Evidence map›Paper›PMID 32206211›Full record

ArticleComputational and structural biotechnology journal2020

A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery.

Prashant Dogra, Joseph D Butner, Javier Ruiz Ramírez, Yao-Li Chuang, Achraf Noureddine, C Jeffrey Brinker, Vittorio Cristini, Zhihui Wang

Open access · goldAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
5.1field-weighted citation impact, top 3% 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

51 citing papers in PubMed, 101 citations in OpenAlex.

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  19. Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.Journal of controlled release : official journal of the Controlled Release Society · 2024
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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

8 authors at 3 institutions in 1 country.

Prashant DograMathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA.
Joseph D ButnerMathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA.
Javier Ruiz RamírezMathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA.
Yao-Li ChuangDepartment of Mathematics, California State University, Northridge, CA 91330, USA.
Achraf NoureddineDepartment of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA.
C Jeffrey BrinkerDepartment of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA.
Vittorio CristiniMathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA.
Zhihui WangMathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA.
Houston Methodist · USUniversity of New Mexico · USCalifornia State University, Northridge · US

Funding

Transport Oncophysics CoreU54CA210181 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI CHANG, JENNY C-N · 2016 to 2020
$9.3M
NCI NIH HHS U54 CA210181
6 · The paper itself

Abstract

Towards clinical translation of cancer nanomedicine, it is important to systematically investigate the various parameters related to nanoparticle (NP) physicochemical properties, tumor characteristics, and inter-individual variability that affect the tumor delivery efficiency of therapeutic nanomaterials. Comprehensive investigation of these parameters using traditional experimental approaches is impractical due to the vast parameter space; mathematical models provide a more tractable approach to navigate through such a multidimensional space. To this end, we have developed a predictive mathematical model of whole-body NP pharmacokinetics and their tumor delivery

Indexed as

Cancer nanotherapyEnhanced permeability and retention effectMechanistic mathematical modelingPBPKPharmacokineticsSensitivity analysis

Identifiers

PMID32206211
PMCPMC7078505
OpenAlexW3007717897

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.