Utilizing Design of Experiments (DOE) for Characterizing Assay Robustness during Drug Development

Kyra J. Cowan, Ph.D., Scientist, Genentech, Inc, and presenter at next week's Development, Validation and Maintenance of Biological Assays recently sat down with us to preview her presentation.  For more information on the event, download the agenda.  If you'd like to join us next week in Seattle, as a reader of this blog when you register to join us and mention code IBA13JP, you'll save 20% off the standard rate.

Your abstract states that you identified factors which impacted assay performance utilizing a screening DOE design. Once you have identified these factors do you then utilize the response surface mapping DOE designs to determine appropriate ranges?

We used a 24-run Plackett-Burman screening design to economically model all main effects and all ten 2-way interactions that may impact the response, and these factors and their corresponding levels that were included in this robustness DOE were selected based on prior knowledge of the assay and the acceptance criteria for the assay (Cowan KJ et al., Bioanalysis 4(17), 2127-39 (2012)). A response surface mapping DOE to determine appropriate ranges could have been used, in particular to further define the optimal coat concentration (for example) for this study, however we chose not to run this because we clearly saw the impact of having an unstable ligand, which was the source of the lack of robustness for this assay, and that only by handling the ligand under specific conditions could we rely on the method.

Assay robustness is a common analytical development step to utilize DOE approaches. Do you use DOE for any other development steps? If so which ones?

We can and have in some instances employed DOE to initiate assessment of reagent conditions and assay parameters at the beginning of the development stages of an assay (eg. for assay optimization), whether it’s a PK or an ATA assay, to efficiently assess the levels of the critical factors (such as coat concentration, minimum dilution of matrix, and detection reagent concentration). Some have found this to be a useful step in accelerating assay development, to assess assay robustness, or in new reagent validation.

 Many of our conference attendees are developing cell-based bioassays. Can you envision using the same DOE approach for a cell-based assay instead of a Ligand Binding Assay and what would be the additional challenges?

 As with all DOEs, the critical step is to determine which factors (and their levels) need to be assessed, since this design was meant to be a fit-for-purpose DOE and the factors will define the effectiveness of the DOE. The factors selected to be analyzed, and in turn the output or response of the DOE, should reflect the experience of the assay developer during development. This particular Plackett-Burman design used in the presentation was originally designed so that it could also incorporate not only coating conditions and substrate development time, but different lots of matrix and detection lots and their concentrations. For a cell-based assay robustness DOE, additional or alternative factors that could be included in the design that may change in small increments over time could be cell seeding density, cell harvest density, cell age, and both assay and detector (eg. Alamar Blue) incubation times, and the 2 levels for each of these factors could be the predicted or potential small variations on the final conditions selected for that cell-based assay. A recent paper by Xinyi C. Chen et al (2012) implemented DOE in the development and validation of a cell-based assay and describes a fractional factorial design to address some of these issues (Chen XC et al., J. Immnunol. Met. 376, 32-45 (2012)).

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