Evidence map›Paper›PMID 37771190›Full record

ArticleCPT: pharmacometrics & systems pharmacology2023

Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing.

Zachary L Taylor, Ethan A Poweleit, Kelli Paice, Katherine M Somers, Kathryn Pavia, Alexander A Vinks, Nieko Punt, Tomoyuki Mizuno, Sonya Tang Girdwood

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

29 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. The Coming of Age of Model-Informed Precision Therapeutics for Children.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 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.

Zachary L TaylorDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0001-5178-2397
Ethan A PoweleitDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0001-6805-2243
Kelli PaiceDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0003-0863-7316
Katherine M SomersDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0001-8683-9488
Kathryn PaviaDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0002-6231-3323
Alexander A VinksDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0003-4224-2967
Nieko PuntDepartment of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.ORCID 0000-0002-7655-6094
Tomoyuki MizunoDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0002-8471-9826
Sonya Tang GirdwoodDivision of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.ORCID 0000-0001-7737-8499

Funding

T32 Cincinnati Pediatric Clinical Pharmacology Training ProgramT32HD069054 · NICHD · CINCINNATI CHILDRENS HOSP MED CTR · PI TRACY A GLAUSER, Sonya C Tang Girdwood · 2011 to 2026
$3.0M
Antibiotic Model-Informed Precision Dosing in Critical IllnessR35GM146701 · NIGMS · CINCINNATI CHILDRENS HOSP MED CTR · PI Sonya C Tang Girdwood · 2022 to 2026
$2.0M
Predicting Psychiatric Readmission with Machine Learning in Children and AdolescentsF31MH132265 · NIMH · CINCINNATI CHILDRENS HOSP MED CTR · PI POWELEIT, ETHAN ANDREW · 2022 to 2023
$71k
NICHD NIH HHS T32 HD069054NIGMS NIH HHS R35 GM146701NIMH NIH HHS F31 MH132265
6 · The paper itself

Abstract

There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.

Indexed as

Models, BiologicalSoftwareBayes TheoremHumansPrecision Medicine

Identifiers

PMID37771190
PMCPMC10725261

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.