Enhanced Drug Delivery to Solid Tumors via Drug-Loaded Nanocarriers: An Image-Based Computational Framework

Abstract

Objective: Nano-sized drug delivery systems (NSDDSs) offer a promising therapeutic technology with sufficient biocompatibility, stability, and drug-loading rates towards efficient drug delivery to solid tumors. We aim to apply a multi-scale computational model for evaluating drug delivery to predict treatment efficacy.

Methodology: Three strategies for drug delivery, namely conventional chemotherapy (one-stage), as well as chemotherapy through two- and three-stage NSDDSs, were simulated and compared. A geometric model of the tumor and the capillary network was obtained by processing a real image. Subsequently, equations related to intravascular and interstitial flows as well as drug transport in tissue were solved by considering real conditions as well as details such as drug binding to cells and cellular uptake. Finally, the role of periodic treatments was investigated considering tumor recurrence between treatments. The impact of different parameters, nanoparticle (NP) size, binding affinity of drug, and the kinetics of release rate, were additionally investigated to determine their therapeutic efficacy.

Results: Using NPs considerably increases the fraction of killed cells (FKCs) inside the tumor compared to conventional chemotherapy. Tumoral FKCs for two-stage DDS with smaller NP size (20nm) is higher than that of larger NPs (100nm), in all investigate release rates. Slower and continuous release of the chemotherapeutic agents from NPs have better treatment outcomes in comparison with faster release rate. In three-stage DDS, for intermediate and higher binding affinities, it is desirable for the secondary particle to be released at a faster rate, and the drug with slower rate. In lower binding affinities, high release rates have better performance. Results also demonstrate that after 5 treatments with three-stage DDS, 99.6% of tumor cells (TCs) are killed, while two-stage DDS and conventional chemotherapy kill 95.6% and 88.5% of tumor cells in the same period, respectively.

Conclusion: The presented framework has the potential to enable decision making for new drugs via computational modeling of treatment responses and has the potential to aid oncologists with personalized treatment plans towards more optimal treatment outcomes.

Keywords: drug delivery; drug-loaded nanocarriers; image-based model; nanomedicine; solid tumors; treatment efficacy; tumor penetration.

Figure 1

Schematic of drug delivery mechanisms considered in the current study. (A) one-stage DDS or conventional chemotherapeutic delivery, (B) two-stage DDS (i.e., NP delivery), and (C) three-stage DDS.

Figure 2

Block diagram of the current study for computational modeling of drug transport of two-stage DDS.

Figure 3

(A) Real image of tumor, and (B) computational field considered in numerical simulation which is obtained by image-processing of realistic image.

Figure 4

Comparison of the results with previously published study (28) using FKCs over time.

Figure 5

Spatiotemporal distributions of concentrations of chemotherapy drug in tumor and its surrounding normal tissue with increasing time. These non-dimensional concentrations ${\tilde{C}}_{INT}$ were calculated at a given time by dividing that concentration at any point of geometry by the maximum concentration in the whole domain.

Figure 6

Spatiotemporal distributions of concentrations of drug-loaded NPs in tumor and its surrounding normal tissue with increasing time.

Figure 7

Spatiotemporal distributions of concentrations of three-stage NPs in tumor and its surrounding normal tissue with increasing time.

Figure 8

Comparison of treatment efficacy of two NP sizes for different release rates. K_ON was set to 15[m³/[(mol.s)] in all cases. (A) 100 nm, (B) 20 nm.

Figure 9

Comparison of treatment efficacy of two multi-stage delivery scenarios for different values of K_ON, K_el1, and K_el2. (A) NP1=100nm and NP2=10nm, (B) NP1=20nm and NP2=5nm.

Figure 10

Survival rate of TCs for one (conventional), two and three-stage drug delivery systems.