There is great potential for using 3D printed designs fabricated via additive manufacturing processes for diverse biomedical applications. 3D printing offers capabilities for customizing designs for each new fabrication that could leverage automated design processes for personalized patient care, but there are challenges in developing accurate and efficient assessment methods. Here, we conduct a sensitivity analysis for a biological growth simulation for evaluating 3D printed lattices for regenerating bone and then use these simulations to identify performance trends. Four design topologies were compared by generating varied unit cells. Biological growth was modeled in a voxel environment by simulating the advancement of a tissue front by calculating its local curvature. Designs were generated with properties suitable for bone tissue engineering, namely 50% porosity and microscale pores. The sensitivity analysis determined trade-offs between prediction consistency and computation time, suggesting calculating curvature within a radius of 7.5 voxels is sufficient for most cases. Topologies were compared in bulk with design variations. All topologies had similar tissue growth rates for a given surface-volume ratio, but with differing unit cell sizes. These findings inform future optimization for selecting unit cells based on volume requirements and other criteria, such as mechanical stiffness. A fitted analytical relationship predicted tissue growth rate based on a design’s surface-volume ratio, which enables design evaluation without computationally expensive simulations. Lattices were 3D printed with biocompatible materials as proof-of-concepts, demonstrating the feasibility of the approach for future computational design methods for personalized medicine (Figure presented.).