Predictive modeling and loose-loop control for perfusion bioreactors

Abstract

Perfusion bioreactors are widely used to produce recombinant proteins and monoclonal antibodies for therapeutic and diagnostic use. Better control of the cellular environment can lead to higher volumetric productivity, ensure product consistency and optimize medium utilization. The objective was to manipulate and control substrate concentrations in the perfusion bioprocess using predictive modeling and control. The goal of the predictive controller was to minimize future deviations from the set point concentration, by structuring the controller output. The appropriate structure for the future manipulated variable was specified using the selected model of glucose uptake rates (GUR). When there was a deviation from the set point value, the flow rates were adjusted to drive the process close to the set point value in a defined first order manner. The shape of the first order process response depended on the magnitude of the deviation from the set point value. With daily sampling and glucose measurement, a feed rate profile (eight flow rates per day) was specified to control the bioprocess. Despite the infrequent sampling, the predictive control protocols demonstrated glucose variation of less than 0.4 mM in transient conditions, and less than 0.2 mM in pseudo-steady-state conditions. The non-linear controller allowed for rapid changes in set point concentrations (6–9 h) or a reference trajectory to be followed. Set point changes and reference trajectories were simulated and tested with real process data. Modeling error and measurement bias were simulated to have the greatest potential effect during exponential growth. With good model estimation of the GUR, predictive control was able to maintain the process at the set point with a level of variability approaching that of the glucose assay.