Evidence map›Paper›PMID 32078303›Full record

SynthesisACS nano2020

Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach.

Yi-Hsien Cheng, Chunla He, Jim E Riviere, Nancy A Monteiro-Riviere, Zhoumeng Lin

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in ACS nano, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
170citing papers in PubMed, 5 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

170 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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110 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

5 authors.

Yi-Hsien ChengInstitute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States.ORCID 0000-0002-4314-9732
Chunla HeInstitute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States.
Jim E RiviereInstitute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States.
Nancy A Monteiro-RiviereNanotechnology Innovation Center of Kansas State (NICKS), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States.ORCID 0000-0002-0132-0861
Zhoumeng LinInstitute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States.ORCID 0000-0002-8731-8366

Funding

Physiologically based pharmacokinetic modeling and analysis of administration route-dependent tissue distribution of gold nanoparticlesR03EB026045 · NIBIB · UNIVERSITY OF FLORIDA · PI LIN, ZHOUMENG · 2019 to 2020
$152k
Physiologically based pharmacokinetic modeling and analysis of nanoparticle delivery to tumorsR03EB025566 · NIBIB · KANSAS STATE UNIVERSITY · PI LIN, ZHOUMENG · 2017 to 2018
$152k
NIBIB NIH HHS R03 EB025566NIBIB NIH HHS R03 EB026045
6 · The paper itself

Abstract

Numerous studies have engineered nanoparticles with different physicochemical properties to enhance the delivery efficiency to solid tumors, yet the mean and median delivery efficiencies are only 1.48% and 0.70% of the injected dose (%ID), respectively, according to a study using a nonphysiologically based modeling approach based on published data from 2005 to 2015. In this study, we used physiologically based pharmacokinetic (PBPK) models to analyze 376 data sets covering a wide range of nanomedicines published from 2005 to 2018 and found mean and median delivery efficiencies at the last sampling time point of 2.23% and 0.76%ID, respectively. Also, the mean and median delivery efficiencies were 2.24% and 0.76%ID at 24 h and were decreased to 1.23% and 0.35%ID at 168 h, respectively, after intravenous administration. While these delivery efficiencies appear to be higher than previous findings, they are still quite low and represent a critical barrier in the clinical translation of nanomedicines. We explored the potential causes of this poor delivery efficiency using the more mechanistic PBPK perspective applied to a subset of gold nanoparticles and found that low delivery efficiency was associated with low distribution and permeability coefficients at the tumor site (

Indexed as

Disease Models, AnimalDrug Delivery SystemsAnimalsDrug CarriersGoldInjections, IntravenousMaleMetal NanoparticlesMiceMice, Inbred BALB CNeoplasmsTissue DistributionDrug CarriersGoldadvanced materialdrug deliverynanomedicinenanoparticlephysiologically based pharmacokinetic modelingtissue biodistributiontumor delivery

Identifiers

PMID32078303
PMCPMC7098057

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.