DOE:Layout 1 14/1/10 19:54 Page 48
Assays
researcher in making assays more robust to nui-
sance factors and in reducing the amount of vari-
ability in the output (Figure 14).
DOE is the most effective method to achieve prod-
uct and process knowledge and optimisation.
MODDE from MKS Umetrics
(www.umetrics.com) is a state-of-the-art design of
experiments software package that allows investi-
gators to achieve three important stages of DOE,
which are also critical requirements during assay
Figure 15: By using the Design Space Validation tool in MKS Umetrics MODDE, the
development. These stages are: identifying the
robustness is tested with a large number of random disturbances (Monte Carlo Simulation)
in the specified region, in this case the Experimental Region. In this example the optimised
most important factors and their ranges (screen-
experimental conditions found to improve the signal-to-background signal in a reporter gene ing), locating an optimal factor combination which
assay were investigated
can be used as a future set-point (optimisation) and
investigating the sensitivity of the set-point to
changes in the important factors (robustness test-
ing). The latest release, MODDE 9, introduces a
novel approach for Design Space Estimation (DSE)
and validation by taking a quantum leap towards
fulfilling the objective of the Quality by Design
(QbD) paradigm, which defines design space as
“the multidimensional combination and interac-
tion of input variables (eg material attributes) that
have been demonstrated to provide assurance of
quality”
5
. Based on Monte Carlo simulation, and
unique to MODDE 9, the DSE can be utilised in
robustness testing and validation during assay
development as it can provide estimates of the
largest possible design space and give quality or
probability estimates for a safe region of operabil-
ity for future results (Figure 15).
Developing useful assays through DOE relies on
two things. One is your understanding of some key
concepts such as experimental treatments, experi-
mental units, randomisation, replication and
Figure 16: JMP® from SAS prediction variance surface for three-factor custom design for
blocking. The other is the use of statistical technol-
assay optimisation ogy that allows you to quickly construct designs
that adequately express the biological context of
the assay and the nuances of its operational setting,
appropriate number of experiments and replicates and then to easily analyse the resulting data to rap-
to ensure that the maximum amount of informa- idly draw the best conclusions. JMP®, a desktop
tion is obtained from the results. Statistically, sig- product from SAS (www.jmp.com), gives scientists
nificant factors are identified through the ANOVA and technicians this capability, providing a single,
table or through graphs such as Pareto and normal unified environment that allows you to generate
plots. Minitab also generates graphs showing the and analyse efficient designs that are customised to
behaviour of the response, such as main effects and your specific situation rather than insisting you
interactions plots as well as cube, contour or three- pick an omnibus design from a pre-existing library.
dimensional response surface plots to assist in JMP’s computer-generated designs allow you to
interpreting results. Once the key factors are take specific account of constraints in your factors,
revealed, Minitab guides scientists through the include mixture and process factors in the same
optimisation tool to select the reagent combina- design, and correctly handle the hard and very
tions that will result in optimal critical quality hard to change factors needed when randomisation
attributes of the assay. Minitab can also assist the is restricted. JMP provides extensive diagnostics
48 Drug Discovery World Winter 2009/10
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