Design space determination of pharmaceutical processes: Effects of control strategies and uncertainty

The identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive - which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty.

Practical use of primary drying models in an industrial environment with limited availability of equipment sensors

In the pharmaceutical industry, lyophilization is typically adopted to extend long-time stability of valuable thermolabile medicines and vaccines. Primary drying is the most time-consuming and energy-intensive step of the entire process; thus, accelerating and optimizing the primary drying recipe is a key process development goal. To that purpose, mathematical models have been proposed and successfully validated. However, models typically require invasive experiments and/or sensors (e.g. product temperatures) for parameter estimation, which are rarely available in good manufacturing practice (GMP) environment. This represents a severe limitation when leveraging the model to transfer operation recipes across different facilities and for scale-up. In this study, we assess the possibility to exploit limited industrial data for model parameter estimation, namely pressure measurements and gravimetric tests, by defining a calibration protocol that is tested on two different pieces of equipment. Results are verified on a recently proposed model, and show that statistically meaningful estimates can be obtained without the need of product temperature measurements, and that model predictions and optimal inputs trajectories are comparable to those obtained from the model calibrated using the full set of temperature and pressure data.