Evolution from univariate to multivariate optimization methods in liquid chromatography

Global retention models and their application to the prediction of chromatographic fingerprints

The resolution of samples containing unknown compounds of different nature, or without standards available, as is the case of chromatographic fingerprints, is still a challenge. Possibly, the most problematic aspect that prevents systematic method development is finding models that describe without bias the retention behaviour of the compounds in the samples. In this work, the use of global models (able to describe the whole sample) is proposed as an alternative to the use of individual models for each solute. Global models contain parameters that are specific for each solute, while other parameters ‒related to the column and solvent‒ are common for all solutes. A special regression procedure is presented for the construction of global models, which are applied to predict highly complex chromatograms, such as chromatographic fingerprints, for diverse experimental conditions in isocratic and gradient elution. Another interesting application is the prediction of molecular properties, such as log P_o/w, from the specific solute parameters of the global models. The examined adapted models are based on the equations proposed by Snyder, Schoenmakers, Neue and Kuss, Jandera, and Bosch Rosés to describe the retention. In all cases, the predictive capability was very satisfactory. Two cases of study were considered: chromatograms of camomile extracts analysed using acetonitrile gradients, and a set of 145 known compounds in a wide range of structures and functionalities, eluted isocratically with acetonitrile/water mobile phases.

A strategy to develop fast RP-HPLC methods using monolithic silica columns

Since the appearance of monolithic silica, much work has been done describing the properties of monolithic silica columns. Meanwhile the transferability of analytical methods from conventional to monolithic silica columns has been intensively investigated [1-5]. RP HPLC method development strategies for conventional columns should be updated or scaled to meet the higher performing monolithic column technology. Because of the high permeability of monolithic silica columns it should be possible to decrease the time for method development by applying high isocratic flow rates. Here we suggest a clear strategy for method development using monolithic columns. The strategy will be applicable for various sample compositions, e. g., acidic, basic, or neutral. The applicability of monolithic columns for especially complex separations of basic mixtures without the need of using a highly basic mobile phase that harms the column will be pointed out in this work. This work will describe in detail the actual method development process. For better understanding of our strategy, the influence of flow rate, column length, mobile phase composition, pH, and temperature will be discussed. Details about the application of a flow program will be mentioned.

Models and objective functions for the optimisation of selectivity in reversed-phase liquid chromatography

Interpretive methodologies are the most efficient tools for finding the optimal conditions in chromatography. These methodologies are supported by models or algorithms able to infer the system behaviour upon changes in the experimental factors. Once the models are built with data obtained from sets of carefully designed experiments, molecular modelling or other approaches, they can be applied to predict the performance of new conditions. The different elements involved in these methodologies, for both isocratic and gradient elution, are given. Special attention is devoted to the description of retention, owing to its major impact on the prediction of chromatographic resolution. Several models considering the main factors affecting retention (i.e. organic modifiers, pH and temperature), and procedures that enhance the predictions, are presented. Both the existence of skewed peaks and the effect of elution conditions on peak profiles are considered. Finally, the assessment of resolution, as well as other secondary aims that affect the practical suitability of the optimal conditions, is discussed.

Fast HPLC method for the determination of glimepiride, glibenclamide, and related substances using monolithic column and flow program

This work presents a fast method for the simultaneous separation and determination of glimepiride, glibenclamide, and two related substances by RP LC. The separation was performed on a Chromolith Performance (RP-18e, 100 mm x 4.6 mm) column. As mobile phase, a mixture of phosphate buffer pH 3, 7.4 mM, and ACN (55:45 v/v) was used. Column oven temperature was set to 30 degrees C. The total chromatographic run time was 80 s. This was achieved using a flow program from 5 to 9.9 mL/min. Precisions of the interday and the intraday assay for both retention times and peak areas for the four analyzed compounds were less than 1.2%. The method showed good linearity and recovery. The short analysis time makes the method very valuable for quality control and stability testing of drugs and their pharmaceutical preparations.

Business-objective-directed, constraint-based multivariate optimization of high-performance liquid chromatography operational parameters

The goal of a separation can be defined in terms of business needs. One goal often used is to provide the required separation in minimum time, but many other goals are also possible. These include maximizing resolution within an analysis-time limit, or minimizing the overall cost. The remaining requirements of the separation can be applied as constraints in the optimization of the goal. We will present a flexible, business-objective-based approach for optimizing the operational parameters of high performance liquid chromatography (HPLC) methods. After selecting the stationary phase and the mobile-phase components, several isocratic experiments are required to build a retention model. Multivariate optimization is performed, within the model, to find the best combination of the parameters being varied so that the result satisfies the goal to the fullest extent possible within the constraints. Interdependencies of parameters can be revealed by plotting the loci of optimal variable values or the function being optimized against a constraint. We demonstrate the concepts with a model separation originally requiring a 54 min analysis time. Multivariate optimization reduces the predicted analysis time to as short as 8 min, depending on the goals and constraints specified.