Evidence map›Paper›PMID 39146980›Full record

ArticleJournal of controlled release : official journal of the Controlled Release Society2024

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.

Kun Mi, Wei-Chun Chou, Qiran Chen, Long Yuan, Venkata N Kamineni, Yashas Kuchimanchi, Chunla He, Nancy A Monteiro-Riviere, Jim E Riviere, Zhoumeng Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of controlled release : official journal of the Controlled Release Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–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

46 citing papers in PubMed.

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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

10 authors.

Kun MiDepartment 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.
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; Department of Environmental Sciences, College of Natural & Agricultural Sciences, University of California, Riverside, CA 92521, 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.
Venkata N KamineniDepartment 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.
Yashas KuchimanchiDepartment 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.
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.

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

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R

Indexed as

Machine LearningNanoparticlesNeoplasmsAnimalsAntineoplastic AgentsDrug Delivery SystemsMiceNanomedicineNeural Networks, ComputerTissue DistributionAntineoplastic AgentsArtificial intelligenceBiodistributionDeep neural network modelDrug deliveryMachine learningNanomedicineNanoparticlesQuantitative structure-activity relationship (QSAR)

Identifiers

PMID39146980
PMCPMC11886896

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.