Conventional drug design has a long lead time and costly trial-and-error strategies in optimizing materials and manufacturing procedures. Here demonstrated is the combinational use of polymer chemistry, 3D printing, and machine learning in facilitating drug design. Specifically, the tissue scaffolds developed via our method will provide a solution to disease, pelvic floor prolapse (POP), common in females and professional athletes. The POP frequently happens after child delivery or frequent weight lifting, and there are no effective strategies to cure it via surgeries. We first designed polyvinyl alcohol (PVA), an FDA-approved biopolymer, with crosslinking networks that can be biocompatible and biodegradable. The biodegradability is controlled precisely by the crosslinking density and microenvironment conditions. Then we used the 3D printing technique (i.e., direct ink writing (DIW) or bioplotting) for porous media design. The pore size and porosity influencing mechanical durability need optimizations to bear the load from organs in the human body. Therefore, the printing structure needs to be consistent that in-situ monitoring and data analysis can help. Via in-situ imaging systems and machine learning procedures, the tissue scaffold structures and topologies showed superior and highly customizable features and exhibited high potential for personalized medicine. We will also show the culture experiments for cell proliferation and differentiation results.

Demonstration of polymer design, 3D printing, and machine learning for regenerative medicine applications

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.