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

Abstract

When pH is used as factor in reversed-phase liquid chromatographic (RPLC) separations, the need for providing quality and informative data with the minimal experimental effort becomes imperative. The most rational way to achieve this is by means of experimental designs. The interest in finding optimal designs involving solvent content and pH in RPLC is considerable, since these factors allow large variations in selectivity when ionisable compounds are involved. Unfortunately, the equations that describe the retention of these compounds with pH are nonlinear. As a consequence, factorial and other designs based on geometrical considerations are not well suited, whereas D-optimal and related designs can only be applied in an iterative fashion. In this work, an extension of G-optimal designs, aimed to enhance the quality of the predictions, is examined for problems involving solvent content and pH. The study was carried out with a set of probe ionisable compounds, for which information on retention behaviour was accurately known. A stepwise strategy was used to obtain a rapid estimation of the best design with a given number of experiments. The objective of the study was to investigate the distribution and number of points in the ideal design for compounds of different acid-base behaviour, and the possibility of finding common designs for groups of compounds. A further goal was to derive design construction rules containing the information requirements, without needing any further mathematical treatment.

Experimental design for the optimization and robustness testing of a liquid chromatography tandem mass spectrometry method for the trace analysis of the potentially genotoxic 1,3‐diisopropylurea

“…Such a task can be achieved only by a fundamental understanding of the given analytical system. Hence, in order to address the requirements of highly organized method development, design of experiments, [24][25][26][27] quality by design, [28,29] and design space computer modeling [30][31][32] have gained increased attention in the field of analytical chemistry in recent years. Conventionally the trial-and-error method development approach is followed during impurity analysis of pharmaceuticals.…”

“…The quality by design methodologies for genotoxic impurity control has also been recently reviewed . Besides method development, design of experiments finds application in a wide range of analytical tasks – such as minimization of instrumental noise, headspace sampling, content uniformity tests, pH optimization, capillary electrophoresis, and SPE – with an increasing number of publications. For further reading on the general aspects of DoE the authors recommend the book Design of Experiments ‐ Principles and Applications edited by Eriksson…”

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

“…The mathematical model and the graphical response surface allow superior, global, and accurate understanding of the simultaneous effects of variable factors on the measured response, minimizes costs and time spent during method development, and allows visualization of the design factors on the measured response, minimizes costs and time spent during method development, and allows visualization of the design space or response surface [7][8][9]. DoE is applied for the optimization of a wide range of chromatographic methods, including chiral separations [9][10][11][12][13][14]. Instead of using expensive, widely available chiral stationary phases (CSP) [14][15][16][17][18], limited budget laboratories of small industrial pharmaceutical enterprises use chiral mobile phase additives (CMPA) to achieve enantioresolution on commonly available achiral stationary phases in various modes (normal phase and reversed phase) [19][20][21][22][23].…”

Optimization of hydrophilic interaction LC by univariate and multivariate methods and its combination with salting‐out liquid–liquid extraction for the determination of antihypertensive drugs in the environmental waters

“…On the other hand, multivariate optimization approach based on the experimental design is generally supposed to evaluate the interactions between variables and alleviate work labor. This approach has been successfully applied to optimize chromatographic separation, such as RP‐HPLC, normal phase HPLC, and CE . However, the experimental design approach was relatively rarely used to optimize the HILIC separation .…”