Imre Molnar

High performance liquid chromatography (HPLC) is a widely used and well-established technique, routinely employed by thousands of analytical scientists worldwide. Nonetheless, certain challenges—arising from the complex interplay of multiple factors affecting peak retention and separation—persist. In this context, multidimensional modeling approaches can provide valuable support.

Presenting a novel chromatographic modelling method to establish QbD-compliant comparative testing of eluent design spaces.

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.

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.

Food analysis is often handled less thoroughly than pharmaceutical analysis because of the smaller life-threatening risk expected from foodstuffs. However, food analysis is still a major focus for chromatographers from a scientific and an analytical point of view. Adoption of modern “in‑silico” techniques, such as chromatographic modelling, offer analysts new possibilities for method development.

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.

In this study, the quality-by-design principle is applied instead of trial-and-error in the development of a liquid chromatography (LC) method. A mixture of an active pharmaceutical ingredient and its 13 impurities was analyzed on a short narrow-bore column (50 mm ? 2.1 mm, packed with sub-2-?m particles) providing short analysis times. The performance of commercial modelling software for robustness testing was systematically compared to experimental measurements and design-of-experiment–based predictions.

In this study, the quality-by-design principle is applied instead of trial-and-error in the development of an LC method.

A new type of method development that uses modeling to find the "best" separation for high performance liquid chromatography (HPLC) was investigated and principles of Quality by Design (QbD) were followed when planning the investigation. The process delivered precise results and the method was able be transferred to a routine quality control (QC) laboratory.

A systematic way to develop HPLC methods consists of building up and running a useful set of experiments (DOE, design of experiments) to evaluate the influence of several factors or method conditions on the selectivity of a chromatogram. The crucial part here is the peak tracking between the chromatograms.

A look back at the life of this chromatography pioneer.