In silico modeling enables greener analytical and preparative chromatographic methods

Abstract

Green analytical chemistry has garnered significant interest given the heightened focus for organizations to decrease their environmental footprint. Of the analytical methodologies, those involving separation science (chromatography, extractions, crystallization, etc.) tend to be the most detrimental to the environment as they require large volumes of solvent. Chromatographic methods can be made significantly greener while preserving their performance by changing the mobile phase and method conditions. However, this process is laborious and involves significant analyst time for experimentation and method refinement. Herein, we introduce in silico modeling and computer-assisted method development as a rapid, accurate, robust, and green technique to develop greener methods. For the first time, it is shown that the analytical method greenness score (AMGS) can be mapped across the entire separation landscape. This allows for methods to be developed based off their performance and greenness simultaneously. Given the increased scrutiny of fluorinated solvents, the utility of in silico modeling is demonstrated to move from a fluorinated mobile phase additive to an alternative chlorinated additive. Here, the AMGS is reduced from 9.46 to 4.49 while critical pairs go from fully overlapped to having a resolution of 1.40. Acetonitrile is also shown to be replaced in the mobile phase with environmentally friendlier methanol to reduce the AMGS from 7.79 to 5.09 while preserving the critical resolution. Finally, we demonstrate the use of a resolution map to capitalize on peak crossover and increase the loading of an active pharmaceutical ingredient by 2.5× in a preparative chromatography setting. This results in 2.5× less replicates needed during the purification. This study provides a framework that separation scientists can apply to green chromatographic separations using in silico modeling.