Evidence map›Paper›PMID 26296709›Full record

SynthesisDrug metabolism and disposition: the biological fate of chemicals2015

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Jennifer E Sager, Jingjing Yu, Isabelle Ragueneau-Majlessi, Nina Isoherranen

Open access · bronzeAbstract readSystematic Review
In one paragraph

Synthesis in Drug metabolism and disposition: the biological fate of chemicals, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 239 papers.

0numbers the graph read from it
0cells of the map it votes in
239citing papers in PubMed
36.0field-weighted citation impact, top 1% 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

239 citing papers in PubMed, 497 citations in OpenAlex.

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179 more citing papers are in PubMed but not listed here.

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

4 authors at 1 institution in 1 country.

Jennifer E SagerDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington.
Jingjing YuDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington.
Isabelle Ragueneau-MajlessiDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington.
Nina IsoherranenDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington ni2@u.washington.edu.
University of Washington · US

Funding

Pharmacological Sciences SupplementT32GM007750 · NIGMS · UNIVERSITY OF WASHINGTON · PI ATKINS, WILLIAM M · 1985 to 2023
$12.6M
Understanding Fetal Exposure to Cannabinoids Through In Vitro and In Vivo StudiesP01DA032507 · NIDA · UNIVERSITY OF WASHINGTON · PI UNADKAT, JASHVANT D · 2013 to 2023
$12.4M
NIDA NIH HHS P01 DA032507NIGMS NIH HHS T32 GM007750NIGMS NIH HHS T32-GM007750
6 · The paper itself

Abstract

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Indexed as

Computer SimulationModels, BiologicalAnimalsDrug InteractionsHumansPharmaceutical PreparationsPharmacokineticsPharmaceutical Preparations

Identifiers

PMID26296709
PMCPMC4613950
OpenAlexW2103606212

What OpenQuestion holds

Textmetadata
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.