Model-Informed Precision Dosing in Pediatric Patients: Current Software Tools and Bedside Guided Application.

BACKGROUND: Personalized dosing is particularly important in pediatric patients, as age-related physiological maturation, developmental changes, and patient-specific factors contribute to substantial pharmacokinetic variability. Standard dosing approaches often fail to adequately account for this heterogeneity and may lead to subtherapeutic or supratherapeutic drug concentrations. Model-informed precision dosing (MIPD) addresses these limitations by integrating individual patient data with population pharmacokinetic/pharmacodynamic models to optimize dosing at the individual level more accurately. OBJECTIVE: This review aimed to (1) provide a structured overview of currently available bedside-compatible MIPD software applications for pediatric dosing, (2) systematically summarize published clinical studies evaluating their use in pediatric and neonatal populations, and (3) illustrate practical bedside application through a step-by-step tutorial. METHODS: We identified MIPD software tools that incorporate Bayesian forecasting and at least one pediatric module through iterative literature searches using terms for MIPD and dosing software, and by developer verification. For eligible platforms, we reviewed PubMed-indexed clinical studies involving pediatric patients, summarizing key aspects including study design, population, drug, clinical decision support-related objectives, and findings. A representative tutorial was developed using NextDose for neonatal vancomycin dosing. RESULTS: We identified 11 available MIPD software platforms, varying in deployment, model libraries, drug coverage, electronic health record integration, and regulatory status (e.g., CE-marked medical devices vs non-regulated clinical decision support). Review of 26 clinical studies enrolling more than 4000 pediatric and neonatal patients demonstrated that MIPD consistently outperformed conventional methods in predictive accuracy, precision, and pharmacokinetic target attainment, including significant improvements in target area under the curve attainment for key drugs like vancomycin. Benefits included a reduced sampling burden and faster therapeutic exposure, predominantly for antibiotics (vancomycin, tobramycin, gentamicin) and select chemotherapeutics (busulfan). CONCLUSIONS: Model-informed precision dosing software tools offer substantial potential to improve precision dosing in pediatrics, with robust evidence of superior target attainment and operational advantages. Gaps remain in prospective randomized trials, direct patient-centered outcome data, cost-effectiveness analyses, non-antibiotic drug coverage, and clinician training.