The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

We present a systematic and automatic approach for integrating tableting reduced-order models with upstream unit operations. The approach not only identifies the upstream critical material attributes and process parameters that describe the coupling to the first order and, possibly, the second order, but it also selects the mathematical form of such coupling and estimates its parameters. Specifically, we propose that the coupling can be generally described by normalized bivariate rational functions. We demonstrate this approach for dry granulation, a unit operation commonly used to enhance the flowability of pharmaceutical powders by increasing granule size distribution, which, inevitably, negatively impacts tabletability by reducing the particle porosity and imparting plastic work. Granules of different densities and size distributions are made with a 10% w/w acetaminophen and 90% w/w microcrystalline cellulose formulation, and tablets with a wide range of relative densities are fabricated. This approach is based on product and process understanding, and, in turn, it is not only essential to enabling the end-to-end integration, control, and optimization of dry granulation and tableting processes, but it also offers insight into the granule properties that have a dominant effect on each of the four stages of powder compaction, namely die filling, compaction, unloading, and ejection.

Background/Objectives: Glidants and lubricants are commonly used pharmaceutical excipients that enhance powder flowability and reduce inter-particle friction, respectively, but they also negatively impact critical quality attributes such as tablet tensile strength and drug release rate. Quantifying these effects is essential as the pharmaceutical industry transitions from batch to continuous manufacturing. Methods: This study develops a rational-function-based modeling approach to capture the effects of lubricants and glidants on tableting. The framework automatically identifies upstream critical material attributes and process parameters, such as excipient concentration and mixing time, and describes their coupling to first and second orders. Reduced-order models were constructed to evaluate the influence of these variables on the four stages of powder compaction—die filling, compaction, unloading, and ejection—using formulations composed of 10% acetaminophen, microcrystalline cellulose, and varying small concentrations of magnesium stearate or colloidal silica. Tablets were fabricated across a wide range of relative densities by varying dosing position and turret speed. Results: The modeling approach successfully quantified the effects of lubricant and glidant mixing conditions on each compaction stage, providing mechanistic insight into how upstream conditions propagate through the tableting process and influence critical quality attributes. Conclusions: Overall, the rational-function-based framework offers a systematic approach to quantify and predict the impact of lubricants and glidants on tablet performance, thereby enhancing product and process understanding in continuous manufacturing.