Beyond SOPs

A decade ago, the introduction of standard operating procedures was groundbreaking, but new understanding now helps bring the rigor of QbD to analytical method development.

By Paul Davies and Paul Kippax, Malvern Instruments

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Both the USP [2] and EP [3] recommend acceptance criteria for reproducibility testing. A Coefficient of Variability (COV) of less that 10% is suggested as acceptable for the median (Dv50) particle size or any similar value which is close to the center of the particle size distribution. This figure rises to 15% on values towards the edge of the distribution, such as Dv10 and Dv90, the particle size below which 10 and 90% of the population lies on the basis of volume. These limits are doubled for samples containing particles smaller than 10 microns because of the difficulties associated with dispersing such fine powders.

In our example then, where the acceptance criteria for the results are based on pharmacopoeial guidance, robust definition of the MODR requires that any source of variability does not take data reproducibility outside these limits. For example, the precision of air pressure control during dispersion is a function of the analyzer. If air pressure, a CQA, is controlled to within +/-0.1 bar, it is necessary to conduct experiments to determine the level of variability that this introduces in terms of the repeatability and reproducibility of the measured data. All potential sources of variability must be investigated in this way.

As with QbD, AQbD places the emphasis on fully understanding a process, rather than simply focusing on a set of conditions that work for certain sample types. The potential rewards of this approach have already been highlighted, but gaining the necessary understanding is inextricably linked with more extensive experimentation. Tools that can alleviate the burden associated with this research are therefore to be welcomed.









 Figure 6 shows a screen shot from the Mastersizer 3000 illustrating the Measurement Manager tool. This is a software feature that provides real-time feedback which indicates the impact of changing an analytical parameter. Parameters can either be modified by the user in real-time as part of a manually controlled measurement or they can be set within pre-defined measurement sequences within the software’s SOP-player tool. This tool provides the first step towards full automation of the method development process.

In addition to aiding with the process of method development, there is also a requirement to ensure that the data collected are reasonable and therefore reflect the capabilities of the analytical technique in terms of both resolving product changes and delivering reproducible results. Here, tools to assess the quality of the data are extremely valuable, helping to guide the user towards the definition of a good method.

Figure 7 shows the output of the Data Quality assessment tool provided in the Mastersizer 3000. Advice is given relating the measurement process (e.g. instrument cleanliness and alignment) and also the analysis process (e.g. the goodness of fit between the light scattering data acquired by the instrument and the optical model selected to calculate a size distribution from these data). This helps to address many of the method control issues highlighted in Figure 4. Software advances such as these can therefore make a big difference when it comes to the application of AQbD, and they substantially ease the analytical burden associated with its implementation.

A decade or so ago the introduction of standard operating procedures (SOPs) was groundbreaking, but analytical method development is now moving beyond simply defining a fixed set of measurement parameters. AQbD invites analysts to establish a robust MODR, a safe analytical working space. Working within the MODR ensures that results consistently meet defined quality criteria while at the same time providing the flexibility to respond to routinely encountered variability. The understanding that comes with scoping the MODR secures robust application of an analytical method across the product lifecycle and substantially eases method transfer.

As with QbD, AQbD holds out the attraction of greater understanding, but brings with it the burden of a broader research remit. However, advances in instrumentation, most especially in software, can make a major contribution when it comes to efficient scoping of the MODR. Features such as real-time feedback on the stability of a measurement and the impact of changes, for example, and the ability to automatically step through a series of SOPs, can help to substantially lighten the analytical burden, enabling analysts to reap the benefits of AQbD more easily.

[1] QbD Considerations for Analytical Methods – FDA Perspective. Presentation by SharmistaChatterjee at IFPAC Annual Meeting, Jan 25th 2013. Available for download 
[2] General Chapter <429>, “Light Diffraction Measurement Of Particle Size”, United States Pharmacopeia, Pharmacopoeial Forum (2005), 31, pp1235-1241

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