Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis.

Roman Szucs,Roland Brown,Claudio Brunelli,Jasna Hradski,James C Heaton

doi:10.3390/ijms22083848

Abstract

Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.

Highlights

Pharmaceutical analysis is an important area of chemical analysis used to support diverse and excessively complex activities associated with drug development
Chromatographic Quantitative Structure Retention Relationship (QSRR) models were demonstrated to be useful for the prediction of retention times for hypothetical components with favourable accuracy
The optimum resolution space was shown to be accurately represented when calculated using this approach. This was achieved by using a combination of Dragon, MOE and VolSurf+3D descriptors with a Support Vector Machine regression algorithm which outperformed all other tested conditions

Summary

Introduction

Pharmaceutical analysis is an important area of chemical analysis used to support diverse and excessively complex activities associated with drug development. RP-LC is commonly used to assess the assay/purity of starting materials, isolated synthetic intermediates and Active Pharmaceutical Ingredients (APIs). This usually requires baseline separation of all known components of complex mixtures, their identification and subsequent quantitation. Chemists are required to understand the impact of synthetic parameters on the quality of their processes which make important starting materials, intermediates and final API. This is an essential requirement of commercial synthetic route development. The understanding of degradation requires chromatographic separation of key degradation products from the main component and their subsequent identification and quantitation [4,5,6]

Methods

Results

Conclusion