A Quality-by-Design Methodology for Rapid LC Method Development, Part II - - Chromatography Online
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A Quality-by-Design Methodology for Rapid LC Method Development, Part II
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Michael E. Swartz
Part I of this article described the application of Quality-by-Design (QbD) principles to the task of screening analytical columns. In a QbD approach a statistical experiment design plan (Design of Experiments, or DOE) [1] is used to systematically vary multiple study factors in combination through a series of experiment trials that, taken together, can comprehensively explore a multi-factor design space. Such a design can provide a data set from which the experimenter can identify and quantify interaction effects of the factors along with their linear additive effects, curvilinear effects, and even non-linear effects. This quantitation translates the design space into a knowledge space.


Ira S. Krull
As also described in Part I, in a traditional method development approach one would first study "easy to adjust" instrument parameters, meaning those for which no equipment configuration change was required. However, categorizing instrument parameters as easy versus hard to control is no longer valid in many cases due to the availability of multi-column and multi-solvent switching capabilities for most modern LC instrument systems. Therefore, one can now address up front the instrument parameters which are understood or expected to have the strongest effects on separation of the sample compounds. This is consistent with a QbD approach in which a Pareto analysis would first be carried out to rank instrument parameters in terms of their expected ability to affect compound separation; a manageable number of the top-ranked parameters are then included in the first phase of method development. Table I presents a phased method development approach using a rank-based partitioning of instrument parameters consistent with QbD-based practice.


Table I: Current phasing of method development workflow
An experimenter defines the design space for a given method development study by selecting instrument parameters (study factors) and defining the range or level settings of each. A DOE-based experiment then defines the specific combinations of the study factors which together provide a statistically valid sampling of the design space. Figure 1 illustrates a DOE-based sampling plan for a two-factor design space. The black dots in the figure correspond to the trials defined by a classical two-level factorial design. Such a design is typically used in initial variable screening studies to explore both single factor (linear additive) effects and two-factor interaction effects. The red dot in the figure is the center point run. A best practices approach includes this run to estimate the presence of "curvature" in the response space, that is, effects which depart from linearity. The center point is usually replicated within the design — the results from the center-point repeats provide an internal estimate of the experimental error associated with each measured result.


Figure 1
The gray dots in the figure correspond to the additional points which would be present in a classical three-level factorial design, also referred to as a response surface design. When screening design results indicate that curvature effects are important, the response surface points can be added to the design to identify the specific factor or factors which are expressing the curvature effects and quantify these effects.

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