Evaluation of Current Vancomycin Dosing Practices and Pharmacokinetic Neonatal Infant Models with Therapeutic Drug Monitoring Data from a Pediatric Population Craig M. Comisar, Bhuvana Jayaraman, Jeffrey S. Barrett Laboratory for Applied PK/PD, Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia; Philadelphia, PA BACKGROUND

•

Vancomycin General Information:

• A glycopeptide that is the first-line treatment for coagulase-negative

Staphyloccus aureus

infections involved in children.

Staphylococci

and • Therapeutic drug monitoring (TDM) is used in the clinical setting because high levels are associated with nephrotoxicity while underdosing can lead to bacterial resistance and ineffective treatment.

• The drug is almost entirely renally cleared as parent compound.

•

Vancomycin Dosing at Children’s Hospital of Philadelphia (CHOP):

• Dictated by an institution specific version of the Lexi-Comp’s Pediatric Dosing Guide (5) based primarily on patient postnatal age and weight. Although glomular filtration rate roughly follows median glomular filtration rate, the range of glomular filtration rates among children is highly variable (Fig. 1).

• Clinicians alter dosing to achieve vancomycin trough concentrations between 8 and 15 m g/mL by the third dose, with adjustments made for low/high trough levels or evidence of renal impairment (sudden spikes of serum creatinine).

• Current clinician driven dosing strategies result in inconsistent achievement of even the lowest acceptable (5 m g/mL) vancomycin trough levels days into the treatment regimen (Fig. 2). This is most likely due to a combination of physician underdosing (

47% of doses were under the recommended amount

), systematic underdosing guidance, and inaccessibility of vancomycin pharmacokinetic data.

CHOP Dosing (less than 1000 gm Birth Weight) 100% 150% CHOP Dosing (1000-2000 gm Birth Weight) 130% 90% 110% CHOP Dosing (greater than 2000 gm Birth Weight) Median Glomular Filtration Rate for children >34 weeks Gestational Age 80% 90% 70% 50% 30% 10% 0 70% 60% 100 200

Postnatal Age (days)

300 50% 3 • Several empirical models have been proposed to aid dosing of individual neonatal vancomycin patients using various covariates (weight, age, creatinine concentration, etc.).

Authors

de Hoog et al.

n # obs Postnatal

108 0-29 days (1) Grimsley et 59 347 0-76 days al. (2) Capparelli et al. (3) 374 1103 0-730 days Anderson et al. (4) 214 604 1-27 days

Dosing Covariates in the final model

Weight Weight, Creatinine Concentration Weight, Creatinine Concentration Postnatal Age, Gestational Age Weight, Creatinine Concentration, Postmenstrual Age, Presence of a Ventilator, Inotrope Levels Current Study 444 1611 1-417 days Additional demographic information- Weight: 0.45-12 kg, Gender Distribution: 73% male •Figure 3 shows the mean concentration vs. time profile for a simulated patient in which the patient is appropriately dosed (trough concentrations between 8-15 ug/mL) using the Lexicomp guidelines (5).

The models show very good agreement in trough predictions.

This is most likely due to a near full term child concentration.

and stable creatinine •Figure 4 shows the mean concentration vs. time profile for a simulated patient in which the patient fails to achieve the vancomycin trough target concentration ug/mL) using the (8-15 Lexicomp guidelines (5).

very good The models show agreement in trough predictions though the Anderson et al. and Capparelli et al. data show slightly higher trough concentrations.

This is most likely due to those models ability to account for early gestational age patients.

•Figure 5 shows the mean concentration vs. time profile for a simulated patient in which the patient has greatly increasing serum creatinine levels representing a loss of kidney function and lowered drug clearance. The models provide large differences in this scenario. DeHoog et al.

don’t include creatinine in their model and the functional form of creatinine is different in the other models resulting in a different response to the creatinine spike.

OBJECTIVES

•To utilize published models to evaluate the clinical performance of the current dosing recommendations at Children’s Hospital of Philadelphia (CHOP) across a strata of dosing regimens using simulations of ideally dosed patients.

•To determine the suitability of published neonatal vancomycin models to evaluate CHOP therapeutic drug monitoring (TDM) data.

SIMULATION RESULTS

30 25 Grimsley Model Anderson Model deHoog Model Capparelli Model 20 15 10 5 0 0 25 20 Grimsley Model Anderson Model deHoog Model Capparelli Model 15 10 30 20 10 5 0 0 60 50 Grimsley Model Anderson Model deHoog Model Capparelli Model 40 0 0 50

time (hr)

100 50 100

time (hr)

50 100

time (hr)

150 150 150 Patient #1 Dosing: b.i.d. and t.i.d.

Weight: 3.27 kg Gestational Age: 38 wks Postnatal Age: 4-11 days Creatinine Conc: 0.5-0.6 mg/dL Patient #2 Dosing: q.d.

Weight: 0.795 kg Gestational Age: 25 wks Postnatal Age: 7-14 days Creatinine Conc: 0.7-1.0 mg/dL Patient #3 Dosing: b.i.d.

Weight: 1.9 kg Gestational Age: 31 wks Postnatal Age: 18-25 Days Creatinine Conc: 1.0-3.0 mg/dL

FITTED MODEL RESULTS

Authors

deHoog et al. (1) Grimsley et al. (2) Capparelli et al. (3) Anderson et al. (4)

Number of parameters 5 5 12 11 AIC from parameters fixed to published results AIC from the parameters optimized for CHOP data 1077 503 637 519 479 478 1661 484

CHOP therapeutic drug monitoring data was fit using the published models. Model fit was evaluated using the akaike information criterion (AIC), a least squares analysis adjusted to the number of fitted parameters.

When the parameters were fixed, the Grimsley and Capparelli models fit the CHOP data most precisely.

The large differences in the fits are most likely due to two main factors. TDM data includes mostly trough vancomycin levels versus a rich data set which could encompass a more valid pharmacokinetic profile. Additionally the data from the TDM data did not include any information on inotrope levels or ventilator presence.

Postmenstural age/gestational age was also estimated using other covariates. When parameters were allowed to optimize, all but deHoog et al. model, which did not include creatinine concentration, fitted the data equally well.

METHODS

•Eight CHOP patients representing every one of the different CHOP dosing regimens shown in figure 2, were simulated using NONMEM. The patients’ real demographic information (age, weight, visit history) and serum creatinine levels were used in the simulations.

1000 simulations were run for each individual using the structural models and parameters proposed by the authors listed above. Dosing was set to be the exact dosing schedule recommended by the Lexi Comp’s Pediatric Dosing Guide for CHOP (5). Simulations were set to collect data in a one week period of intravenous vancomycin treatment.

•Simulation results were evaluated to observe deviations from the target trough serum vancomycin concentrations and to look at simulation performance between the various published models.

•The structural models were then fit to actual CHOP TDM data using NONMEM using two scenarios. The first fit fixed parameters to the published results and the second fit allowed parameters in the various model to optimize to the CHOP data.

CONCLUSIONS

• Current non-modeling based vancomycin dosing practices at CHOP result in poor achievement of target vancomycin trough concentrations.

• Simulation of vancomycin dosing identified potential underdosing situations in pediatric patients following Lexi-Comp guidelines.

• When optimized in NONMEM, all models except the deHoog et al. model fit the vancomycin therapeutic drug monitoring data with equal precision.

Published models parameters should only be used when all covariate information is available.

REFERENCES

(1) M. de Hoog, R. C. Schoemaker, J. W. Mouton, and J. N. van den Anker,

“

Vancomycin population pharmacokinetics in neonates”,

Clinical Pharmacology & Therapeutics, 2000,

(2) C. Grimsley 67(4), 360-367.

and A. H. Thomson, “Pharmacokinetics and dose requirements of vancomycin in neonates”, (3) E. V. Capparelli, J. R. Lane, G. L. Romanowski, E. J. McFeely, W. Murray, P. Sousa, C. Kildoo Arch Dis Child Fetal Neonatal Ed, 1999, 81

,

F221 –F227.

and J. D. Connor, “The influences of renal function and maturation on vancomycin elimination in newborns and infants”,

J. Clin. Pharmacol, 2001, 41, 927-934.

(4) B. J. Anderson, K. Allegaert, J. N. Van den Anker, V. Cossey and N. H. G. Holford , “Vancomycin pharmacokinetics in preterm neonates and the prediction of adult clearance

”, Br J Clin Pharmacol, 2006,

63

,

75 –84.

(5) Lexi-Comp Online, Pediatric Lexi-Drugs Online, Hudson, Ohio: Lexi-Comp, Inc.; 2004; May 30, 2008. http://www.crlonline.com/crlsql/servlet/crlonline

Therapeutic Drug Monitoring (TDM) Data from 8 CHOP patients representing the major different Lexi-Comp dosing categories (Fig. 2)

• Demographic Information (Sex, Weight, Postnatal Age, Height, etc.) • Creatinine Serum Concentrations • Vancomycin Serum Concentrations

Dosing

Simulated assuming complete adherence to Lexi-Comp guidelines (5)

Simulation Objectives

•

Test efficacy of Lexi-Comp (5) to produce required trough

•

concentrations in a patients Evaluate model performance in different types of patients

Model Fit Objectives

•

Compare the “off-the-shelf” utility of

•

each model (parameters unchanged) Compare the CHOP-optimized fit of each model

• •

Tested 4 models using NONMEM 1000 simulations were run for each

•

individual Simulations were set to collect data in a one week period

• •

Tested 4 models using NONMEM Fixed parameter and CHOP-optimized fits were run separately

Dosing

Actual from clinical records

Findings

Simulated assuming complete adherence to Lexi-Comp guidelines (5)

Findings

Actual from clinical records Therapeutic Drug Monitoring (TDM) Data from 8 neonatal CHOP patients representing the major different Lexi-Comp dosing categories (Fig. 2)

• Demographic Information (Sex, Weight, Postnatal Age, Height, etc.) • Creatinine Serum Concentrations • Vancomycin Serum Concentrations

Fitted Model Objectives

• Compare the “off-the-shelf” utility of each model (parameters unchanged) • Compare the CHOP-optimized fit of each model

Simulation Objectives

• Test efficacy of Lexi-Comp (5) to produce target trough concentrations in patients • Evaluate model performance in different types of patients

Dosing

Actual from clinical records • Tested 4 models using NONMEM • Fixed parameter and CHOP optimized fits were run separately

Findings

• Compared how well each model fit CHOP data by analyzing the akaike information criterion (lower value indicates better fit) • Tested 4 models using NONMEM • 1000 simulations were run for each individual • Simulations were set to collect data in a one week period

Dosing

Simulated assuming complete adherence to Lexi-Comp guidelines (5)

Findings

• Simulated vancomycin troughs from each model and compared results to target trough levels • Compared the predicted vancomycin levels between the different models • Simulation results were evaluated to observe deviations from the target trough serum concentrations and to look at simulation performance between the various published • Fixed parameter model results allowed for comparison of the “off-the-shelf” utility of • CHOP optimized parameter results allowed for the maximized fit comparison of each •Eight CHOP patients representing every one of the shown in figure 2, were simulated using NONMEM information (age, weight, visit history) and serum creatinine simulations.

1000 simulations were run for each models and parameters proposed by the authors listed the exact dosing schedule recommended by the Guide for CHOP (5). Simulations were set to collect intravenous vancomycin treatment.

•Simulation results were evaluated to observe deviations serum vancomycin concentrations and to look at the various published models.

•The structural models were then fit to actual CHOP two scenarios. The first fit fixed parameters to the fit allowed parameters in the various model to optimize