(308c) Development of RTD-Based Flowsheet Modeling Including Process Uncertainty for Continuous Solid-Based Drug Manufacturing

Over the past twenty years, there has been a trend in pharmaceutical industry to commercialize continuous manufacturing (CM) for the production of solid-based drug products, given the various advantages of CM and the alignment with FDA’s support of the quality-by-design paradigm (Lee et al., 2015; Plumb, 2005; Schaber et al., 2011). Overall, CM has the potential to reduce production costs while improving product quality (Ierapetritou, Muzzio, & Reklaitis, 2016; Schaber et al., 2011). To fully utilize the above benefits for CM, it is important to properly design the process to guarantee the production of qualified drug products. Process design relies heavily on detailed understanding of the process characteristics, which gives rise to the need for developing process models. A variety of modeling techniques have been developed in the literature, including discrete-element method (DEM), population balance modeling (PBM), residence time distribution (RTD) modeling, and semi-empirical modeling (Ierapetritou et al., 2016). We focus on RTD modeling in this study because it is well-suited for building integrated flowsheet models with low computational cost while maintaining sufficient degree of characterization of the process behavior, particularly from a process dynamics point of view. This implies that given an upstream disturbance, the system response can be predicted using convolution of the developed RTD models with the disturbance. A number of studies have been conducted to develop RTD-based flowsheet models for different manufacturing routes (Martinetz et al., 2018; G. Tian et al., 2021; Geng Tian et al., 2017), and disturbance propagation has been tracked downstream to quantify its effects on final drug product quality.

RTD model parameters are determined based on RTD experiments conducted under steady-state operation. Maintaining a steady state during manufacturing can get difficult due to numerous unpredictable events like process fluctuation of flowrates, changes in flow patterns within unit operations and human-based variabilities. Thus, there is a certain degree of uncertainty inherently associated with the system and corresponding RTD models. The uncertainty in RTD characterization can lead to significant implications for RTD flowsheet applications like material traceability and diversion of out-of-specification (OOS) material. The uncertainty in RTD of process flowsheets can prohibit identifying the true start and endpoints of OOS material, leading to production of OOS tablets. Thus, it is important to consider this inherent process uncertainty and properly characterize its range and implications on drug product quality. To this end, we have developed a two-stage methodology encompassing quantification of the degree of uncertainty associated with RTD profiles for various unit operations and investigation of uncertainty propagation along the downstream unit operations of the manufacturing line to obtain the overall RTD flowsheet model incorporating process uncertainty. The updated RTD flowsheet model can then be used to demonstrate its ability for disturbance propagation and precise determination of OOS products. The proposed methodology would pave the way for robust and efficient solid-based drug production as it provides a unique strategy to incorporate the effects of process uncertainty in maintaining drug product quality for CM applications.

References:

Ierapetritou, M., Muzzio, F., & Reklaitis, G. (2016). Perspectives on the continuous manufacturing of powder-based pharmaceutical processes. AIChE Journal, 62(6), 1846-1862. doi:10.1002/aic.15210

Lee, S. L., O’Connor, T. F., Yang, X., Cruz, C. N., Chatterjee, S., Madurawe, R. D., . . . Woodcock, J. (2015). Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production. Journal of Pharmaceutical Innovation, 10(3), 191-199. doi:10.1007/s12247-015-9215-8

Martinetz, M. C., Karttunen, A. P., Sacher, S., Wahl, P., Ketolainen, J., Khinast, J. G., & Korhonen, O. (2018). RTD-based material tracking in a fully-continuous dry granulation tableting line. Int J Pharm, 547(1-2), 469-479. doi:10.1016/j.ijpharm.2018.06.011

Plumb, K. (2005). Continuous Processing in the Pharmaceutical Industry. Chemical Engineering Research and Design, 83(6), 730-738. doi:10.1205/cherd.04359

Schaber, S. D., Gerogiorgis, D. I., Ramachandran, R., Evans, J. M. B., Barton, P. I., & Trout, B. L. (2011). Economic Analysis of Integrated Continuous and Batch Pharmaceutical Manufacturing: A Case Study. Industrial & Engineering Chemistry Research, 50(17), 10083-10092. doi:10.1021/ie2006752

Tian, G., Koolivand, A., Gu, Z., Orella, M., Shaw, R., & O'Connor, T. F. (2021). Development of an RTD-Based Flowsheet Modeling Framework for the Assessment of In-Process Control Strategies. AAPS PharmSciTech, 22(1), 25. doi:10.1208/s12249-020-01913-8

Tian, G., Lee, S. L., Yang, X., Hong, M. S., Gu, Z., Li, S., . . . O'Connor, T. F. (2017). A dimensionless analysis of residence time distributions for continuous powder mixing. Powder Technology, 315, 332-338. doi:10.1016/j.powtec.2017.04.007