Current tissue-engineered (TE) products face the fundamental limitation of inadequate supply of nutrients and oxygen from the host blood vessels to the implanted cells. This transport is mainly limited by passive diffusion until the product is vascularized in vivo by angiogenesis. It results in the formation of a necrotic core of dead tissue at the center of the TE product. In the light of this problem, vascularization of TE products has received a lot of emphasis in recent times. It is now clear that for successful regeneration of three dimensionally complex and thicker tissues, a TE product must be vascularized prior to implantation. The built-in vasculature will efficiently deliver nutrients and remove waste products from the TE product. The primary goal of our research is to design and develop capillary flow networks with optimal transport characteristics for convective delivery of nutrients when integrated within natural biodegradable TE scaffolds for skin tissue engineering (Figure 1). To ensure smooth integration with the host vasculature, the pressure losses across these networks should be minimal. The flow distribution in these networks should be devoid of defects like dead volumes for achieving maximum transport efficacy.

Our network design consists of planar bifurcating channels of micron dimensions, which are optimized for maximum mass transport efficiency and minimum pressure drop. Previously we developed and optimized a method to fabricate these optimal networks in natural biodegradable collagen-glycosaminoglycan (GAG) matrices. In this work, we will present results of numerical simulations of pressure drop and flow distribution in these networks. We will also discuss results of experimentally verifying the pressure drop as well as quantifying the flow distribution in PDMS analogs of the flow networks.

By using standard photo/soft lithography techniques, the network designs were obtained in poly-dimethyl siloxane (PDMS) molds. Subsequently, microfluidics was established in the PDMS networks and the differential pressure drop across the network was measured using an amplified pressure sensor. The pressure drop-flow rate relationship was obtained as a function of network generations and network porosity. All devices obey the Poiseuille flow behavior. In addition, flow velocities were measured at different segments of the network devices using the technique of particle image velocimetry (PIV) and the flow distribution efficacy of the networks was quantified. Further, we used a commercially available Finite Elements Method (FEM) software, Comsol Multiphysics^TM to numerically simulate the pressure drop and velocity fields in the network designs, and compared the results with those from the aforementioned experiments. Figure 2 is a comparison between experimental and numerical pressure drop values with flow rate for a 6^th generation microfluidic network. Collectively, the results validate our approach for incorporating synthetic ‘vascular' networks with TE products.