(308f) Application of Process Modeling and Simulation for Quality Risk Management of Continuous Drug Product Manufacturing | AIChE

(308f) Application of Process Modeling and Simulation for Quality Risk Management of Continuous Drug Product Manufacturing


O'Connor, T., U.S. Food and Drug Administration
Koolivand, A., Food and Drug Administration
Continuous pharmaceutical manufacturing is an emerging technology that offers production rate flexibility, robustness, and product quality. Understanding process dynamics in relation to material properties (e.g. potency, material flow properties), equipment design (screw types for a feeder), and process conditions (e.g. mass flow rate) allows material traceability throughout the entire manufacturing line. Such understanding is considered essential for identification and mitigation of risks to product quality. Quality risk management is a broad system of considerations and practices, applied across the drug product lifecycle, that encompass the assessment, control, and communication of risks to product quality.

Process models can be used to identify potential risks by estimating the impact of potential variations in the process, equipment conditions, or incoming raw materials on product quality attributes. The control strategy adopted to mitigate the identified risks can be incorporated into the process model, enabling an evaluation of the control strategy’s effectiveness in ensuring product quality. Furthermore, process models can provide a formal structure for capturing process knowledge and assumptions, facilitating risk communication among various stakeholders, managing prior knowledge for its use in other product and process development.

In this work, we developed and validated process models for continuous direct compression (CDC) of tablets and demonstrated how these process models can aid the process risk assessment through sensitivity analysis, as well as the assessment of risk mitigation strategies through residence time distribution (RTD) analysis. Specifically, sensitivity analysis is used to quantitatively rank the relative effect of the uncertain inputs on model outputs and identify the process parameters and material attributes that are critical to product quality. The results of sensitivity analysis suggest that the API and the excipients density, their flowrates, the blender’s rpm, tablet die fill depth, and main compression force are the most significant factors that highly affect the process operability and tablet quality. Moreover, RTD analysis is used to examine process ability in mitigating the risk of upstream disturbances (i.e., variation in the feeder’s flowrate) on product quality. One critical technical challenge was successfully addressed when calculating the propagation of upstream disturbances through the entire process by convolution of the disturbance and RTD. In performing the convolution integral calculation, historical state information is required. However, it is not readily available in process modeling platforms. Thus, a novel approach is taken whereby a partial differential equation is used to propagate historical state information to the current state values. Our validated RTD model served as a tool for material traceability throughout the manufacturing line and facilitated setting the in-process control limits for the final blend composition. The presentation illustrates how these modeling approaches have been used in the regulatory assessment of drug applications employing CDC processes by identifying high risk areas, facilitating the evaluation of risk mitigation strategies, and fostering communications among stakeholders.


This abstract reflects the views of the authors and should not be constructed to represent FDA’s views or policies.