(235c) Formulating Biomolecular Gels for 3D Bioprinting Using High-Throughput Autonomous Microrheology

Formulating inks and resins for 3D bioprinting of cell-laden polymer gels presents a complex rheological optimization. If gelation kinetics are too slow, printability and 3D cell encapsulation can be compromised due to long solidification times; if gelation is too fast, cell viability is lost due to incompatibly high crosslinking densities. Finding an optimal “Goldilocks zone” can therefore require rheological screening of large formulation spaces of gelation kinetics, with material requirements that are prohibitive for precious materials including novel polymer chemistries and biomolecules. To address this challenge, we have developed a high-throughput, low-volume, fully autonomous rheology platform for polymer formulation discovery and design. Based on passive probe microrheology, the workflow combines robotic sample preparation, automated microscopy, custom-built high-throughput data analysis and ML-guided active learning to achieve a fully autonomous workflow that can be used to screen large formulation spaces with complex objective functions and minimal time and material requirements. As a demonstration, we apply the workflow to bioinks comprised of crosslinkable protein and peptide-synthetic hybrid materials, in which the optimization of gelation kinetics involves high-dimensional compositional search spaces. We show that achieving optimal gel times from this workflow can be achieved autonomously in less than 24 hours and with less than one milligram of polymer material. The resulting ink formulations exhibit acceptable cell encapsulation and viability without any additional optimization. These results therefore show considerable promise for using high-throughput autonomous microrheology to optimize formulations for bioprinting, and polymer additive manufacturing more broadly.