(W0930-02-10) Enabling Advanced Manufacturing Technologies and Quality by Control (QbC) with the Implementation of Advanced Process Control

Purpose: This study highlights the transformative potential of model-based, closed-loop control strategies in advancing pharmaceutical manufacturing from traditional Quality by Design (QbD) toward a more agile and efficient Quality by Control (QbC) paradigm. Focusing on a high shear wet granulation–fluid bed drying (HSWG–FBD) process train, we integrated real-time monitoring technologies such as Near-Infrared (NIR) spectroscopy and Particle Vision and Measurement (PVM) to actively control critical quality attributes (CQAs), including moisture content and granule size distribution (GSD). By enabling adaptive, material-aware control, this approach significantly reduces development time and cost>, eliminating the need for extensive Design of Experiments (DoEs) when introducing new APIs. Moreover, the closed-loop feedback system enhances process robustness and flexibility, effectively managing variability, setpoint changes, and process disturbances

Methods: To evaluate and optimize a Fluidized Bed Drying process from a process control perspective, this experimental method focused on tightly controlling critical quality attributes (CQAs) related to drying efficiency and granule integrity. The APC method was implemented with two main goals. The first one was to control the final granule moisture to achieve a final moisture level ( < 2.5%w/w) within a specified drying duration. The purpose was to ensure process efficiency while avoiding under- or over-drying, which can affect downstream tabletability and stability. The second objective was focused on the granule integrity and particle size distribution (PSD) Control. The goal of this objective was to maintain granule structure during drying—avoiding attrition, breakdown into fines, or undesired agglomeration. The particle size distribution must remain consistent to ensure uniformity and predictable tablet properties. The measured CQAs were the drying time, the moisture endpoint and the consistency in PSD. The PSD Metrics for Monitoring and Control were based on the real-time or offline measurements of the granule D10, D50, and D90 and the granules span. A lower span indicates tighter control of granule size and higher batch uniformity. The rational of the APC method was to provide a structured approach to designing experiments for FBD process optimization by targeting the key parameters that drive product performance and process robustness. The APC control was used in conjunction with Process Analytical Technologies (PAT) and model-based control strategies to establish feedback loops and enable Quality by Control (QbC).

Results: This study implemented a real-time, model-based Advanced Process Control (APC) strategy for Fluid Bed Drying (FBD)—a key unit operation in solid oral dosage manufacturing—focusing on two critical quality attributes (CQAs): moisture content and granule size distribution (GSD). The APC strategy was deployed in three integrated phases: Phase 1 – Modeling & Integration: Developed chemometric and mechanistic models using DoE, PAT data fusion, and hybrid modeling approaches. Phase 2 – APC Development: Built in silico APC strategies and deployed them in the live automation system at the New Brunswick site. Phase 3 – Performance evaluation: Assessed controller performance using live production data, KPI dashboards, and sensor quality checks. Real-time monitoring tools—NIR spectroscopy and Particle Vision and Measurement (PVM)—enabled continuous feedback and data-driven control. A hybrid modeling framework incorporating a physics-based digital twin, PAT data, and real-time automation feedback allowed predictive moisture control through sensor fusion. Quantitative Results Demonstrating Impact: 75% reduction in standard deviation of Span, reflecting a substantial decrease in variability of the particle size distribution. 13% increase in average d10 at the drying endpoint, indicating lower fines generation and improved granule uniformity. Larger particle size observed in APC batches compared to open-loop runs, suggesting reduced granule fragmentation and better preservation of granule integrity under closed-loop control. This work demonstrated the value of combining closed-loop APC, PAT integration, and digital twin modeling to Enhance process robustness and product consistency, accelerate development and reduce experimentation needs, improve scalability and technology transfer efficiency and advance toward smart, adaptive, and Industry 4.0-ready manufacturing systems. These results support the transition from Quality by Design (QbD) to Quality by Control (QbC) in pharmaceutical processes.

Conclusion: This study demonstrates that accurate real-time moisture monitoring combined with Advanced Process Control (APC) delivers substantial and scalable benefits to pharmaceutical manufacturing. The successful deployment of a physics-driven, hybrid modeling framework for the Fluid Bed Drying (FBD) process established a replicable, process-agnostic strategy for real-time control that can be extended to other unit operations. This work confirmed the effectiveness of closed-loop APC and sensor fusion for FBD. It also laid the foundation for smart manufacturing—providing a transferable digital control architecture that aligns with Quality by Control (QbC) and Industry 4.0 goals.

References: 1. Arden, N.S., et al., Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future. International Journal of Pharmaceutics, 2021. 602: p. 120554.
2. Laske, S., et al., A Review of PAT Strategies in Secondary Solid Oral Dosage Manufacturing of Small Molecules. Journal of Pharmaceutical Sciences, 2017. 106(3): p. 667-712.
3. Destro, F. and M. Barolo, A review on the modernization of pharmaceutical development and manufacturing – Trends, perspectives, and the role of mathematical modeling. International Journal of Pharmaceutics, 2022. 620: p. 121715.
4. Huang, J., et al., AIChE PD2M Advanced Process Control workshop-moving APC forward in the pharmaceutical industry. Journal of Advanced Manufacturing and Processing, 2021. 3(1): p. e10071.
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The figure shows the digital integration and the PAT tool integration in the Advanced Process Control (APC)

1) Process dashboards showing the cross-over point detected in real time, signaling the end of the drying cycle 2) The drying process is perturbed during the open loop runs to generate statistically rich data for model development



1) The %fines and Span shows a reduction of variability by 75% 2) The average D10 at the end point of all APC batches is increased by 13% indicating lower fines generation