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March 11, 2026Advanced Drug Delivery Reviews0 citationsOpen Access

From molecule to absorption: a multiscale framework for subcutaneous biologic delivery

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MLMario de LucioVSVivek D. SreeGSGalen H. Shi

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Abstract

Subcutaneous (SC) injection has become the preferred route for self-administered monoclonal antibodies (mAbs), yet achieving consistent bioavailability remains challenging because molecular-scale interactions (protein self-association) propagate through device mechanics (cavitation, sloshing) into tissue response (depot geometry, lymphatic uptake) as a coupled system where optimizing any single component in isolation yields diminishing returns. This review introduces the Drug-Device-Container-Tissue (DDCT) framework-a unified, multiscale approach that traces the monoclonal antibody (mAb) journey from storage through injection to lymphatic absorption. We synthesize findings from molecular dynamics simulations, rheological measurements, high-speed imaging, and poromechanical modeling to establish four key principles. First, concentration-dependent viscosity and diffusion arise from antibody self-association, directly impacting injectability and tissue transport. Second, device actuation induces sloshing, cavitation, and hydrodynamic shear, which can compromise protein stability; design strategies to mitigate these phenomena are now well-characterized. Third, device actuation parameters such as spring force and injection time critically influence delivery reliability and the risk of intramuscular injection. Fourth, tissue heterogeneity-including adipocyte geometry, collagen septa, and lymphatic distribution-governs depot formation and absorption kinetics in ways that patient-specific factors such as body mass index significantly modulate. By mapping these coupled phenomena across scales, the DDCT framework identifies cross-interface interactions as the critical bottleneck for optimizing SC delivery, providing a foundation for the rational design of next-generation autoinjectors, high-concentration formulations, and predictive computational tools.

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Lucio et al. (2026) studied this question.

synapsesocial.com/papers/6a0be62199ab7efa16d0bd54https://doi.org/10.1016/j.addr.2026.115857
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