On-Chip Prediction of Combination Drug Efficacy Using a Magnetofluidic Platform with Integrated Capacitive Sensors

Area of Science:

• Biomedical Engineering
• Microfluidics
• Drug Discovery

Background:

• Lab-on-a-chip platforms offer potential for automated drug screening but often struggle with combination drug testing and precise dose control.
• Existing methods for combination drug screening are complex, require large sample volumes, and are prone to errors, hindering efficient dose optimization.
• Probing the efficacy of combination drugs on a microfluidic scale remains a significant challenge in pharmaceutical research.

Purpose of the Study:

• To develop a compact magnetically regulated microfluidic (MRM) platform for automated and rapid combination drug screening.
• To integrate interdigitated electrode-based capacitive sensors for real-time quantification of drug doses.
• To enable efficient identification of efficacious drug combinations and optimal dosing ratios for cancer cell treatment.

Main Methods:

• Designed a microfluidic device with 3 inlets, patterned magnets, and 15 isolated dispersion microchannels leading to 5 outlet wells.
• Utilized magnetic field-induced deflection to sort magnetically coated drug combinations at clinically relevant concentrations (10-485 μg mL⁻¹).
• Quantified delivered drug doses using integrated capacitive sensors and cultured MCF-7 human breast cancer cells to assess drug combination efficacy.

Main Results:

• Successfully sorted magnetically tagged drug combinations (Curcumin, Paclitaxel, Quercetin) within the microfluidic platform.
• Demonstrated real-time quantification of drug concentrations and identified the most effective combinations and ratios against MCF-7 cells.
• Developed an Ordinary Least Squares (OLS) regression model to predict drug efficacy, reducing the need for iterative experiments.

Conclusions:

• The developed MRM platform enables rapid, automated, and precise combination drug screening with integrated dose quantification.
• The platform facilitates the identification of optimal drug combinations and concentrations for targeted therapies, exemplified by its application to breast cancer cells.
• This approach significantly reduces experimental costs, reagent consumption, and time in drug discovery and development pipelines.