(672b) Introducing Contcarsim, a Benchmark Simulator for Design and Control of Continuous Integrated Filtration-Drying of Crystallization Slurries

Within the recent modernization momentum in the pharmaceutical industry, the transition to continuous manufacturing and the introduction of closed-loop control on quality have been identified as key enablers for delivering the next generation of pharmaceutical quality (Fisher et al., 2016; Destro et al., 2022). However, their practical adoption in industrial plants is still lagging behind, either because of the economic cost for their implementation, of the lack of personnel trained for this purpose, or even because of concerns about the regulatory approval of novel technology (Grangeia et al., 2020; Reinhardt et al., 2020).

In this presentation, we demonstrate advanced operation design, monitoring and control applications in pharmaceutical development and manufacturing through ContCarsim, a novel simulator for continuous integrated filtration-drying of crystallization slurries. ContCarSim, publicly released in the MATLAB environment, aims at promoting the adoption of advanced operation design, process monitoring and control by the pharmaceutical community within the quality-by-design and quality-by-control initiatives (ICH, 2009; Su et al., 2017), by making available a digital environment where the user can easily test and benchmark control strategies, or generate data for process analytics studies.

ContCarSim allows the user to simulate the operation of a novel carousel technology for continuous integrated filtration-drying, in normal operating conditions or under a set of different disturbance scenarios. Furthermore, the user-friendly structure of ContCarSim allows the implementation on the simulator of user-conceived control strategies, which may include advanced elements such as, for instance, model predictive control and state estimation. We demonstrate the use of ContCarSim through the implementation of a three-layer control system, featuring an end-point controller based on state estimation, which terminates filtration and drying cycles when the target product quality has been achieved. The proposed control system also includes a real time optimization routine, for maximizing the throughput of dry crystals obtained from the unit operation.

References

Destro, F., Hur, I., Wang, V., Abdi, M., Feng, X., Wood, E., Coleman, S., Firth, P., Barton, A., Barolo, M. and Nagy, Z.K., 2021. Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing. Chemical Engineering Science, 244, p.116803.

Destro, F., Barolo, M., 2022. A review on the modernization of pharmaceutical development and manufacturing-Trends, perspectives, and the role of mathematical modeling. Int. J. Pharm., p.121715.

Fisher, A.C., Lee, S.L., Harris, D.P., Buhse, L., Kozlowski, S., Yu, L., Kopcha, M., Woodcock, J. (2016). Advancing pharmaceutical quality: An overview of science and research in the U.S. FDA’s Office of Pharmaceutical Quality. Int. J. Pharm. 515, 390–402.

Grangeia, H.B., Silva, C., Simões, S.P., Reis, M.S. (2020). Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives. Eur. J. Pharm. Biopharm. 147, 19–37.

ICH (2009). Q8(R2) - Pharmaceutical development. Q8(R2). Available at: https://www.fda.gov/media/71535/download

Reinhardt, I.C., Oliveira, J.C., Ring, D.T. (2020). Current perspectives on the development of Industry 4.0 in the pharmaceutical sector. J. Ind. Inf. Integr. 18, 100131.

Su, Q., Moreno, M., Giridhar, A., Reklaitis, G. V., Nagy, Z.K. (2017). A Systematic Framework for Process Control Design and Risk Analysis in Continuous Pharmaceutical Solid-Dosage Manufacturing. J. Pharm. Innov. 12, 327–346.