(657g) Digital Design and Optimization of a Continuous Counter-Current Extraction Process for Purifying Active Pharmaceutical Ingredients.

Continuous manufacturing (CM) has attracted increased attention in the pharmaceutical industry, due to its promise of enhanced process and product performance, as well as improved safety and cost over traditional batch operations [1,2]. In current practice, process development and suitable control strategies are typically based on heuristic approaches or experimental investigations. Such protocols often lead to suboptimal designs and are usually accompanied by increased development time, high material consumption, and increased environmental footprint. The use of modelling tools and optimization techniques can help reduce experimental work, improve decision-making, and bring products to market faster.

In this work, we focus on the development of continuous counter-current extraction processes, which are often employed in the separation of chemicals with low distribution coefficients [3]. We use advanced process modeling tools to design a multistage counter-current extraction process for separating an active pharmaceutical ingredient (API) from a key dimer impurity generated during synthesis. Thermodynamic methods and data-based correlations are utilized to predict improved partition coefficients and enhance extraction efficiency. Uncertainty analysis and optimization [4] have been performed to identify optimal process parameters that lead to high product yield and purity. The developed process model is used to complement experimental work and facilitate decision making in process development [5]. In addition, a residence time distribution model was developed to inform and improve process decisions in large-scale production. Overall, this work demonstrates the benefits of using continuous processes and emphasizes the impact of integrated computational and experimental workflows on pharmaceutical process development.

[1] C.L. Burcham, A.J. Florence, M.D. Johnson, 2018. Annual Review of Chemical and Biomolecular Engineering 9, 253-281.

[2] N. Yazdanpanah, C.N. Cruz, T.F. O’Connor, 2019. Computers and Chemical Engineering 129, 1065110.

[3] J. Chen, C. Zhou, B. Xie, J. Zhang, 2023. I&EC Research, doi.org/10.1021/acs.iecr.2c04282.

[4] R. Schenkendor, D.I. Gerogiorgis, S.S. Mansouri, K.V. Gernaey, 2020. J Processes 8, 2227-9717.

[5] F. Zhao, I.E. Grossmann, S. García-Muñoz, S.D. Stamatis, 2021. AIChE Journal, DOI: 10.1002/aic.17189.