Abstract
Background and objective
Radio-frequency ablation (RFA)-mediated thermosensitive liposomes (TSL) drug delivery promotes loco-regional drug delivery, simultaneously reducing systemic toxicity. However, the heterogeneous intratumoral microenvironment, such as non-uniformly distributed microvascular network, extracellular matrix, intracellular space, disproportionate blood flow through tumor microvessels, and disparate extracellular diffusion impedes the transport of intravenously administered TSL, significantly affecting therapeutic efficacy. The current study is focused on elucidating the effect of aforementioned tumor microenvironment heterogeneities on TSL drug delivery outcomes, which remains unexplored.
Methods
Different tumor heterogeneities, namely blood flow, extracellular drug diffusion, microvascular, extracellular and intracellular volume fractions, and 3D brain tumor geometry, are extracted using advanced magnetic resonance imaging (MRI) techniques. The extracted patient-specific information is incorporated in the bioheat-fluid-mass transport coupled solver developed in OpenFOAM to numerically predict the effect of each heterogeneity on drug delivery efficacy and compare with homogeneous scenarios.
Results
A significant underestimation of 70–72% in free drug and TSL extracellular concentrations and 56% in intracellular drug concentration is observed when using homogeneous properties compared to complete heterogeneous. Further, microvascular and extracellular volume fractions significantly affect the drug distribution patterns of the four heterogeneities, with blood flow and drug diffusion contributing the least.
Conclusions
The current study highlights the need to incorporate patient-specific tumor microenvironment heterogeneities in future modeling studies to predict the therapeutic outcomes of hyperthermia-related drug delivery treatments.
Radio-frequency ablation (RFA)-mediated thermosensitive liposomes (TSL) drug delivery promotes loco-regional drug delivery, simultaneously reducing systemic toxicity. However, the heterogeneous intratumoral microenvironment, such as non-uniformly distributed microvascular network, extracellular matrix, intracellular space, disproportionate blood flow through tumor microvessels, and disparate extracellular diffusion impedes the transport of intravenously administered TSL, significantly affecting therapeutic efficacy. The current study is focused on elucidating the effect of aforementioned tumor microenvironment heterogeneities on TSL drug delivery outcomes, which remains unexplored.
Methods
Different tumor heterogeneities, namely blood flow, extracellular drug diffusion, microvascular, extracellular and intracellular volume fractions, and 3D brain tumor geometry, are extracted using advanced magnetic resonance imaging (MRI) techniques. The extracted patient-specific information is incorporated in the bioheat-fluid-mass transport coupled solver developed in OpenFOAM to numerically predict the effect of each heterogeneity on drug delivery efficacy and compare with homogeneous scenarios.
Results
A significant underestimation of 70–72% in free drug and TSL extracellular concentrations and 56% in intracellular drug concentration is observed when using homogeneous properties compared to complete heterogeneous. Further, microvascular and extracellular volume fractions significantly affect the drug distribution patterns of the four heterogeneities, with blood flow and drug diffusion contributing the least.
Conclusions
The current study highlights the need to incorporate patient-specific tumor microenvironment heterogeneities in future modeling studies to predict the therapeutic outcomes of hyperthermia-related drug delivery treatments.
| Original language | English |
|---|---|
| Article number | 107390 |
| Number of pages | 24 |
| Journal | International Communications in Heat and Mass Transfer |
| Volume | 153 |
| Early online date | 14 Mar 2024 |
| DOIs | |
| Publication status | Published - 1 Apr 2024 |
Bibliographical note
Ajay Bhandari and Anup Singh acknowledge the support from the Science and Engineering Research Board (Grant Number: SRG/2021/000053) and (Grant Number: CRG/2019/005032), respectively. Ajay Bhandari and Wenbo Zhan acknowledge the support received from the Royal Society (Grant Number: IES\R1\221015).Data Availability Statement
The simulated data is accessible from the corresponding author upon reasonable request.Keywords
- RFA
- TSL
- tumor heterogeneity
- Advanced MRI
- Human Brain tumors
