Evidence map›Paper›PMID 26829600›Full record

ArticleTherapeutic drug monitoring2016

Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software.

Michael Neely, Michael Philippe, Teresa Rushing, Xiaowei Fu, Michael van Guilder, David Bayard, Alan Schumitzky, Nathalie Bleyzac, Sylvain Goutelle

Abstract readComparative StudyValidation Study
In one paragraph

Article in Therapeutic drug monitoring, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 3 pooled it
13.1field-weighted citation impact, top 2% of its field
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

39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 64 citations in OpenAlex.

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  8. Risk factors of daptomycin overexposure: a case-control study.Antimicrobial agents and chemotherapy · 2025
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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

9 authors at 5 institutions in 2 countries.

Michael Neely*Laboratory of Applied Pharmacokinetics and Bioinformatics, Division of Pediatric Infectious Diseases, University of Southern California Children's Hospital Los Angeles; †Institute of Pediatric Hematology and Oncology; ‡Pharmacy department, Institute of Pediatric Hematology and Oncology, Hospices Civils de Lyon; §Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Lyon 1, Villeurbanne, France; ¶Pharmacy Department; and ‖Pathology and Laboratory Medicine, University of Southern California Children's Hospital Los Angeles.
Michael Philippe
Teresa Rushing
Xiaowei Fu
Michael van Guilder
David Bayard
Alan Schumitzky
Nathalie Bleyzac
Sylvain Goutelle
Children's Hospital of Los Angeles · USUniversity of Southern California · USCentre National de la Recherche Scientifique · FRHospices Civils de Lyon · FRLaboratoire de Biométrie et Biologie Evolutive · FR

Funding

Southern California Clinical and Translational Science InstituteUL1TR000130 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BUCHANAN, THOMAS A · 2012 to 2015
$36.0M
Ontogeny of Voriconazole Pharmaockinetics and MetabolismR01HD070886 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI NEELY, MICHAEL N. · 2012 to 2016
$2.5M
NCATS NIH HHS UL1 TR000130NICHD NIH HHS R01 HD070886
6 · The paper itself

Abstract

backgroundBusulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden.

methodsThe authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building.

resultsMean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (-2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was -9.2% (-16.7% to -1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (-0.3% to 4.2%, P = 0.08).

conclusionsBestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

Indexed as

Models, BiologicalAdministration, IntravenousAdolescentAlgorithmsAntineoplastic Agents, AlkylatingArea Under CurveBayes TheoremBiasBusulfanChildChild, PreschoolDose-Response Relationship, DrugHumansInfantSoftwareYoung AdultAntineoplastic Agents, AlkylatingBusulfan

Identifiers

PMID26829600
PMCPMC4864122
OpenAlexW2301518476

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.