An ode model for optimal operation of T-cell culture in suspension system

Abstract

Background & Aim Manufacturing requires sufficient understanding of the variables and associated mechanisms for adequate control and risk analysis. In the case of cell therapies, relatively complex relationships between inputs/parameter controls and process outcomes demands suitable models to define/optimise for manufacturing operation. Our aim is to take a Quality by Design approach to process control of cell therapy manufacture that addresses autologous cell therapies variability in patient-specific source material through first principles models. Methods, Results & Conclusion Our Ordinary Differential Equation (ODE) model delivers the required process operation based on the premise of T cells subpopulation specific secretome and paracrine/autocrine sensitivity. The mechanistic T cell model allows prediction of variability in process outcomes with respect to input variability and thereby will identify process operation with acceptable risk and opportunities for process optimization. Ambr15 platform was used to validate the ODE model with respect to input variability including; cell seeding density, feeding ration, frequency and glucose concentration in bulk medium. Our results demonstrated a simple unstructured model of T-cell growth in a stirred tank system which provides framework for operation with respect to timings and volume of medium delivery to maintain an uninhibited growth. Such a model can also indicate how glucose delivery can be controlled to sustain cell growth while selectively affect the growth of specific sub-population of T cells.