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Part III: A Quality-by-Design Methodology for Rapid LC Method Development

    Part III:  A Quality-by-Design Methodology for Rapid LC Method Development

    Quality-by-design (QbD) is a methodology gaining widespread acceptance in the pharmaceutical industry. A core tenet of this methodology is the idea of establishing the design space of a product or process as a primary R&D goal. Many articles have been published recently describing the successful application of QbD to process development. By recognizing a liquid chromatography (LC) instrument as a small process-in-a-box, one can readily see the applicability of QbD to LC method development.

    CH Q8 defines a design space as “The multidimensional combination and interaction of input variables (for example, material attributes) and process parameters that have been demonstrated to provide assurance of quality”(1). Two key elements of this definition warrant brief discussion. First, the phrase “multidimensional combination and interaction” clearly indicates that the “design space” should be characterized by studying input variables and process parameters in combination, and not by a univariate (one-factor-at-a-time) approach. Second, the term “design space” is one of many terms used in the Design of Experiments (DOE) lexicon to denote the geometric space, or region, which can be sampled statistically by a formal experimental design.

    Other terms in common use include design region, factor space, and “joint factor space.”(2). However, the phrase “demonstrated to provide assurance of quality” clearly defines this design space as a subset region of an experimentally explored design region in which performance is acceptable. Therefore, in this article, the term “experimental design region” refers to the geometric region described by the ranges of LC parameters studied in combination by a formal experimental design. When the experimental results are of reasonable quality, DOE can translate the experimental design region into a “knowledge space” within which all important instrument parameters are identified, and their effects on method performance are fully characterized. As DOE is fundamentally a model-building exercise, this translation is accomplished by deriving equations (models) from the experimental results. Given that the equations have sufficient accuracy and precision, they then can be used to directly establish the ICH-defined design space. The instrument parameter settings in the final LC method, thus, represent a point within the design space. The design space itself represents a region surrounding the final method bounded by edges of failure; parameter setting combinations inside the bounds have acceptable method performance, parameter setting combinations outside the bounds do not.

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