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

Inclusion of physiologically relevant clearance mechanisms into organ-on-a-chip models is essential to reproduce tissue exposure and predict therapeutic efficacy, especially for local therapies and drug delivery applications that are already common in the clinic for ocular and cancer treatments. There remains a need for clearance-enabled organ-on-a-chips amenable to high throughput screening, especially with the emerging trend to expedite formulation and drug delivery vehicle (DDV) design with machine learning. To address this gap, we developed a microfluidic platform that incorporates continuous, pressure-driven clearance through interconnected microchannels and three-dimensional (3D) systems, enabling translational evaluation of local therapies and DDVs, such as injectable hydrogels, that aim to reduce systemic toxicity and enhance efficacy by prolonging drug residence at disease sites. In this study, fluorescent 4 and 65 kDa dextrans were used to confirm that pressure gradients across the platform promote efficient clearance versus passive diffusion. The pressure gradients were then applied to breast cancer spheroids co-cultured with macrophages in a fibrin hydrogel to evaluate the therapeutic efficacy of an interferon gamma (IFN-γ)-releasing agarose hydrogel in combination with anti-human epidermal growth factor receptor 2 (anti-HER2). Fluorescent imaging of spheroid area revealed increased cancer cell viability, lower drug efficacy, when continuous clearance was present, highlighting the impact of drug clearance. This study establishes the clearance-enabled microfluidic platform as a translationally relevant in vitro model for evaluating local therapies under continuous clearance, thereby bridging the gap between traditional static platforms and in vivo models for evaluating local pharmacokinetics and pharmacodynamics.Graphical abstractThe IFlowPlate-T models tunable clearance in 3D. IFN-γ and anti-HER2 efficacy decreased under pressure-driven flow, emphasizing clearance modeling.