Automated solubility screening platform using computer vision

Parisa Shiri; Veronica Lai; Tara Zepel; Daniel Griffin; Jonathan Reifman; Sean Clark; Shad Grunert; Lars P E Yunker; Sebastian Steiner; Henry Situ; Fan Yang; Paloma L Prieto; Jason E Hein

doi:10.1016/j.isci.2021.102176

Automated solubility screening platform using computer vision

iScience. 2021 Feb 12;24(3):102176. doi: 10.1016/j.isci.2021.102176. eCollection 2021 Mar 19.

Authors

Affiliations

¹ Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
² Amgen Inc., Cambridge, MA 02141, USA.

Abstract

Solubility screening is an essential, routine process that is often labor intensive. Robotic platforms have been developed to automate some aspects of the manual labor involved. However, many of the existing systems rely on traditional analytic techniques such as high-performance liquid chromatography, which require pre-calibration for each compound and can be resource consuming. In addition, automation is not typically end-to-end, requiring user intervention to move vials, establish analytical methods for each compound and interpret the raw data. We developed a closed-loop, flexible robotic system with integrated solid and liquid dosing capabilities that relies on computer vision and iterative feedback to successfully measure caffeine solubility in multiple solvents. After initial researcher input (<2 min), the system ran autonomously, screening five different solvent systems (20-80 min each). The resulting solubility values matched those obtained using traditional manual techniques.

Keywords: automation; engineering; green chemistry; organic chemistry; organic synthesis.