.

model.

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Hence, OFV was decreased by 15.53 (p<0.001) units when renal function was included in the

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The final model allows for the quantification of the effect of a wide range of CrCl

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(normal and decreased renal function) as a continuous covariate in critically ill adult but also elderly patients. In fact, 39.7% of the studied population consisted of patients over the age of 65. According to the results of the final model, when CrCl equals to the median value of 75 ml/min, vancomycin CL is 3.73 l/h, while CrCl-dependent part is 1.8 l/h. On the other hand, for CrCl of 15 and 45 ml/min, vancomycin total CL is 2.29 and 3.01 l/h, respectively (Figure 5). While most previous population studies used CrCl20 , Colin et al. used serum creatinine to describe the influence of renal function on vancomycin elimination. They built a pooled populationpharmacokinetic model based on data from 14 studies in different patient populations. The final

Journal Pre-proof model describes size-related, maturational, age-induced and serum creatinine-related changes in vancomycin CL. An increase in serum creatinine leads to a decrease in vancomycin CL, which is in line with our results21 . Interestingly, in the research by Garcia et al. using a standard population two-stage approach, besides CrCl, in addition to renal function estimated by Levey formula, age, APACHE II score and serum albumin were included in the vancomycin CL in intensive care unit patients18 . In order to calculate CrCl, patients’ gender, body weight, age and

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serum creatinine are required. In our analysis, inclusion of CrCl in the model resulted in a greater

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decrease in OFV compared to an independent age effect. Furthermore, the CrCl calculation using

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the Cockcroft-Gault takes age into account. Hence, the model may be over-parametrised.

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Therefore, the independent impact of these covariates seems to be not justified. Assessment of the remaining biochemical parameters, and co-therapy with nonsteroidal

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anti-inflammatory drugs as potential covariates that affect vancomycin CL, did not lead to a

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significant change in OFV, and therefore were not included in the final model. One of the main limitations of this study is that it was performed in less than ideal

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conditions (Bosnia and Herzegovina is post-war and low to middle income country). At the

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moment of conducting this study, MICU was newly established and SAPS II and APACHE two was not regularly calculated as well as bilirubin and INR.

Conclusion The population pharmacokinetic model of vancomycin was developed using data obtained from adult critically ill septic patients. The final population model describes and quantifies

the

influence

of

CrCl

on

vancomycin

CL,

which

influences

vancomycin

concentrations, and is required for optimizing the dosage regimen. Therefore, the developed

Journal Pre-proof population pharmacokinetic model of vancomycin can contribute to therapy individualisation of older critically ill patients. More specifically, it can contribute to the assessment of the dosage regimen in order to achieve the desired concentrations of the drug in patients with normal and

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impaired renal function.

References 1.

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toxicity: Critical review of the clinical practice. Clin Exp Pharmacol Physiol 2019. doi: 10.1111/1440-1681.13066

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J.F. Monteiro, S.R. Hahn, J. Goncalves, P. Fresco. Vancomycin therapeutic drug

monitoring and population pharmacokinetic models in special patient subpopulations. Pharmacol Res Perspect 2018;6:e00420. 3.

J.J.B Seng, M.H.A Yong, Z.X. Peh, J.L. Soong, M.H. Tan. Appropriateness of

vancomycin therapeutic drug monitoring and its outcomes among non-dialysis patients in a tertiary hospital in Singapore. Int J Clin Pharm 2018;40:977-81. F. Elbarbry. Vancomycin dosing and monitoring: critical evaluation of the current

M. Rybak, B. Lomaestro, J.C. Rotschafer, et al. Therapeutic monitoring of vancomycin in

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Infectious Diseases Society of America, and the Society of Infectious Diseases Pharmacists. Am

based vancomycin dosing. Am J Health Syst Pharm 2018;75:1986-95. M.J. Rybak. The pharmacokinetic and pharmacodynamic properties of vancomycin. Clin

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Infect Dis 2006;42 Suppl 1:S35-9.

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infusion. 05/06/2018. https://www.medicines.org.uk/emc/product/6255/smpc. Accessed January 2019.

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critically ill patient - concepts appraised by the example of antimicrobial agents. Adv Drug Deliv Rev 2014;77:3-11. 11.

J.A. Roberts, F.S. Taccone, A.A. Udy, J.L. Vincent, F. Jacobs, J. Lipman. Vancomycin

dosing in critically ill patients: robust methods for improved continuous-infusion regimens. Antimicrob Agents Chemother 2011;55:2704-9. R.M. Savic, M.O. Karlsson. Importance of shrinkage in empirical bayes estimates for

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2007;82:17-20.

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the validation of nonlinear mixed-effects population models. Comput Methods Programs Biomed

based drug development - part 2: introduction to pharmacokinetic modeling methods. CPT Pharmacometrics Syst Pharmacol 2013;2:e38. 17.

M. Bergstrand, A.C. Hooker, J.E. Wallin, M.O. Karlsson. Prediction-corrected visual

predictive checks for diagnosing nonlinear mixed-effects models. AAPS J 2011;13:143-51. 18.

M. del Mar Fernandez de Gatta Garcia, N. Revilla, M.V. Calvo, A. Dominguez-Gil, A.

Sanchez Navarro. Pharmacokinetic/pharmacodynamic analysis of vancomycin in ICU patients. Intensive Care Med 2007;33:279-85.

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life:

P.J. Colin, K. Allegaert, A.H. Thomson, et al. Vancomycin pharmacokinetics throughout results

from

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pooled

population

analysis

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evaluation

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recommendations. Clin Pharmacokin 2019. doi: 10.1007/s40262-018-0727-5.

current

dosing

Journal Pre-proof List of figures

Figure 1 - Observed vancomycin concentrations (DV) vs individual predicted concentrations (IPRED) for the final model with regression line (dashed).

Figure 2 - Observed vancomycin concentrations vs population predicted concentrations (PRED)

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for the final model with regression line (dashed).

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Figure 3 - Conditional weighted residuals (CWRES) vs predicted concentration (PRED) for the

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final model of vancomycin. Line of identity (solid); regression line (dashed).

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Figure 4 - Prediction- and variability-corrected visual predictive check (pvcVPC) of the final

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model; prediction- and variability-corrected vancomycin concentrations vs time. Solid and dashed lines represent the median, 5th and 95th percentiles of the observed data, with shaded

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confidence intervals of the simulation based prediction intervals.

Figure 5 – Vancomycin concentrations vs time profile after 1-h infusion of 1,000 mg/12 h based on typical population parameters for patient with mild (green line), moderate (pink line) and severe (blue line) reduction in estimated glomerular filtration rate.

Journal Pre-proof Table 1 Demographic and biochemical patients’ data Number (%)/

Median

Characteristic

Mean ± Sd

(Range)

Gender

Male

40 (54.8%)

Female

33 (45.2%) 60 (20 – 87)

56.9 ± 17.0

WT (kg)

78.2 ± 14.2

HT (cm)

174 ± 9.31

BMI (kg/m2 )

25.7 ± 4.14

SECr (µmol/l)

109 ± 72.8

90.3 (33 – 419)

CrCl (ml/min)

80.0 ± 44

75 (14.28 – 192.9)

26.5 ± 3.78

27.5 (18 – 36)

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80 (30 – 120)

175 (145 – 198) 25.0 (14.3 – 41.5)

54.9 ± 8.81

54.5 (40.1 – 83.5)

73.3 ± 148

40.1 (12 – 1044)

74.7 ± 193

28 (5 – 1355)

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ALT (U/l)

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Total plasma protein (g/l)

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Albumin (g/l)

AST (U/l)

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Age (years)

Sd, standard deviation; WT, weight; HT, height; BMI, body mass index; SECr, serum creatinine; CrCl, creatinine clearance; AST, aspartate aminotransferaze; ALT, aspartate aminotransferase.

Journal Pre-proof Table 2 Population pharmacokinetic parameters of the final model and bootstrap analysis Original dataset Median

95 % CI

θCLCrCl

0.024

0.0245

0.0132 - 0.0371

θCLRES

1.93

1.853

0.986 - 2.75

θV

0.511

0.506

IIVCLCrCl (CV%)

56.6

52.8

Wp (%)

34.5

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Estimated value

34.7

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Parameter

Bootstrap

0.358 - 0.631 27.1 - 77.1 26.8 - 45.4

CI, confidence interval; θCL CrCl, typical value of creatinine clearance-dependent fraction of CL; θCL RES, typical

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value for residual CL, none creatinine clearance-dependent; θV, typical value for V; IIVCLCrCl, interindividual

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variability of CLCrCl; CV, coefficient of variation; Wp, proportional error.

Journal Pre-proof Highlights: 

The population pharmacokinetic model of vancomycin was developed for adult critically ill septic patients.



A mono-exponential model is suitable for describing “concentration vs. time” data of vancomycin.



The developed population pharmacokinetic model of vancomycin can contribute to

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The developed model can contribute to achieving therapeutic drug concentrations in

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critically ill patients.

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

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therapy individualisation.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5