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

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.

Keywords: advanced material; drug delivery; nanomedicine; nanoparticle; physiologically based pharmacokinetic modeling; tissue biodistribution; tumor delivery.

Figure 1

Procedure, strategies, and inclusion/exclusion criteria for the literature search. Following the literature search from the databases of Cancer Nanomedicine Repository (CNR) and PubMed and application of listed selection criteria, 200 tumor-bearing mouse studies with a total of 376 data sets published from 2005 to 2018 were identified for subsequent PBPK modeling and simulation analyses.

Figure 2

Schematic diagram of PBPK models in (A) healthy and (B) tumor-bearing mice intravenously administered with AuNPs and various inorganic and organic nanomaterials (INMs and ONMs), respectively. Except plasma and brain, each compartment is divided into three major parts: capillary blood, tissue interstitium, and endocytic/phagocytic cells (PCs) or tumor cells (TCs).

Figure 3

Representative simulation results from the PBPK model in tumor-bearing mice intravenously administered with various types of INMs, including (A) and (B) gold,^, (C) iron oxide, (D) gadolinium (Gd)-calcium phosphate (CaP), and (E) silica NMs as well as ONMs, including (F) liposome, (G) dendrimer, (H) hydrogel, (I) polymeric, (J) single-wall carbon nanotube (SWCNT), (K) ginseng extract, and (L) anticancer drug 10-hydroxycamptothecin (HCPT) NMs. Tumor tissue concentrations as presented in the y-axis are expressed in the units of percent of the injected dose (%ID),%ID/g, or μg/g according to units used in the original articles. R² is the coefficient of determination. Uppercase letters P and A followed by each legend represent passive and active targeting strategies, respectively. Abbreviations: A, active targeting; AuNP, gold nanoparticle (NP); BSA, bovine serum albumin; F, folate; FA, folic acid; G4 dendrimer, generation 4 polyamidoamine dendrimer; GNC, gold nanocluster; IONP, iron oxide NP; NC/ND/NR, nanocube/nanodisc/nanorod; P, passive targeting; PEG, polyethylene glycol; PSMA, prostate-specific membrane antigen; RGD, arginine-glycine-aspartic acid peptide; SNP, silica NP; Tat, peptide; Zn, zinc(II).

Figure 4

Subgroup analyses on tumor delivery efficiencies estimated at the last sampling time point according to the original literature (DE_Tlast) using our tumor-bearing PBPK model. Box-and-whisker plots of tumor delivery efficiency data (%ID) for different subgroups: (A) year, (B) targeting strategy, (C) type of nanomaterials (NMs), (D) inorganic NMs, (E) organic NMs, (F) shape, (G) hydrodynamic diameter, (H) ζ potential, (I) tumor model, and (J) cancer type. The boxes represent the 25th to 75th percentiles, and solid lines in the boxes indicate the median values. The pink dashed and solid lines denote the median and mean values of tumor delivery efficiencies derived from a previous study based on 193 published data sets from 2005 to 2015. The green dashed and solid lines stand for the median and mean values of tumor delivery efficiencies derived from the present study based on 376 published data sets from 2005 to 2018.

Figure 5

Proposed long-term strategy in facilitating the design of future nanomedicines and translation from preclinical studies to clinical applications, and the role of PBPK modeling and simulation approach in this field.