Caracterización multimodal de la liberación sostenida de bioagentes desde un depósito epicárdico para la...

Epicardial delivery of therapies has the potential to prevent adverse remodeling and promote in situ regeneration after myocardial infarction (MI) but further optimization of bioagent dosing and transport to heart muscle is required to maximize their therapeutic potential. Replenishable reservoir systems have enabled localized bioagent delivery to the epicardial surface but therapy transport from these systems is constrained by semipermeable membranes and fibrous capsule formation. Our approach to improved therapy delivery from epicardial reservoir systems is multi-pronged. First, we introduce a membrane-free reservoir system by incorporating a gelatin scaffold into a flexible polymer implant to promote direct integration with the epicardial surface and act as a replenishable depot to encourage myocardial-directed transport. Next, we perform in vitro and ex vivo validations and multi-scale computational simulations to characterize biomaterial, tissue, and organ-level transport of therapy, considering both native tissue architecture, and the effect of blood vessel clearance. As an in vivo use case of our system, we investigated the functional effect of multi-dose regimens of human follistatin-like 1 protein (FSTL1) in a rat model of myocardial infarction (MI). Groups receiving multiple doses of FSTL1 show increased cardiac performance (ejection fraction and fractional shortening), and decreased chamber stiffness 28 days after MI. Multi-dosing increases ventricular wall thickness and reduces infarct size. We demonstrate a dose-dependent increase in blood vessel number and density in the infarct zone. Finally, we establish a computational and experimental framework for patient-specific modeling to optimize implant parameters such as reservoir size and shape, infarct location, and dosing regimens, with a vision for clinical-imaging guided bioagent delivery strategies that can be modified on a per-patient, therapy-specific basis to optimize dosing regimens of various bioagents. This study highlights the potential for integrating personalized computational models with replenishable delivery systems to improve bioagent transport from biomaterials and enhance post-MI therapeutic outcomes.