A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability.

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

An extended 3-compartment model for describing step change experiments in pharmaceutical twin-screw feeders at different refill regimes.

Residence time distributions (RTDs) are a valuable tool for product tracking in the unit operations of a continuous line for manufacturing pharmaceutical oral solid dosage (OSD) and the integrated system itself. The first unit operation in such a continuous line in which extended intermixing can occur, is typically a feeder. The RTD of a feeder can be obtained by performing tracer experiments with a tracer material. A physical interpretation can be given to the observed tracer concentration responses by fitting a tanks-in-series (TIS) or compartmental model to it. Consequently, the internal mixing behaviour inside the feeder hopper can be rationalized. However, typically, a constant volume is assumed for the tanks or compartments in these models. This has led to several publications where the experimental set-up does not violate the constant volume assumption, i.e. one performs refills at a high hopper fill level. Here, we step away from this assumption and develop a set of differential equations for a 3-compartment model in order to account for a non-constant volume of the compartments. Moreover, the model distinguishes between a bypass trajectory formed by the agitator inside the feeder and an inner mixing volume, in which the tracer concentration lags on the tracer concentration in the bypass volume. This compartmentalization was inspired by the results obtained in a previous study using a spatial sampling method to assess the tracer concentration throughout the feeder hopper for different experimental runtimes. The developed model successfully describes the step responses for different refill regimes: the standard smooth first order plus dead time response (FOPDT) for a high refill regime and the more complex step response, including dips in the rising phase of the curve, for the low refill regime. As a consequence, a more thorough understanding of the complex mixing behaviour inside the feeder is obtained, which allows for an improved traceability. Next to that, the model delivers enhanced knowledge on the interaction between the residence time and the refill regime. The developed model was fitted to a data set, containing step change experiments for different pharmaceutical materials (Tablettose 80 (T80), Microcelac 100 (MCL), and Avicel PH101 (MCC)), different mass flow rates, and refill regimes. The experimentally observed phenomena could be reliably described by the proposed model. The model showed an improved transferability compared to typical TIS models.