(560i) From Vial to MSMPR, an Automatable Workflow for APIs Purification

The digitalization of drug substance process development has accelerated dramatically in recent years, driven by the adoption of automation,¹ advanced process control, and data-centric modeling frameworks.² In synthetic chemistry, feedforward control architectures have demonstrated the capability to maintain optimal operation and product quality under continuous conditions.³

In crystallization science, automated and material-sparing kinetic workflows⁴ and data-rich model-based frameworks^5,6 are beginning to bridge the gap between laboratory experimentation and predictive digital twins.⁷ Despite these advances, crystallization process development still lags behind, often relying on empirical screening and limited data integration, underscoring the need for automatable and structured workflows that connect preformulation to scalable operation.⁶

This work introduces a vial-to-MSMPR workflow for API purification that provides a systematic and automatable framework to transition from solubility screening to steady-state crystallization. The workflow integrates solubility and impurity-partitioning studies with offline analytical tools (HPLC, XRPD, and DSC) to evaluate yield, purity, and polymorphic stability. Using less than 3 g of material, the optimal solvent system and operating policy are identified within one week. A two-stage mixed-suspension, mixed-product-removal (MSMPR) crystallizer serves as a scalability accelerator, enabling steady-state and kinetic evaluation in a single experiment.

By emphasizing minimal material use, data traceability, and modular instrument control, this workflow is readily automatable and compatible with emerging digital infrastructures for process development. It offers a practical bridge between discovery-scale screening and digitally connected, scalable crystallization—advancing the broader transformation toward smart, data-driven purification workflows envisioned under Pharma 5.0.

(1) Lunt, A. M.; Fakhruldeen, H.; Pizzuto, G.; Longley, L.; White, A.; Rankin, N.; Clowes, R.; Alston, B.; Gigli, L.; Day, G. M.; Cooper, A. I.; Chong, S. Y. Modular, Multi-Robot Integration of Laboratories: An Autonomous Workflow for Solid-State Chemistry. Chem. Sci. 2023, 15 (7), 2456–2463. DOI:10.1039/d3sc06206f.

(2) Griffin, D. J.; Coley, C. W.; Frank, S. A.; Hawkins, J. M.; Jensen, K. F. Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development. Org. Process Res. Dev. 2023, 27 (11), 1868–1879. DOI:10.1021/acs.oprd.3c00229.

(3) Hsieh, H. W.; Griffin, D. J.; Nambia, A.; Sarkar, N.; Ismail, H. Y.; Saigal, K.; Shen, D. E.; Goudas-Salomon, N.; Wimalasinghe, R.; Zeng, A.; Thiel, O. R.; Beaver, M. G. PAT-Enabled Automated Feedforward Control: An Application to the Continuous Manufacture of Apremilast Hsiao-Wu. Org. Process Res. Dev. 2024, 28, 2844–2853. DOI:10.1021/acs.oprd.3c00400.

(4) Arruda, R. J.; Cally, P. A. J.; Wylie, A.; Shah, N.; Joel, I.; Leff, Z. A.; Clark, A.; Fountain, G.; Neves, L.; Kratz, J.; Thorat, A. A.; Marziano, I.; Rose, P. R.; Girard, K. P.; Capellades, G. Automated and Material-Sparing Workflow for the Measurement of Crystal Nucleation and Growth Kinetics. Cryst. Growth Des. 2023, 23 (5), 3845–3861. DOI:10.1021/acs.cgd.3c00252.

(5) Angulo, A.; Mcmullen, J. P. Data-Driven Modeling for the Enhanced Understanding for the Crystallization of an Active Pharmaceutical Ingredient. Org. Process Res. Dev. 2025, online.

(6) Barhate, Y.; Kilari, H.; Wu, W. L.; Nagy, Z. K. Population Balance Model Enabled Digital Design and Uncertainty Analysis Framework for Continuous Crystallization of Pharmaceuticals Using an Automated Platform with Full Recycle and Minimal Material Use. Chem. Eng. Sci. 2024, 287 (July 2023), 119688. DOI:10.1016/j.ces.2023.119688.

(7) Pickles, T.; Leghrib, Y.; Weisshaar, M.; Goncharuk, M.; Timperman, P.; Doherty, T.; Ford, D. D.; Moores, J.; Florence, A. J.; Brown, C. J. Digital Discovery Automated Scale-up Crystallisation DataFactory for Model-Based Pharmaceutical Process. 2025. DOI:10.1039/d4dd00406j.