Predictions of Chromatography Methods by Chemical Structure Similarity to Accelerate High-Throughput Medicinal Chemistry

Abstract

We introduce a new workflow that relies heavily on chemical quantitative structure-retention relationship (QSRR) models to accelerate method development for micro/mini-scale high-throughput purification (HTP). This provides faster access to new active pharmaceutical ingredients (APIs) through high-throughput experimentation (HTE). By comparing fingerprint structural similarity (e.g., Tanimoto index) with small training data sets containing a few hundred diverse small molecule antagonists of a lipid metabolizing enzyme, we can predict retention time (RT) of new compounds. Machine learning (ML) helps to identify optimal separation conditions for purification without performing the traditional crude QC step involving ultrahigh performance liquid chromatography (UHPLC) analyses of each compound. This green-chemistry approach with the use of predictive tools reduces cost and significantly shortens the design-make-test (DMT) cycle of new drugs by way of HTE.

Scheme 1. Aryl Amide Coupling Library

Figure 1

Method prediction and micro/mini-scale purification workflow that delivers final compound arrays for assay.

Figure 2

Similarity data for the QSRR training set. (A) Histogram showcasing the distribution of pairwise structural similarities expressed as the Tanimoto index. (B) Heat map visualization for all 168 compounds of the training set.

Figure 3

Calculated and experimental UHPLC RT correlation of the QSRR test set.