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 Vancomycin General Information: A glycopeptide that is the first-line treatment for coagulase-negative Staphylococci and Staphyloccus aureus infections involved in children. 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  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  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. Proposed Alternative Modeling-based Dosing Strategies for Vancomycin Dosing: Several empirical models have been proposed to aid dosing of individual neonatal vancomycin patients using various covariates (weight, age, creatinine concentration, etc.). 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. BACKGROUND OBJECTIVES SIMULATION RESULTS METHODS 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. Authorsn# obsPostnatalDosingCovariates in the final model de Hoog et al. (1) daysWeight Grimsley et al. (2) daysWeight, Creatinine Concentration Capparelli et al. (3) daysWeight, Creatinine Concentration Postnatal Age, Gestational Age Anderson et al. (4) daysWeight, Creatinine Concentration, Postmenstrual Age, Presence of a Ventilator, Inotrope Levels Current Study days Additional demographic information- Weight: kg, Gender Distribution: 73% male 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, 67(4), (2)C. Grimsley and A. H. Thomson, “Pharmacokinetics and dose requirements of vancomycin in neonates”, Arch Dis Child Fetal Neonatal Ed, 1999,81, F221–F227. (3)E. V. Capparelli, J. R. Lane, G. L. Romanowski, E. J. McFeely, W. Murray, P. Sousa, C. Kildoo and J. D. Connor, “The influences of renal function and maturation on vancomycin elimination in newborns and infants”, J. Clin. Pharmacol, 2001, 41, (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, 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 and stable creatinine concentration. 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 (8-15 ug/mL) using the Lexicomp guidelines (5). The models show very good 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. FITTED MODEL RESULTS Authors Number of parameters AIC from parameters fixed to published results AIC from the parameters optimized for CHOP data deHoog et al. (1) Grimsley et al. (2) Capparelli et al. (3) Anderson et al. (4) 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. 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 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. Figure 2: Percentage of patients achieving the lowest acceptable vancomycin trough levels (>5 ug/mL). Figure 1: CHOP vancomycin dosing as a function of glomular filtration rate.

Test efficacy of Lexi-Comp (5) to produce required trough concentrations in a patients Evaluate model performance in different types of patients Compare the “off-the-shelf” utility of each model (parameters unchanged) Compare the CHOP-optimized fit of each model 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. Fixed parameter model results allowed for comparison of the “off-the-shelf” utility of each model. CHOP optimized parameter results allowed for the maximized fit comparison of each model. 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 Simulation Objectives Model Fit Objectives Dosing Simulated assuming complete adherence to Lexi-Comp guidelines (5) Actual from clinical records 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 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 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. Findings Simulated assuming complete adherence to Lexi-Comp guidelines (5) Findings Actual from clinical records Test efficacy of Lexi-Comp (5) to produce target trough concentrations in patients Evaluate model performance in different types of patients Compare the “off-the-shelf” utility of each model (parameters unchanged) Compare the CHOP-optimized fit of each model 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 Simulation ObjectivesFitted Model Objectives Dosing Simulated assuming complete adherence to Lexi-Comp guidelines (5) Actual from clinical records 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 Findings Simulated vancomycin troughs from each model and compared results to target trough levels Compared the predicted vancomycin levels between the different models Findings Compared how well each model fit CHOP data by analyzing the akaike information criterion (lower value indicates better fit)