Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies

Area of Science:

• Biomedical Engineering
• Pharmacology
• Cancer Research

Background:

• Organ-on-a-chip (OOC) models are crucial for predicting therapeutic efficacy, especially for local therapies.
• Current OOC models lack physiologically relevant clearance mechanisms, limiting their predictive power for drug delivery vehicles (DDVs).
• High-throughput screening OOCs with clearance are needed to accelerate DDV design using machine learning.

Purpose of the Study:

• To develop a microfluidic platform with continuous, pressure-driven clearance for OOC models.
• To enable translational evaluation of local therapies and DDVs, mimicking in vivo clearance.
• To assess the impact of drug clearance on therapeutic efficacy in a cancer model.

Main Methods:

• Developed a microfluidic platform with interconnected microchannels and 3D systems for pressure-driven clearance.
• Used fluorescent dextrans to validate efficient clearance over passive diffusion.
• Evaluated drug efficacy using breast cancer spheroids in a fibrin hydrogel with continuous clearance.

Main Results:

• The microfluidic platform demonstrated efficient clearance via pressure gradients.
• Continuous clearance led to increased cancer cell viability and reduced drug efficacy.
• This highlights the significant impact of drug clearance on local therapy outcomes.

Conclusions:

• Established a clearance-enabled microfluidic platform as a translationally relevant in vitro model.
• The platform bridges the gap between static OOCs and in vivo models for local pharmacokinetics and pharmacodynamics.
• This model is vital for evaluating local therapies and DDVs under realistic clearance conditions.