Evidence map›Paper›PMID 37499908›Full record

ArticleJournal of controlled release : official journal of the Controlled Release Society2023

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice.

Wei-Chun Chou, Qiran Chen, Long Yuan, Yi-Hsien Cheng, Chunla He, Nancy A Monteiro-Riviere, Jim E Riviere, Zhoumeng Lin

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of controlled release : official journal of the Controlled Release Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 1 pooled it
14.7field-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

89 citing papers in PubMed, 1 synthesis or guideline pooled it, 146 citations in OpenAlex.

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29 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

8 authors at 3 institutions in 1 country.

Wei-Chun ChouDepartment of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA.
Qiran ChenDepartment of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA.
Long YuanDepartment of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA.
Yi-Hsien ChengInstitute of Computational Comparative Medicine, Kansas State University, Manhattan, KS 66506, USA.
Chunla HeDepartment of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA.
Nancy A Monteiro-RiviereNanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, KS 66506, USA; Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC 27606, USA.
Jim E RiviereCenter for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC 27606, USA; 1Data Consortium, Kansas State University, Olathe, KS 66061, USA.
Zhoumeng LinDepartment of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32608, USA; Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32610, USA. Electronic address: linzhoumeng@ufl.edu.
University of Florida · USKansas State University · USNorth Carolina State University · US

Funding

Development of a web-based predictive model of nanoparticle delivery to tumors by integrating physiologically-based pharmacokinetic modeling with artificial intelligenceR01EB031022 · NIBIB · UNIVERSITY OF FLORIDA · PI Zhoumeng Lin · 2021 to 2026
$2.2M
NIBIB NIH HHS R01 EB031022
6 · The paper itself

Abstract

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R

Indexed as

NanoparticlesNeoplasmsAnimalsArtificial IntelligenceMiceModels, BiologicalTissue DistributionArtificial intelligenceDrug deliveryMachine learningNanomedicineNanotechnologyPhysiologically based pharmacokinetic modeling

Identifiers

PMID37499908
PMCPMC11008607
OpenAlexW4385329900

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.