Factorial Design of Experiments to Optimize Multiple Protein Delivery for Cardiac Repair

Myocardial infarction (MI) is a major cardiovascular disease responsible for millions of deaths annually. Protein therapies can potentially repair and regenerate the infarcted myocardium. However, because of the short half-lives of proteins in vivo, their low retention at the target tissue, and the lack of spatiotemporal cues upon injection, the efficacy of protein therapy can be limited. This efficacy can be improved by utilizing controlled release systems to overcome shortcomings associated with a direct bolus injection. Equally important is the determination of an optimal combination of different proteins having distinct roles in cardiac function and repairs to prevent or reverse the multiple pathologies that develop after infarction. In this work, we used a rat MI model to test a combination of potentially complementary proteins: tissue inhibitor of metalloproteinases 3 (TIMP-3), interleukin-10 (IL-10), basic fibroblast growth factor (FGF-2), and stromal cell-derived factor 1 alpha (SDF-1α). To achieve controlled and timed release of the proteins per their physiologic cues during proper tissue repair, we used a fibrin gel-coacervate composite. TIMP-3 and IL-10 were encapsulated in fibrin gel to offer early release, while FGF-2 and SDF-1α were encapsulated in heparin-based coacervates and distributed in the same fibrin gel to offer sustained release. We utilized a powerful statistical tool, factorial design of experiments (DOE), to refine this protein combination based on its improvement of ejection fraction 4 weeks after MI. We found that TIMP-3, FGF-2, and SDF-1α demonstrated significant contributions toward improving the ejection fraction, while the IL-10's effect was insignificant. The results also suggested that the higher doses tested for TIMP-3, FGF-2, and SDF-1α had greater benefit on function than lower doses and that there existed slight antagonism between TIMP-3 and FGF-2. Taken together, we conclude that factorial DOE can guide the evolution of multiple protein therapies in a small number of runs, saving time, money, and resources for finding the optimal dose and composition.