Chromatography Online


Imre Molnár | Authors


Employing Mechanistic Model’s Flexibility to Create Robust UHPLC Method Design: Case Study on Industrial Cannabis sativa Sample

With the era of new complex drug products there is an increasing demand on multivariate modelling tools to integrate robust analytical method design, facilitate efficient and science-based change management, and improve communication between regulators and industry. Mechanistic models provide several advantages in terms of modelling efficiency, versatility, and high predictive power. The following case study will demonstrate a robust method design for industrial Cannabis sativa.

Robustness Modelling in Ultrahigh-Pressure Liquid Chromatography Methods

Many workers in pharmaceutical laboratories are unable to change any aspect of their methods, although they often encounter severe problems and create many out-of-specification (OoS) results. They are particularly afraid to investigate these problems from a chromatographic perspective in case they generate new unforeseen problems. In the literature, however, there are numerous examples showing that it is worthwhile trying to understand the reasons for “unexplainable” behaviour in ultrahigh-pressure liquid chromatography (UHPLC) using modelling. By using modelling, problems can be recognized and often eliminated with legal operations according to the allowed tolerance limits mentioned in pharmacopoeia descriptions. The following article aims to show that “visual chromatographic modelling” can be a useful aid.

Equivalent Column Selection in HPLC

High performance liquid chromatography (HPLC) methods are used today to control the quality of many chemical and pharmaceutical products. The methods are usually developed by optimizing the properties of the mobile phase with a specific column. However, if the original column is no longer in production, the result will often change when a different column is used. In this case, we were interested in finding one or two equivalent columns that can replace the original column without changes in selectivity and robustness. This study demonstrates a new way to compare columns and select suitable replacement columns. The presented method will also allow the evaluation of different columns with the same method, as well as the evaluation of the robustness of the common method with different columns.