(646b) Integration of In-Silico Tools and High-Throughput Automation for Enhanced Crystallization Development

The presentation will discuss a case study, where multiple in-silico solubility prediction tools and high throughput automation were utilized to accelerate solubility and morphology screening in binary and ternary solvent systems. Regression-based prediction tools using single solvent solubility data and first-principle models were used to identify potential solvent systems to improve crystallization performance. Over 85 solvent combinations were prepared using an automated platform for solubility measurement and morphology screening, and several synergistic solubility behaviors were observed. Experimental solubility trends conformed well with in-silico predictions, indicating these tools can be a beneficial first screen at identifying synergistic solvents to explore. The presentation will delve into the utility of an integrated approach comprising of the solubility modeling and automated experimental screening platform in the efficient data generation to enable timely crystallization process development.

All authors are employees of AbbVie and may own AbbVie stock. AbbVie sponsored and funded the study; contributed to the design; participated in the collection, analysis, and interpretation of data, and in writing, reviewing, and approval of the final publication.