(161u) Directed Evolution of Polymers through Combinatorial Design & Statistical Learning: Applications in Gene Editing | AIChE

(161u) Directed Evolution of Polymers through Combinatorial Design & Statistical Learning: Applications in Gene Editing


Kumar, R. - Presenter, University of Mi
To fully realize the therapeutic potential of CRISPR/Cas9 gene editing, we must improve its affordability, safety, and accessibility by replacing engineered viral vectors with synthetic materials. Owing to their exquisite tunability and precision, polymers have emerged as promising class of biomaterials that can resolve challenges impeding the clinical translation of gene editing platforms. However, our ability to rapidly realize material properties that are tailored for specific therapeutic goals is handicapped by the vast design spaces opened up by polymer synthesis, and the limitations of ab initio models in predicting biological performance directly from polymer structure. Here, I will present an information-driven workflow for polymeric vector discovery that resulted in (1) the discovery of a highly efficient polymeric vehicle for genome editing payloads, that outperformed four state-of-the-art commercially available transfection reagents for in vitro delivery (2) the identification of structure-function relationships correlating polymer attributes to cellular toxicity, editing efficiency and payload uptake, informing the synthesis of subsequent polymer libraries. The union of high-throughput experimentation, machine learning, and imaging informatics can be leveraged to establish a polymer discovery pipeline that mimics evolutionary processes of mutation and natural selection. The workflow discussed herein demonstrates the possibility of translating statistically derived polymer design principles to therapeutically useful materials.

About Ramya Kumar:

Ramya obtained her B.E. (Hons.) in chemical engineering from BITS Pilani, India, and her PhD in chemical engineering at the University of Michigan, Ann Arbor, advised by Prof. Joerg Lahann. At Michigan, she received a Rackham Predoctoral fellowship, the Procter & Gamble Team Innovation award, and the Richard & Eleanor Towner Prize for creative and innovative teaching. Ramya is also an ACS PMSE Future Faculty awardee. Currently, Ramya is a postdoctoral associate at the University of Minnesota, Twin Cities where she has been developing materiomics workflows to accelerate polymeric vector discovery at Prof. Theresa Reineke’s group.