Evaluation of Neonatal and Paediatric Vancomycin Pharmacokinetic Models and the Impact of Maturation and Serum Creatinine Covariates in a Large Multicentre Data Set.

BACKGROUND AND OBJECTIVE: Infants and neonates present a clinical challenge for dosing drugs with high interindividual variability due to these patients' rapid growth and the interplay between maturation and organ function. Model-informed precision dosing (MIPD), which can account for interindividual variability via patient characteristics and Bayesian forecasting, promises to improve individualized dosing strategies in this complex population. Here, we assess the predictive performance of published population pharmacokinetic models describing vancomycin in neonates and infants, and analyze the robustness of these models in the face of clinical uncertainty surrounding covariate values. METHODS: The predictive precision and bias of nine pharmacokinetic models were compared in a large multi-site data set (N = 2061 patients, 5794 drug levels, 28 institutions) of patients aged 0-365 days. The robustness of model predictions to errors in serum creatinine measurements and gestational age was assessed by using recorded values or by replacing covariate values with 0.3, 0.5 or 0.8 mg/dL or with 40 weeks, respectively. RESULTS: Of the nine models, two models (Dao and Jacqz-Aigrain) resulted in predicted concentrations within 2.5 mg/L or 15% of the measured values for at least 60% of population predictions. Within individual models, predictive performance often 2 differed in neonates (0-4 weeks) versus older infants (15-52 weeks). For preterm neonates, imputing gestational age as 40 weeks reduced the accuracy of model predictions. Measured values of serum creatinine improved model predictions compared to using imputed values even in neonates ≤1 week of age. CONCLUSIONS: Several available pharmacokinetic models are suitable for MIPD in infants and neonates. Availability and accuracy of model covariates for patients will be important for guiding dose decision-making.

Continuous Learning in Model-Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin.

Model-informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor dosing to an individual patient's needs, improving attainment of therapeutic drug exposure targets and thus potentially improving drug efficacy or reducing adverse events. However, selection of an appropriate model for supporting clinical decision making is not trivial. Error or bias in dose selection may arise if the selected model was developed in a population not fully representative of the intended MIPD population. One previously proposed approach is continuous learning, in which an initial model is used in MIPD and then updated as additional data becomes available. In this case study of pediatric vancomycin MIPD, the potential benefits of the continuous learning approach are investigated. Five previously published models were evaluated and found to perform adequately in a data set of 273 pediatric patients in the intensive care unit. Additionally, two predefined simple PK models were fitted on separate populations of 50-350 patients in an approach mimicking clinical implementation of automated continuous learning. With these continuous learning models, prediction error using population PK parameters could be reduced by 2-13% compared with previously published models. Sample sizes of at least 200 patients were found suitable for capturing the interindividual variability in vancomycin at this institution, with limited benefits of larger data sets. Although comprised mostly of trough samples, these sparsely sampled routine clinical data allowed for reasonable estimation of simulated area under the curve (AUC). Together, these findings lay the foundations for a continuous learning MIPD approach.

Simulated Comparison of a Bayesian Clinical Decision Support System Versus Standard of Care For Achieving Gentamicin Pharmacokinetic Targets in Neonates.

BACKGROUND: Gentamicin therapy in neonates is optimized through achieving specific peak and trough concentrations. The objective of this study was to compare the ability a Bayesian clinical decision support system (CDSS) with standard of care (SOC) in determining personalized gentamicin therapies for neonates, at regimen initiation and in response to measured drug concentrations. METHODS: This retrospective review and simulation compared target attainment among 4 arms: historical dosing according to SOC, via nomogram for initial dosing (SOC-initial) and via clinician judgment in response to measured concentrations (SOC-adjusted), and simulated dosing using the CDSS, incorporating a neonatal pharmacokinetic model for initial dosing (CDSS-initial) and incorporating maximum a posteriori-Bayesian analysis in response to measured concentrations (CDSS-adjusted). "True" patient pharmacokinetic parameters and peak and trough concentration predictions were calculated via the CDSS using the entirety of the patient dosing and concentration history. The primary outcome was pharmacokinetic target attainment of desired gentamicin peak and trough concentrations. RESULTS: The study included 564 gentamicin concentrations among 339 patients. Mean demographics were 35 weeks gestational age (52% premature births) and 2.44 kg dosing weight. Mean PK parameters were 0.0533 L/h/kg clearance, 0.458 L/kg volume of distribution, and 8.66 hours half-life. Peak concentrations in the desired range were achieved in 96% of significantly more often in the CDSS-initial regimens and 94% of CDSS-adjusted regimens versus 86% of SOC-initial regimens and 66% of SOC-adjusted regimens. No difference was found in trough target attainment among study groups. CONCLUSIONS: In simulation, a Bayesian CDSS showed superiority to SOC in achieving gentamicin pharmacokinetic exposure targets in neonates. Use of a CDSS may improve the safety and efficacy of gentamicin therapy for neonates.

Bayesian clinical decision support-guided versus clinician-guided vancomycin dosing in attainment of targeted pharmacokinetic parameters in a paediatric population.

OBJECTIVES: To compare a Bayesian clinical decision support (CDS) dose-optimizing software program with clinician judgement in individualizing vancomycin dosing regimens to achieve vancomycin pharmacokinetic (PK)/pharmacodynamic (PD) targets in a paediatric population. METHODS: A retrospective review combined with a model-based simulation of vancomycin dosing was performed on children aged 1 year to 18 years at the University of California, San Francisco Benioff Children's Hospital Mission Bay. Dosing regimens recommended by the clinical pharmacists, 'clinician-guided', were compared with alternative 'CDS-guided' dosing regimens. The primary outcome was the percentage of occasions predicted to achieve steady-state trough levels within the target range of 10-15 mg/L, with a secondary outcome of predicted attainment of AUC24 ≥400 mg·h/L. Statistical comparison between approaches was performed using a standard t-test. RESULTS: A total of n=144 patient occasions were included. CDS-guided regimens were predicted to achieve vancomycin steady-state troughs in the target range on 70.8% (102/144) of occasions, as compared with 37.5% (54/144) in the clinician-guided arm (P<0.0001). An AUC24 of ≥400 mg·h/L was achieved on 93% (112/121) of occasions in the CDS-guided arm versus 72% (87/121) of occasions in the clinician-guided arm (P<0.0001). CONCLUSIONS: In a simulated analysis, the use of a Bayesian CDS tool was better than clinician judgement in recommending vancomycin dosing regimens in which PK/PD targets would be attained in children.