Dataset Information

Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review.

ABSTRACT: Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.

SUBMITTER: Abdulla A

PROVIDER: S-EPMC7940353 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review.

Abdulla Alan A Edwina Elma E EE Flint Robert B RB Allegaert Karel K Wildschut Enno D ED Koch Birgit C P BCP de Hoog Matthijs M

Frontiers in pediatrics 20210223

Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using populati ...[more]

PMID: 33708753

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Project description:Background and objectiveThe immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring, typically based on whole-blood trough concentrations (C0). Unfortunately, therapeutic drug monitoring target attainment rates are often unsatisfactory and patients with on-target exposure may still develop organ rejection. As everolimus displays erythrocyte partitioning, haematocrit-normalised whole-blood exposure has been suggested as a more informative therapeutic drug monitoring marker. Furthermore, model-informed precision dosing has introduced options for more sophisticated dose adaptation. We have previously developed a mechanistic population pharmacokinetic model, which described everolimus plasma pharmacokinetics and enabled estimation of haematocrit-normalised whole-blood exposure. Here, we externally evaluated this model for its utility for model-informed precision dosing.MethodsThe retrospective dataset included 4123 pharmacokinetic observations from routine clinical therapeutic drug monitoring in 173 renal transplant recipients. Model appropriateness was confirmed with a visual predictive check. A fit-for-purpose analysis was conducted to evaluate whether the model accurately and precisely predicted a future C0 or area under the concentration-time curve (AUC) from prior pharmacokinetic observations. Bias and imprecision were expressed as the mean percentage prediction error (MPPE) and mean absolute percentage prediction error (MAPE), stratified on 6 months post-transplant. Additionally, we compared dose adaptation recommendations of conventional C0-based therapeutic drug monitoring and C0- or AUC-based model-informed precision dosing, and assessed the percentage of differences between observed and haematocrit-normalised C0 (∆C0) and AUC (∆AUC) exceeding ± 20%.ResultsThe model showed adequate accuracy and precision for C0 and AUC prediction at ≤ 6 months (MPPEC0: 8.1 ± 2.5%, MAPEC0: 26.8 ± 2.1%; MPPEAUC: - 9.7 ± 5.1%, MAPEAUC: 13.3 ± 3.9%) and > 6 months post-transplant (MPPEC0: 4.7 ± 2.0%, MAPEC0: 25.4 ± 1.4%; MPPEAUC: - 0.13 ± 4.8%, MAPEAUC: 13.3 ± 2.8%). On average, dose adaptation recommendations derived from C0-based and AUC-based model-informed precision dosing were 2.91 ± 0.01% and 13.7 ± 0.18% lower than for conventional C0-based therapeutic drug monitoring at ≤ 6 months, and 0.93 ± 0.01% and 3.14 ± 0.04% lower at > 6 months post-transplant. The ∆C0 and ∆AUC exceeded ± 20% on 13.6% and 14.3% of occasions, respectively.ConclusionsWe demonstrated that our population pharmacokinetic model was able to accurately and precisely predict future everolimus exposure from prior pharmacokinetic measurements. In addition, we illustrated the potential added value of performing everolimus therapeutic drug monitoring with haematocrit-normalised whole-blood concentrations. Our results provide reassurance to implement this methodology in clinical practice for further evaluation.

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Dataset Information

Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review.

Publications

Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review.

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