Cell and gene therapies, such as chimeric antigen receptor T cell therapies (CAR-T), have led to some miraculous cures that are transforming the clinical landscape for treating certain types of tumors, such as B cell lymphomas and various blood cancers. Despite its great promise, many barriers continue to limit the effectiveness of CAR-T therapies. One of the largest barriers is developing reliable potency assays for these cell-based medicines, which by nature are highly heterogeneous. An engineered lot will inevitably have a distribution of growth properties, cytolytic activity, gene expression patterns, and secretion profiles, and it is not clear which cell subpopulations are most correlated with positive clinical outcomes. Since each lot of drug uses a patient’s own cells, drug testing must be performed on every patient, which magnifies the need for reliable potency assays. In this talk, I will summarize the current status and future directions of the field of quality control analysis of cell based medicines, while focusing on the emergence of single cell based functional screening tools that can measure the cytolytic properties of individual T cells. In particular, I will summarize our recent results on a platform that we are developing at Celldom, which uses a high density microwell array and high throughput image analysis techniques to analyze the statistical distribution of T-cell cytolytic properties across a range of effector to target ratios (E:T). I will also describe the applications of our functional single cell screening tools in other applications, such as BCR and TCR discovery, cell line development, and drug resistance testing for applications in patient stratification.
1. Discuss the different types of quality control metrics and manufacturing controls required for hetereogeneous cell based medicines.
2. Discuss the importance of multi-parameter methods for characterizing single effector cells in cocultures.
3. Describe the importance of quantifying rare cell responses in a sample, and their effect on clinical outcomes.