Using Computational Tools to Understand Interactions between Osmolytes and Optimize the Preservation of Heterogeneous Populations of Primary Cells

Abstract

Immunotherapies such as chimeric antigen receptor (CAR) T-cell therapy are emerging therapies for the treatment of cancers and persistent viral infections. It is common for immunotherapy products to be collected in one site and processed in another site. Cryopreservation is the technology to stabilize cells at a low temperature for a variety of applications including diagnosis and treatment of disease. However, cryopreservation with the current gold standard, DMSO, can result in poor post-thaw recoveries and adverse reactions to patients upon transfusion. In this work, we propose to understand and optimize DSMO-free cryoprotectants with combinations of non-toxic and natural osmolytes including sugars, sugar alcohols and amino acids. The post-thaw recoveries of Jurkat cells display comparable performance to DMSO and non-linear interactions between osmolytes. Raman spectroscopy observes different protective properties of osmolytes, and statistical modeling characterizes the importance of osmolytes and their interactions. The differential evolution algorithm was applied to optimize the formulations of cryoprotectants previously, but the suboptimal control parameters reduce the performance. The influence of control parameters and four types of differential evolution algorithms are examined and optimized for DMSO-free cryoprotectants specifically. Additionally, we demonstrate that these DMSO-free cryoprotectants can cryopreserve human peripheral blood mononuclear cells as good as conventional DMSO. The advantages of DMSO-free cryoprotectants can improve the accessibility of cell therapy. It will also be critical to providing a methodology to develop multiple component DMSO-free cryoprotectants for other cell types.