Patient-Specific Computational Modeling and Magnetic Nanoconstructs: Tools for Maximizing the Efficacy of Stem Cell-Based Therapies

摘要

Stem cell transplantation has the potential to restore heart function following myocardial infarction. However, the success of any stem cell-based therapy is critically linked to the effective homing and early engraftment of the injected cells at the infarcted site. Here, a hierarchical multiscale computational model is proposed for predicting the patient-specific vascular transport and intratissue homing and migration of stem cells injected either systemically or locally. Starting with patient-specific data, such as the vascular geometry, blood flow, and location of the infarcted area, the computational model can be used to perform parametric analysis to identify optimal injection conditions in terms of administration route, injection site, catheter type, and infusion velocity. In addition to this, a new generation of magnetic nanoconstructs is introduced for labeling stem cells and monitoring their behavior in vivo via magnetic resonance imaging. These nanoconstructs also can be used for multimodal imaging, merging MRI and nuclear imaging, and the intracellular delivery of active agents to support stem cell differentiation. The convergence of computational modeling and novel nanoconstructs for stem cell labeling could improve our understanding in cell homing and early engraftment at the infarcted site and thus pave the way to more effective stem cell-based therapies.