Postdoctoral Fellowship: MPS-Ascend: Coarse-Grained Modeling of Aggrecan- Mimetic Copolymers: Polymer Design and Architecture Effects on Structure and Phase Behavior

NON-TECHNICAL SUMMARY

The proposed work will lead to the development of a materials design framework that uses molecular dynamics simulations and machine learning to design candidate polymeric materials that mimics the biomolecule aggrecan. Aggrecan is found in cartilage tissue, and the degradation of aggrecan has been implicated in the onset and progression of the disease osteoarthritis.

Osteoarthritis is a disease that afflicts the joints, where the cartilage tissue in the joint is deteriorated leading to pain, inflammation, and dysfunction of the joint. This disease affects senior citizens in particular, and it is believed to affect 10% of the population over the age of 60 and costs the US economy over $60 billion dollars annually.

Aggrecan mimetic polymers are seen as a promising approach to regenerative treatment for osteoarthritis. There are several challenges in developing treatments using aggrecan mimics that must be overcome. Key properties the candidate treatments must possess to overcome these challenges include injectability, biocompatibility, and immobility once in the treatment area.

This work will develop computational methods, using simulation and machine learning to design candidate polymers that possess the desired properties for use in cartilage treatment. This design framework can be further utilized to design materials for a variety of challenges in health and consumer products. The PI will work with their mentor and institution to broaden participation amongst underrepresented groups through an established training program for aspiring materials scientists as well as outreach activities aimed at providing community college students with guidance and exposure to STEM graduate education and research.

These efforts of the PI to broaden participation of underrepresented groups in STEM will also be informed by their past participation in diversity, equity and inclusion programs and past experiences as a community college student. TECHNICAL SUMMARY

The goal of the proposed work is to develop a computational model for designing aggrecan mimetic copolymers, that uses coarse-grained molecular simulations and machine learning methods, and is informed and validated by experimental data. The proposed copolymer will be made up of a thermoresponsive component and a charged component to effectively mimic the proteoglycan aggrecan, while achieving necessary material properties for use as an injection treatment.

The candidate must be biocompatible, injectable, and once in the treatment area, and must remain immobile in the target tissue. The PI proposes a copolymer made with the biocompatible and charged polymer poly(sodium-4-styrene sulfonate) (PSSNa) and the thermoresponsive polymer poly(N-isopropylacrylamide) (PNIPAm). The goal is to design a copolymer that possesses the charge density of aggrecan and undergoes a sol-gel transition at physiological temperatures.

The latter will ensure that the material is injectable at room temperature and is immobilized at body temperature in the treatment area. The PI will develop a coarse-grained model for the proposed copolymer, that will be parameterized and validated by experimental results provided by the collaborator on the proposed project. The PI will use the coarse-grained model in molecular dynamics simulations to model copolymers with a variety of copolymer design parameters, such as the copolymer architecture, sidechain grafting density, side chain length, and molecular weight.

The simulation and experimental data will be used to develop a data-driven forward and inverse model that can predict optimal polymer design for desired solution behavior and polymer chain conformation properties. The simulation and data-driven models will be used to generate candidate materials to serve as potential injection treatments for restoring cartilage damaged by the progression of osteoarthritis.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.