Optimal experimental designs in RPLC at variable solvent content and pH based on prediction error surfaces

Testing experimental designs in liquid chromatography (II): Influence of the design geometry on the prediction performance of retention models

In liquid chromatography, the reliability of predictions carried out with retention models depends critically on the quality of the training experimental design. The search of the best design is more complex when gradient runs are used instead of isocratic experiments. In Part I of this work (JCA 1624 (2020) 461180), a general methodology based on the error propagation theory was developed and validated for assessing the quality of training designs involving gradients. The treatment relates the mathematical properties of a retention model with the geometry of the training designs and their subsequent predictions. In that work, only five usual designs were considered. Part II investigates in detail the effects on predictions when the features of the training design (number and distribution of the experiments, initial and final modifier content, gradient slope(s), and location of gradient nodes and pulses) are varied systematically. Several groups of related designs containing one or more isocratic steps, linear or multi-linear gradients, or mixed isocratic/gradient runs, among others (in total 38 designs) were evaluated. Box and whiskers and triple plots of expected relative uncertainties were used to evidence the differences in prediction performance. The purpose was to give recommendations to construct designs with good prediction performance. The best designs sample (considering all runs) concentrations as diverse as possible, at any gradient time.

Quality-by-Design Is a Tool for Quality Assurance in the Assessment of Enantioseparation of a Model Active Pharmaceutical Ingredient

The design of experiments (DoE) is one of the quality-by-design tools valued in analytical method development, not only for cost reduction and time effectiveness, but also for enabling analytical method control and understanding via a systematic workflow, leading to analytical methods with built-in quality. This work aimed at using DoE to enhance method understanding for a developed UHPLC enantioseparation of terbutaline (TER), a model chiral drug, and to define quality assurance parameters associated with using chiral mobile phase additives (CMPA). Within a response surface methodology workflow, the effect of different factors on both chiral resolution and retention was screened and optimized using Plackett-Burman and central composite designs, respectively, followed by multivariate mathematical modeling. This study was able to delimit method robustness and elucidate enantiorecognition mechanisms involved in interactions of TER with the chiral modifiers. Among many CMPAs, successful TER enantioresolution was achieved using hydroxypropyl β-cyclodextrin (HP-β-CD) added to the mobile phase as 5.4 mM HP-β-CD in 52.25 mM ammonium acetate. Yet, limited method robustness was observed upon switching between the different tested CMPA, concluding that quality can only be assured with specific minimal pre-run conditioning time with the CMPA, namely 16-column volume (60 min at 0.1 mL/min). For enantiorecognition understanding, computational molecular modeling revealed hydrogen bonding as the main binding interaction, in addition to dipole-dipole inside the CD cavity for the R enantiomer, while the S enantiomer was less interactive.