Achiral-Chiral Two-Dimensional Liquid Chromatography Platform to Support Automated High-Throughput Experimentation in the Field of Drug Development

Anal Chem. 2020 Nov 17;92(22):15187-15193. doi: 10.1021/acs.analchem.0c03754. Epub 2020 Nov 3.

Abstract

Automated high-throughput experimentation (HTE) is a powerful tool for scientists to explore and optimize chemical transformations by simultaneously screening yield, stereoselectivity, and impurity profiles. To analyze the HTE samples, high-throughput analysis (HTA) platforms must be fast, accurate, generic, and specific at the same time. A large amount of high-quality data is critical for the success of machine learning models in the era of big data. Conventional chiral liquid chromatography-mass spectrometry (LC/MS) HTE methods are hampered by compound co-eluting, possible ion suppression, and limited chiral column lifetime in the presence of crude reaction mixtures or complex sample matrices. To overcome these limitations, a generic and fast achiral-chiral heart-cutting two-dimensional (2D)-LC method has been developed to determine both the yield and stereoselectivity of chemical transformations within a 10 min run time. Successful implementation of the 2D-LC HTA platform in a routine drug development environment was achieved for real-world project support, with the analysis so far of over 2000 reaction mixtures prepared in the 96-well plate format. Excellent performance of the method was demonstrated by relative standard deviation (RSD) lower than 0.83% for the 1D and 2D retention times, and determination coefficients higher than 0.99. The presented HTA 2D-LC platform has had a significant impact on drug development by analyzing the HTE samples rapidly with unambiguous peak tracking and providing a robust approach for accurately generating a large amount of high-quality data in a short time.

MeSH terms

  • Chromatography, Liquid / methods*
  • Drug Development / methods*
  • High-Throughput Screening Assays / methods*
  • Machine Learning
  • Stereoisomerism
  • Time Factors