An additional 12% had tried DOE, but are not currently using it today. Some 45% of survey respondents were using DOE in some aspects of AD today, with a further 23% planning to implement it in the future. Figure 1 shows the current level of use of DOE in AD among respondents to the HTStec survey. The survey showed that only around 5% of all assays developed today were done using DOE, although the expectation was that significant cost savings (3x) would be achieved by applying DOE. Current use of design of experiments in assay development Some of the findings of this market report (1) form the basis of this article and the setting to review the tools that currently support DOE in AD. With this in mind and as part HTStec’s tracking of emerging life science marketplaces, a survey was undertaken in June 2009.ġ) how widely DOE approaches in AD are usedĢ) what is the current level of understanding of DOEģ) what views people hold about DOE and its benefitsĦ) what restricts its wider implementation today. However, there is paucity of factual information around the application of DOE in AD, although many anecdotes prevail in the industry. One of the methods companies are increasingly exploring to compress AD times without compromising on quality is DOE.
Using the traditional approach up to around 10 different combinations of assay conditions (factors) may be explored using either manual liquid handling or a basic automated liquid handler set up. A typical AD lab may be expected to develop in excess of five assays per year, with around one in 10 assays never achieving the desired assay quality parameters/signal window (the main criteria of development success).
Typically there are levels of design which can be applied: these range from the simplest fractional factorial (which includes experiments to identify which factors are most critical), followed by full factorial (which enables identification of significant interactions between factors), and the more complex surface area design (which facilitates finer optimisation of factors).Īssay development (AD) has become a bottleneck in many pharmaceutical organisation’s lead discovery operations, with assays typically taking in excess of a month or more to develop using a traditional approach (ie changing one setting at a time or sequential design). Until new tools or more encompassing solutions emerge, the full impact of DOE on AD is unlikely to be realised.ĭesign of experiments (DOE) is a well established and proven statistical method which has broad application across many disciplines and industries. A market opportunity exists for a turnkey solution that directly links statistical design with automated liquid handler programming and also feeds the assay readout directly into the statistical analysis, to suggest and facilitate further iterative retesting. DOE needs to be simpler to implement to make a major impact on AD.
Only one liquid handling vendor currently offers application specific software and support for investigating DOE in biological assays.Īlthough standalone DOE software packages are available, these were not written specifically for biological applications and they vary in their suitability for AD. The use of design of experiments (DOE) in assay development (AD) has the potential to speed up assay optimisation (ie reduce assay development bottlenecks) and to facilitate a more thorough evaluation of assay variables.