This installment of Validation Viewpoint describes how statistically rigorous quality-by-design (QbD) principles can be put
into practice to accelerate each phase of liquid chromatography (LC) instrument method development. Here, in Part I, the authors
examine the current approaches to column screening in terms of design space coverage — a key element of process knowledge.
The second part presents novel data treatments to both accelerate and bring quantitation to the column-screening effort. The
third installment will focus on integrating quantitative method robustness estimation into formal method development. Moving
robustness estimation upstream into the method development effort is consistent with both FDA and ICH guidances. It also enables
the identification of instrument methods that simultaneously meet both mean performance and performance robustness requirements.
Ira S. Krull
Development of analytical methods for LC instrument systems typically is carried out in two phases. The first phase involves
column screening, sometimes referred to as column scouting. Column screening is the experimental work done to identify the
analytical column (stationary phase) with the best selectivity in terms of all compounds in the sample that must be resolved
adequately. Formal method development, the second phase, involves experimenting with additional instrument parameters believed
to affect compound separation strongly. The overall goal of the two phases is identification of the instrument parameter settings
that provide optimum chromatographic performance.
This three-part column describes how statistically rigorous QbD principles can be put into practice to accelerate each phase
of LC instrument method development. The column is presented in three parts. In this first part, we address column screening.
We examine current method development approaches in terms of design space coverage — a key element of process knowledge. In
Part II, we will present QbD data treatments to both accelerate and bring quantitation to the column-screening effort. The
final installment will focus on integrating quantitative method robustness estimation into formal method development.
Traditional LC Method Development Practice
Reversed-phase LC is by far the most widely used LC separation method in the pharmaceutical and biotechnology industries.
Reversed-phase LC is therefore the basis of the discussions and examples used in this paper. However, the reader will recognize
that the instrumentation, software, and QbD-based methodologies presented here are applicable to other LC approaches such
as normal-phase LC and hydrophilic interaction liquid chromatography (HILIC).
The traditional approach to LC method development is to systematically vary one factor across some experimental range while
the level settings of all other controllable factors are held constant. This "one-factor-at-a-time" (OFAT) approach, still
widely practiced today, is carried out by selecting one instrument parameter to study while holding all other parameters fixed.
The "best" performing level of the study parameter is identified normally by visual inspection of the trial chromatograms;
the parameter is then fixed at this level, and a new instrument parameter is selected for the next iteration. The OFAT process
is repeated parameter by parameter until an adequately performing instrument method is obtained.
Table I: Historical phasing of method development workflow
The OFAT approach is carried out routinely in two informal phases, with a specific set of instrument parameters relegated
to each phase. The instrument parameters associated with the first phase are those that were historically "easy to adjust,"
meaning that new levels of the parameters could be set directly in the instrument method without the need to physically change
the instrument configuration. The instrument parameters associated with the second informal phase were those for which changing
a level normally required changing the instrument configuration; for example, switching a solvent reservoir or switching to
a different analytical column. Table I lists the instrument parameters commonly utilized in the two informal OFAT phases of
traditional method development.
I am quite disappointed by the misinterpretation of the concept of "design space" despite the given definition coming from FDA (and ICH) guidelines. Figure 2 does not depicts the design space but the experimental domain investigated to find optimal operating conditions. As defined by FDA and ICH guidelines, the design space "provide assurance of quality", which means that the selected criteria (e.g. resolution, analysis time...) are met (e.g. Rs>1.5, analysis time<15min.) with a given probability (level of quality). Equations 11 and 12 in P. Lebrun et al, Chemom. intell. lab. syst., 91 (2008) 4–16, give an example of mathematical interpretation of design space applied to HPLC. In other words, design space is a subregion of experimental domain within which 'objectives are met', its why the above mentioned guidelines conclude that "Working within the design space is not considered as a change".