Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy

Abstract

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.

Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT)

“…However, there was a clear deviation from linearity, especially in the lower organic modifier volume ( Baeza-Baeza and García-Alvarez-Coque, 2020 ). Furthermore, the QSRR model displayed excellent prediction capacity for ionizable compounds but was limited to the type and calculation method of the molecular descriptors ( Kumari et al, 2023 ). In addition, previous studies have reported the combined influence of temperature and mobile phase composition on chromatographic retention ( Arkell et al, 2018 ; Caltabiano et al, 2018 ), and the simultaneous effect of pH and temperature or mobile phase composition has been less reported.…”

Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity

“…Quantitative structure retention relationships (QSRRs) have been used for help in the prediction of retention parameters to reduce method development times [1][2][3]. These models establish a relationship between a chromatographic retention parameter and a set of physiochemically relevant molecular descriptors.…”