journal article·2020-03-24Open access

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

ACS Nano· Volume 14· Issue 3

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 (P < 0.01). We also demonstrate how PBPK modeling and simulation can be used as an effective tool to investigate tumor delivery efficiency of nanomedicines.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoparticle-Based Drug Delivery · Field-Flow Fractionation Techniques · Radiation Therapy and Dosimetry

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States

Nanotechnology Innovation Center of Kansas State (NICKS), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas 66506, United States

1Data Consortium, Kansas State University, Manhattan, Kansas 66506, United States

Funding

National Institute of Biomedical Imaging and Bioengineering · Kansas State University · College of Veterinary Medicine, Kansas State University

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