Evidence map›Paper›PMID 42627441›Full record

SynthesisPaediatric drugs2026

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

Abdullah Aljutayli, Emna Gaies, Mohammed S Alasmari, Zekra Kamel Aljehani, Abrar Samman, Sara Abudahab, Manal Abouelkheir, Abdullah Alsultan

Abstract readSystematic Review
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In one paragraph

Synthesis in Paediatric drugs, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Abdullah AljutayliDepartment of Pharmaceutics, College of Pharmacy, Qassim University, Buraydah, Saudi Arabia. A.ALJUTYLI@qu.edu.sa.ORCID http://orcid.org/0009-0001-5972-7832
Emna GaiesClinical Pharmacology Department, Chalbi Belkahia National Center of Pharmacovigilance, Tunis, Tunisia.
Mohammed S AlasmariSecurity Forces Hospital Program, General Directorate of Medical Services, Ministry of Interior, Riyadh, Saudi Arabia.
Zekra Kamel AljehaniPharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.
Abrar SammanDepartment of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
Sara AbudahabDepartment of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA.
Manal AbouelkheirDepartment of Clinical Pharmacy, Faculty of Pharmacy, Misr International University, Cairo, Egypt.
Abdullah AlsultanDepartment of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. absultan@ksu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPersonalized 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.

objectiveThis 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.

methodsWe 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.

resultsWe 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).

conclusionsModel-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.

Indexed as

Models, BiologicalPrecision MedicineSoftwareBayes TheoremChildHumansInfant, NewbornPediatricsPharmacokinetics

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.