(292c) Closed-Loop Spray Drying Process Development with in silico Solvent Screening and Process Modeling

This work presents a systematic framework for developing a closed-loop spray drying process by utilizing in silico solvent screening and process modeling. Computational modeling enables an integrated workflow to design the spray drying process, reducing experimental workload and enhancing efficiency within a Quality by Design (QbD) framework. As a widely used pharmaceutical manufacturing technique, spray drying plays a crucial role in formulating inhalers, vaccines, and oral solid dosage forms. In particular, it enhances the solubility and bioavailability of oral solid dosage drugs by converting crystalline drug substances into amorphous spray-dried dispersions (SDD). The spray drying process consists of three key steps: (1) solution preparation, (2) spray drying (primary drying) to produce wet SDD, and (3) post-drying (secondary drying) to achieve dry SDD. Each step requires careful design to optimize product quality and process efficiency.

Solvent selection is a critical challenge in solution preparation because it must balance several factors: safety, toxicity, boiling point, and solubility. Solubility is particularly important in determining the product morphology, stability, and overall sustainability of the spray drying process. In cases where pure solvents fail to dissolve drug substances effectively, binary or ternary solvent mixtures are often required to boost the solubility, increasing process complexity and analytical challenges. [1] This study applies Hansen’s solubility theory as a systematic approach to solvent screening. [2,3] Hansen solubility parameters (HSPs) comprise three cohesive energy contributions: dispersion forces, polarity, and hydrogen bonding. The HSP distance (Ra), calculated as a weighted Euclidean distance between solute and solvent HSPs, provides a quantitative ranking of solvent systems and facilitates the identification of optimal solvent combinations. Furthermore, HSP values can be predicted using chemical structures alone, streamlining solvent selection for new drug candidates before experimental validation.

Once an optimal solvent system is identified, process modeling plays a crucial role in designing a robust and feasible spray drying process. For instance, modeling can assist in determining the feasible operating space where the outlet temperature remains below the glass transition temperature, preventing unwanted recrystallization. [4] Additionally, drying kinetics significantly influence particle morphology. Rapid drying at higher outlet temperatures tends to produce hollow spherical particles, while slower drying at lower temperatures produces raisin-like structures. [5] This study develops process models to predict the influence of critical process parameters (CPPs) on critical quality attributes (CQAs), including residual solvent levels, glass transition temperature, droplet size, and particle size. Furthermore, key process variables such as outlet temperature and relative saturation are predicted to enhance process understanding and control. Thermodynamic models provide a quantitative assessment of how formulation and operating conditions impact product quality. In addition to defining the feasible design space through glass transition temperature and outlet temperature predictions, these models offer valuable insights into process design across various formulations and solvent systems. In closed-loop spray drying, the condenser is essential for solvent removal and drying gas recycling. While residual solvent levels in closed-loop systems are generally higher than in open-loop configurations, closed-loop operation offers significant sustainability and safety benefits, making it the preferred choice for large-scale pharmaceutical manufacturing. This study investigates the impact of condenser temperature on residual solvent levels in wet SDDs, glass transition temperature, and process design space. Additionally, atomization models are employed to predict droplet size, which serves as a critical input for determining particle size and drying time as well as providing guidance for scale-up. However, the applicability of atomization models remains limited to specific nozzle geometries and equipment configurations, underscoring the need for experimental validation during process transfer.

This work demonstrates an in silico decision-making framework for solvent selection and process design in closed-loop spray drying. These findings provide a foundation for accelerating spray drying development while addressing key challenges in solvent selection, process optimization, and scale-up.

References

[1] Qiu, J., Albrecht, J., & Janey, J. (2019). Synergistic solvation effects: enhanced compound solubility using binary solvent mixtures. Organic Process Research & Development, 23(7), 1343-1351.

[2] Hansen, C. M. (2007). Hansen solubility parameters: a user's handbook. CRC press.

[3] Walsh, D., Serrano, D. R., Worku, Z. A., Norris, B. A., & Healy, A. M. (2018). Production of cocrystals in an excipient matrix by spray drying. International Journal of Pharmaceutics, 536(1), 467-477.

[4] Dohrn, S., Rawal, P., Luebbert, C., Lehmkemper, K., Kyeremateng, S. O., Degenhardt, M., & Sadowski, G. (2021). Predicting process design spaces for spray drying amorphous solid dispersions. International Journal of Pharmaceutics: X, 3, 100072.

[5] Dobry, D. E., Settell, D. M., Baumann, J. M., Ray, R. J., Graham, L. J., & Beyerinck, R. A. (2009). A model-based methodology for spray-drying process development. Journal of pharmaceutical innovation, 4, 133-142.