Abstract
Abstract Subcutaneous (SC) dosing has become an increasingly popular non-oral route of administration for biotherapeutics due to its user-friendly nature and suitability for self-administration when compared to intravenous (IV) dosing. However, comprehensive methods for characterizing the post-injection depot within the complex SC tissue remain limited. In this study, we developed an ex vivo porcine skin model with magnetic resonance imaging (MRI) as a non-invasive and anatomically relevant tool to visualize injected solution depots formed after SC administration. Building upon this, a computational quantification approach using open-source Python libraries was established to calculate depot characteristics, such as effective depot volume, surface area, and surface-to-volume ratio. Using this integrated experimental-computational approach, we evaluated different injection parameters from both formulation and device perspectives with two model drugs, semaglutide and alirocumab. As the result, this method demonstrated its applicability to two different SC therapeutic modalities (peptide and mAb) with their range of injection volumes (0.05 to 1 mL), formulation concentrations (1 mg/mL to 10 mg/mL), and injection devices (prefilled syringe (PFS) and autoinjector (AI)). Notably, the measured effective post-injection depot volumes were consistently 1.2- to 1.6-fold larger than the injection volumes. Some differences in depot morphology were observed among injections of semaglutide with different formulation concentrations, while only minor variations were noted between injection devices. By integrating an ex vivo porcine skin model with MRI and open-source computational tools, this study presents a versatile and reproducible platform for 3D visualization and quantitative characterization of SC drug depots. Graphical abstract