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Background: Extremely low birth weight (ELBW) neonates (birth weight ≤ 1000 g) are at high risk to develop drug-induced acute kidney injury (AKI). However, we lack a pragmatic detection tool to capture their time-dependent (patho)physiologic serum creatinine (Scr) patterns. Pottel et al. suggested rescaling Scr by dividing Scr with the mean Scr value of the age- and sex-specific reference population. We explored if this Pottel method can detect drug-related nephrotoxicity in ELBW neonates., Methods: A previously reported dataset on Scr changes in ELBW neonates exposed to ibuprofen, amikacin, or vancomycin was updated to calculate Pottel scores for every available Scr value in the first 28 postnatal days. We hereby used previously published postnatal age-specific 50 th centile values in an ELBW population. Linear mixed models were applied, analyzing Pottel scores as response variable and continuous time (day), drug exposure, and interaction thereof in the explanatory model., Results: Serum creatinine (n = 3231) observations in 201 ELBW neonates were collected. A statistically significant rise of Pottel scores was observed with ibuprofen starting from postnatal day 4. In addition, a cumulative effect of treatment with mean Pottel scores on day 0 of 1.020 and on day 3 during treatment of 1.106 (95% CI 1.068-1.145, p < 0.001) was observed, corrected for effect of antibiotics. Antibiotic administrations showed a small but statistically significant difference up to postnatal day 5., Conclusions: As rescaled Scr biomarker, the Pottel method showed a clear association with ibuprofen-exposed ELBW neonates, suggesting its applicability as a pragmatic bedside alternative tool to assess nephrotoxicity., (© 2024. The Author(s).)

Aims: We propose using glomerular filtration rate (GFR) as the physiological basis for distinguishing components of renal clearance., Methods: Gentamicin, amikacin and vancomycin are thought to be predominantly excreted by the kidneys. A mixed-effects joint model of the pharmacokinetics of these drugs was developed, with a wide dispersion of weight, age and serum creatinine. A dataset created from 18 sources resulted in 27,338 drug concentrations from 9,901 patients. Body size and composition, maturation and renal function were used to describe differences in drug clearance and volume of distribution., Results: This study demonstrates that GFR is a predictor of two distinct components of renal elimination clearance: (1) GFR clearance associated with normal GFR and (2) non-GFR clearance not associated with normal GFR. All three drugs had GFR clearance estimated as a drug-specific percentage of normal GFR (gentamicin 39%, amikacin 90% and vancomycin 57%). The total clearance (sum of GFR and non-GFR clearance), standardized to 70 kg total body mass, 176 cm, male, renal function 1, was 5.58 L/h (95% confidence interval [CI] 5.50-5.69) (gentamicin), 7.77 L/h (95% CI 7.26-8.19) (amikacin) and 4.70 L/h (95% CI 4.61-4.80) (vancomycin)., Conclusions: GFR provides a physiological basis for renal drug elimination. It has been used to distinguish two elimination components. This physiological approach has been applied to describe clearance and volume of distribution from premature neonates to elderly adults with a wide dispersion of size, body composition and renal function. Dose individualization has been implemented using target concentration intervention., (© 2023 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Background and Objective: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C 0 ) and steady-state area-under-curve (AUC 0-24 ) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions., Methods: C 0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC 0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C 0 and AUC 0-24 . An external dataset was used for predictive performance evaluation., Results: Before starting treatment, C 0 can be predicted a priori using the Catboost-based C 0 -ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C 0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C 0 ) in patients have been obtained, AUC 0-24 can be further predicted using the Catboost-based AUC-ML model combined with C 0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%., Conclusion: C 0 -based and AUC 0-24 -based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background Vancomycin is a frequently used antibiotic in neonates. However, there is no consensus guideline on the optimal dosing regimen and therapeutic drug monitoring (TDM) practices in this patient population. Objective To document the variability in the current dosing and TDM practices in neonatal intensive care units (NICU). Setting Belgian and Dutch NICUs. Method An online questionnaire was disseminated by e-mail to potential respondents. Main outcome measure Differences in vancomycin dosing and TDM practices in comparison with a reference source, the Dutch Paediatric Formulary. Results Eighteen NICUs (response rate 62%) participated. Eleven different dosing regimens are applied, with 83% using intermittent dosing regimens. Stratifying covariates used to determine the (initial) dosage include gestational age, postnatal age, serum creatinine, concurrent use of non-steroidal anti-inflammatory drugs, birth weight and current weight. Large variability is observed with regard to TDM practice as well, both for the concentration target range and the times of (re)sampling. Dosing calculators are more commonly used in the Netherlands than Belgium. Conclusion Significant inter-centre variability in dosing and TDM practices was found. The development of international consensus guidelines is required to optimize therapy. Dosing calculators to guide dosing are not yet considered as part of standard-of-care., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Disentangling renal adverse drug reactions from confounders remains a major challenge to assess causality and severity in neonates, with additional limitations related to the available tools (modified Kidney Disease Improving Global Outcome, or Division of Microbiology and Infectious Diseases pediatric toxicity table). Vancomycin and amikacin are nephrotoxic while still often prescribed in neonates. We selected these compounds to assess their impact on creatinine dynamics as a sensitive tool to detect a renal impairment signal., Methods: A recently developed dynamical model that characterized serum creatinine concentrations of 217 extremely low birth weight (<1000 g, ELBW) neonates (4036 observations) was enhanced with data on vancomycin and/or amikacin exposure to identify a potential effect of antibiotic exposure by nonlinear mixed-effects modelling., Results: Seventy-seven percent of ELBW patients were exposed to either vancomycin or amikacin. Antibiotic exposure resulted in a modest increase in serum creatinine and a transient decrease in creatinine clearance. The serum creatinine increase was dependent on gestational age, illustrated by a decrease with 56% in difference in serum creatinine between a 24 or 32-week old neonate, when exposed in the 3rd week after birth., Conclusions: A previously described model was used to explore and quantify the impact of amikacin or vancomycin exposure on creatinine dynamics. Such tools serve to explore minor changes, or compare minor differences between treatment modalities.

This study aimed to explore vancomycin pharmacokinetics and its covariates in critically ill neonates and to propose an easy applicable dosing nomogram for initial treatment. Individual vancomycin pharmacokinetic parameters were calculated based on therapeutic drug monitoring data using a one-compartmental model. A linear regression model was used for examination of covariates. The mean (SD) volume of distribution (Vd) and clearance (CL) for vancomycin were 0.73 (0.31) L/kg and 0.052 (0.020) L/h/kg, respectively. Vd was related to actual body weight (ABW), gestational and postmenstrual age. CL was also associated with ABW, gestational, postmenstrual age and also creatinine clearance. ABW was the strongest predictor for vancomycin pharmacokinetics and consequently dosing. Loading dose (mg) of 11.81 × ABW (kg) + 7.86 and maintenance dose (mg/day) of 40.92 × ABW (kg) -22.18 most closely approximated pharmacokinetic target. Vancomycin pharmacokinetics was mainly influenced by ABW in neonates and a practical ABW-based dosing algorithm was developed.

Introduction : Vancomycin is commonly administered to neonates, while observational data on therapeutic drug monitoring (TDM, trough levels) suggest that vancomycin exposure and dosage remain substandard. Area covered : Data on vancomycin pharmacokinetics (PK) and its covariates are abundant. Consequently, modeling is an obvious tool to improve targeted exposure, with a shift from TDM trough levels to area under the curve (AUC 24h ) targets, as in adults. Continuous administration appeared as a practice to facilitate AUC 24h target attainment, while Bayesian model-supported targeting emerged as a novel tool. However, the AUC 24h /MIC (minimal inhibitory concentration) target itself should consider neonate-specific aspects (bloodstream infections, coagulase-negative staphylococci, protein binding, underexplored causes of variability, like assays, preparation and administration inaccuracies, or missing covariates). Expert opinion : To improve targeted exposure in neonates, initial vancomycin prescription should be based on 'a priori model-based individual dosing' using validated dosing regimens, followed by further tailoring by dosing optimization applying Bayesian estimation-assisted TDM. Future research should focus on the feasibility to integrate these tools (individualized dosing, Bayesian models) in clinical practice, and to perform PK/PD studies in the relevant animal models and human neonatal setting (coagulase-negative staphylococci, bloodstream infections).

Objectives: In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates., Methods: A 'meta-model' with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0-24 of 400 mg·h/L at steady-state in at least 80% of neonates., Results: A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if <35 weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0-24 target earlier than a standard 'Blue Book' dosage regimen in >89% of the treated patients., Conclusions: The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc., (© The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Ibuprofen and indomethacin are commonly used to induce ductus arteriosus closure in preterm neonates. Our group previously reported that ibuprofen decreased vancomycin clearance by 16%. In this study, we quantified the impact of indomethacin coadministration on vancomycin clearance by extending our vancomycin population pharmacokinetic model with a data set containing vancomycin concentrations measured in preterm neonates comedicated with indomethacin. The modeling data set includes concentration-time data of vancomycin administered alone or in combination with either ibuprofen or indomethacin collected in the neonatal intensive care units of UZ Leuven (Leuven, Belgium) and São Francisco Xavier Hospital (Lisbon, Portugal). The derived vancomycin pharmacokinetic model was subsequently used to propose dose adjustments that yield effective vancomycin exposure (i.e., area under the concentration-time curve from 0 to 24 h [AUC 0-24 ] between 300 to 550 mg·h/liter, with a probability of <0.1 of subtherapeutic exposure) in preterm neonates with patent ductus arteriosus. We found that indomethacin coadministration reduced vancomycin clearance by 55%. Model simulations showed that the most recent vancomycin dosing regimen, which was based on an externally validated model, requires 20% and 60% decreases of the loading and maintenance doses of vancomycin, respectively, when aiming for optimized exposure in the neonatal population. By analyzing vancomycin data from preterm neonates comedicated with indomethacin, we found a substantial decrease in vancomycin clearance of 55% versus a previously reported 16% for ibuprofen. This decrease in clearance impacts vancomycin dosing, and we anticipate that other drugs eliminated by glomerular filtration are likely to be affected to a similar extent as vancomycin., (Copyright © 2019 American Society for Microbiology.)

Background and Objectives: Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups., Methods: A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived., Results: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL -1 (73.4 µmol L -1 ) has a V 1 , V 2 , CL and Q 2 of 42.9 L, 41.7 L, 4.10 L h -1 and 3.22 L h -1 . Clearance matures with age, reaching 50% of the maximal value (5.31 L h -1  70 kg -1 ) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups., Conclusions: A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.

Vancomycin pharmacokinetic (PK) and pharmacodynamic (PD) data in neonates are based on total concentrations. However, only unbound vancomycin is pharmacologically active. The objective was to determine vancomycin protein binding and the covariates impacting unbound vancomycin concentration in neonates and young infants. In neonates and young infants to whom vancomycin was administered intermittently for medical indications, total and unbound vancomycin plasma concentrations were determined using LC-MS/MS. Sampling occurred randomly during vancomycin exposure, covering a broad range of concentrations. Impact of covariates on unbound vancomycin concentration was determined using linear regression. Significant results of the univariate regressions were entered in a stepwise multiple regression. Passing-Bablok regression and Bland-Altman were used to assess the difference between measured and calculated unbound vancomycin concentration. Thirty-seven samples in 33 patients (median (interquartile range) gestational age 35 (29-39) weeks) were collected. Median total and unbound vancomycin concentrations were 14.2 (7.4-20.6) and 13.6 (7.2-22.5) mg/L, respectively. Median unbound fraction was 0.90 (0.77-0.98). Multiple regression revealed total vancomycin concentration (β = 0.884, p < 0.001) and albumin (β = - 0.323, p = 0.007) as most important covariates of unbound vancomycin concentrations, with an R 2 adjusted of 0.953 (p < 0.0001). Mean absolute difference between calculated and measured unbound vancomycin was - 0.008 (95% CI - 0.92-0.91) mg/L. The unbound vancomycin fraction in neonates is higher compared to that in children and adults, and total vancomycin concentration and albumin were the most important covariates of unbound vancomycin concentration. Integration of protein binding in future PK/PD analyses is appropriate to optimize vancomycin dosing and to determine population-specific vancomycin PD targets for neonates.

Substantial interassay variability (up to 20%) has been described for vancomycin immunoassays in adults, but the impact of neonatal matrix is difficult to quantify because of blood volume constraints in neonates. However, we provide circumstantial evidence for a similar extent of variability. Using the same vancomycin dosing regimens and confirming similarity in clinical characteristics, vancomycin trough concentrations measured by PETINIA (2011-2012, n = 400) were 20% lower and the mean difference was 1.93 mg/L compared to COBAS (2012-2014, n = 352) measurements. The impact of vancomycin immunoassays in neonatal matrix was hereby suggested, supporting a switch to more advanced techniques (LC-MS/MS).

Because of the recent awareness that vancomycin doses should aim to meet a target area under the concentration-time curve (AUC) instead of trough concentrations, more aggressive dosing regimens are warranted also in the pediatric population. In this study, both neonatal and pediatric pharmacokinetic models for vancomycin were externally evaluated and subsequently used to derive model-based dosing algorithms for neonates, infants, and children. For the external validation, predictions from previously published pharmacokinetic models were compared to new data. Simulations were performed in order to evaluate current dosing regimens and to propose a model-based dosing algorithm. The AUC/MIC over 24 h (AUC24/MIC) was evaluated for all investigated dosing schedules (target of >400), without any concentration exceeding 40 mg/liter. Both the neonatal and pediatric models of vancomycin performed well in the external data sets, resulting in concentrations that were predicted correctly and without bias. For neonates, a dosing algorithm based on body weight at birth and postnatal age is proposed, with daily doses divided over three to four doses. For infants aged <1 year, doses between 32 and 60 mg/kg/day over four doses are proposed, while above 1 year of age, 60 mg/kg/day seems appropriate. As the time to reach steady-state concentrations varies from 155 h in preterm infants to 36 h in children aged >1 year, an initial loading dose is proposed. Based on the externally validated neonatal and pediatric vancomycin models, novel dosing algorithms are proposed for neonates and children aged <1 year. For children aged 1 year and older, the currently advised maintenance dose of 60 mg/kg/day seems appropriate., (Copyright © 2016, American Society for Microbiology. All Rights Reserved.)

The unbound drug hypothesis states that only unbound drug concentrations are active and available for clearance, and highly variable results regarding unbound vancomycin fractions have been reported in the literature. We have determined the unbound vancomycin fractions in four different patient groups by a liquid chromatography tandem mass spectrometry (LC-MS/MS) method and identified factors that modulate vancomycin binding. We have further developed and validated a prediction model to estimate unbound vancomycin concentrations. Vancomycin (unbound and total) concentrations were measured in 90 patients in four different hospital wards (hematology [n = 33 samples], intensive care unit [ICU] [n = 51], orthopedics [n = 44], and pediatrics [age range, 6 months to 14 years; n = 18]) by a validated LC-MS/MS method. Multiple linear mixed model analysis was performed to identify patient variables that were predictive of unbound vancomycin fractions and concentrations. The variables included in the model were patient age, ward, number of coadministered drugs with high protein binding, kidney function (estimated glomerular filtration rate [determined by Chronic Kidney Disease Epidemiology Collaboration formula]), alpha-1-acid glycoprotein, albumin, total bilirubin, IgA, IgM, urea, and total vancomycin concentrations. In the pediatric cohort, the median unbound vancomycin fraction was 81.3% (range, 61.9 to 95.9%), which was significantly higher (P < 0.01) than the unbound fraction found in the three adult patient cohorts (hematology, 60.6% [48.7 to 90.6%]; ICU, 61.7% [47.0 to 87.6%]; orthopedics, 56.4% [45.9 to 78.0%]). The strongest significant predictor of the unbound vancomycin concentration was the total drug concentration, completed by albumin in the pediatric cohort and albumin and IgA in the adult cohorts. Validation of our model was performed with data from 13 adult patients. A mean difference of 0.3 mg/liter (95% confidence interval [CI], -1.3 to 0.7 mg/liter; R(2) = 0.99 [95% CI, 0.95 to 0.99]) between measured and calculated unbound vancomycin concentrations demonstrated that the predictive performance of our model was favorable. Unbound vancomycin fractions vary significantly between pediatric and adult patients. We developed a formula to estimate the unbound fraction derived from total vancomycin, albumin, and IgA concentrations in adult patients., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

Background: Accurate quantification of vancomycin in plasma is important for adequate dose-adjustment. As literature suggests between-method differences, our first objective was to develop a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for total vancomycin in human plasma and to compare frequently used immunoassays with this method. Secondly, we investigated the clinical impact of between-method quantification differences., Methods: For LC-MS/MS, lithium heparin plasma was extracted by adding a precipitation reagent containing the internal standard (vancomycin-des-leucine). Analysis was performed on an Acquity TQD mass spectrometer equipped with an Acquity UPLC 2795 separations module. Our method was analytically validated and compared with four frequently used immunoassays from four different manufacturers. Vancomycin concentrations were clinically classified as toxic, therapeutic and sub-therapeutic. Clinical discordance was calculated using LC-MS/MS as a reference., Results: A novel LC-MS/MS method using protein precipitation as sole pretreatment and an analysis time of 5.0 min was developed. The assay had a total imprecision of 2.6-8.5%, a limit of quantification of 0.3 mg/L and an accuracy ranging from 101.4 to 111.2%. Using LC-MS/MS as reference, three immunoassays showed a mean proportional difference within 10% and one showed a substantial mean proportional difference of >20%. Clinical discordant interpretation of the obtained concentrations ranged from 6.1 to 22.2%., Conclusions: We developed a novel LC-MS/MS method for rapid analysis of total vancomycin concentrations in human plasma. Correlation of the method with immunoassays showed a mean proportional difference >20% for one of the assays, causing discordant clinical interpretation in more than 1 out of 5 samples., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Purpose: Since glomerular filtration rate (GFR) is responsible for the elimination of a large number of water-soluble drugs, the aim of this study was to develop a semi-physiological function for GFR maturation from neonates to adults., Methods: In the pharmacokinetic analysis (NONMEM VI) based on data of gentamicin, tobramycin and vancomycin collected in 1,760 patients (age 1 day-18 years, bodyweight 415 g-85 kg), a distinction was made between drug-specific and system-specific information. Since the maturational model for clearance is considered to contain system-specific information on the developmental changes in GFR, one GFR maturational function was derived for all three drugs., Results: Simultaneous analysis of these three drugs showed that maturation of GFR mediated clearance from preterm neonates to adults was best described by a bodyweight-dependent exponent (BDE) function with an exponent varying from 1.4 in neonates to 1.0 in adults (ClGFR = Cldrug*(BW/4 kg)(BDE) with BDE = 2.23*BW(-0.065)). Population clearance values (Cldrug) for gentamicin, tobramycin and vancomycin were 0.21, 0.28 and 0.39 L/h for a full term neonate of 4 kg, respectively., Discussion: Based on an integrated analysis of gentamicin, tobramycin and vancomycin, a semi-physiological function for GFR mediated clearance was derived that can potentially be used to establish evidence based dosing regimens of renally excreted drugs in children.

Background: Vancomycin is one of the most evaluated antibiotics in neonates using modeling and simulation approaches. However no clear consensus on optimal dosing has been achieved. The objective of the present study was to perform an external evaluation of published models, in order to test their predictive performances in an independent dataset and to identify the possible study-related factors influencing the transferability of pharmacokinetic models to different clinical settings., Method: Published neonatal vancomycin pharmacokinetic models were screened from the literature. The predictive performance of 6 models was evaluated using an independent dataset (112 concentrations from 78 neonates). The evaluation procedures used simulation-based diagnostics (visual predictive check [VPC] and normalized prediction distribution errors [NPDE])., Results: Differences in predictive performances of models for vancomycin pharmacokinetics in neonates were found. The mean of NPDE for 6 evaluated models were 1.35, -0.22, -0.36, 0.24, 0.66, 0.48, respectively. These differences were explained, at least partly, by taking into account the method used to measure serum creatinine concentrations. The adult conversion factor of 1.3 (enzymatic to Jaffé) was tested with an improvement in the VPC and NPDE, but it still need to be evaluated and validated in neonates. Differences were also identified between analytical methods for vancomycin., Conclusion: The importance of analytical techniques for serum creatinine concentrations and vancomycin as a predictor of vancomycin concentrations in neonates has been confirmed. Dosage individualisation of vancomycin in neonates should consider not only patients' characteristics and clinical conditions, but also the methods used to measure serum creatinine and vancomycin., (© 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.)

Neonatal sepsis is common and is a major cause of morbidity and mortality. Vancomycin is the preferred treatment of several neonatal staphylococcal infections. The aim of this study was to review published data on vancomycin pharmacokinetics in neonates and to provide a critical analysis of the literature. A bibliographic search was performed using PubMed and Embase, and articles with a publication date of August 2011 or earlier were included in the analysis. Vancomycin pharmacokinetic estimates, which are different in neonates compared with adults, also exhibit extensive inter-neonatal variability. In neonates, several vancomycin dosing schedules have been proposed, mainly based on age (i.e., postmenstrual and postnatal), body weight or serum creatinine level. Other covariates [e.g., extracorporeal membrane oxygenation (ECMO), indomethacin or ibuprofen, and growth restriction] of vancomycin pharmacokinetics have been reported in neonates. Finally, vancomycin penetrates cerebrospinal fluid (range = 7-42%). Renal function drives vancomycin pharmacokinetics. Because either age or weight is the most relevant covariate of renal maturation, these covariates should be considered first in neonatal vancomycin dosing guidelines and further adjusted by renal dysfunction indicators (e.g., ECMO and ibuprofen/indomethacin). In addition to the prospective validation of available dosing guidelines, future studies should focus on the relevance of therapeutic drug monitoring and on the value of continuous vancomycin administration in neonates.

Introduction: Administration of ibuprofen or indomethacin in neonates is associated with a reduction of glomerular filtration, reflected in a reduction in the elimination of drugs dependent on renal function for clearance. However, the magnitude of these effects and potential differences between ibuprofen or indomethacin remain unclear., Methods: Observations collected in two population pharmacokinetic studies in preterm neonates investigating amikacin and vancomycin disposition were used to estimate (i) the impact of ibuprofen administration and (ii) the difference between ibuprofen and indomethacin on renal drug clearance., Results: Amikacin clearance was reduced by 21% and vancomcyin was reduced by 18% during co-administration of ibuprofen. In a vancomycin pooled pharmacokinetic study, it was documented that the impact of indomethacin was significantly higher compared to ibuprofen (46 and 28%, respectively)., Conclusions: Both ibuprofen and indomethacin have a quantifiable impact on renal drug clearance. Compared to ibuprofen, the impact of indomethacin is more pronounced. Population modelling with covariate analysis can be a tool to disentangle the impact of various covariates, including ibuprofen or indomethacin, on renal drug clearance.

Aminoglycosides and glycopeptides are almost exclusively eliminated by renal excretion. Postmenstrual age (PMA) is the best predictor of their clearance, presumably because it predicts the time course of development of the glomerular filtration rate (GFR). Intrauterine growth restriction has an impact on the normalized weight of the kidney, on the number of nephrons, on GFR, and on tubular function in human perinatal life. We investigated whether prenatal growth also affects clearance of drugs such as aminoglycosides or glycopeptides that are eliminated through the kidney. Observations collected in two population pharmacokinetic studies involving preterm neonates and investigating amikacin and vancomycin in the first month of postnatal life were used to estimate the impact of prenatal growth (as judged by birth weight for gestational age) on the clearance of these drugs. Data from 1212 drug measurements (vancomycin, 648; amikacin, 564) in 531 subjects (vancomycin, 249; amikacin, 282) were available for study. Neonates born small for gestational age (SGA) were found to have a 16.2% (coefficient of variation, 12.2%) reduction in drug clearance. This effect was present from birth up to the postnatal age of 4 weeks. The covariate size (weight 0.75) explained 47.3% of drug clearance; PMA, 25.2%; coadministration of a nonselective cyclo-oxygenase inhibitor, 3.5%; renal function, 7.6%; and SGA, 1.7%. Renal drug clearance is significantly lower in preterm neonates born SGA than in appropriate-for-gestational-age (AGA) controls. This reduced clearance was observed not only at birth but also up to the postnatal age of 4 weeks.

What Is Already Known About This Subject: *Effects of size, renal function, age (postnatal age, gestational age and postmenstrual age) as predictors of vancomycin clearance in premature neonates are established, but the relative contribution of each component remains poorly quantified, largely because these variables are closely correlated. *We have quantified the covariates contributing to vancomycin clearance population parameter variability in order to establish the major covariates required for dosing predictions. Size, standardized using allometric models, was the primary covariate used in our analysis., What This Study Adds: *Size explained 49.8%, postmenstrual age 18.2% and renal function 14.1% of clearance variability. *Descriptors of the relationship between age and clearance in premature neonates vary. *The use of a variable slope sigmoidal model to describe the relationship between clearance and postmenstrual age predicted an adult clearance of 3.79 l h(-1) 70 kg(-1) (95% confidence interval 2.76, 4.98) from premature neonatal data., Aim: To identify and quantify factors describing the variability of vancomycin clearance in premature neonates., Methods: Population pharmacokinetics were estimated (NONMEM) in 214 neonates [postmenstrual age (PMA) 30.4 weeks, range 24-34 weeks; postnatal age 11.9 days, range 1-27 days; weight 1.30 kg, range 0.42-2.6 kg] using therapeutic drug monitoring data. Covariate analysis included weight, PMA, serum creatinine, use of inotropes or ibuprofen, positive blood culture and respiratory support. A one-compartment linear disposition model with zero order input and first-order elimination was used to describe the data (604 observations)., Results: The population estimate for volume of distribution (V) was 39 l 70 kg(-1) (coefficient of variation 19.4%). Clearance (CL) increased from 0.897 l h(-1) 70 kg(-1) at 24 weeks PMA to 2.02 l h(-1) 70 kg(-1) by 34 weeks PMA. The between-subject variability for CL was 18.6% and the between-occasion variability was 12.2%. The use of ibuprofen reduced clearance, but this effect was attributable to reduced renal function. Overall, 82% of the variability of CL was predictable. Size explained 49.8%, PMA 18.2% and renal function 14.1%. The use of a variable slope sigmoidal model to describe the relationship between clearance and PMA predicted an adult clearance of 3.79 l h(-1) 70 kg(-1) (95% confidence interval 2.76, 4.98)., Conclusions: Size, renal function and PMA are the major contributors to clearance variability in premature neonates. The small (18%) unexplained variability in clearance suggests target concentration intervention is unnecessary if size, age and renal function are used to predict the dose. Extrapolation to an adult clearance from neonatal data is possible using allometric size models and a function describing clearance maturation.