High-throughput screening: designer screens

technology feature Father and son 106 For some, total adoption 107 High-throughput screening: designer screens Nathan Blow In his 1935 book, The Design of Experiments1, British mathematician Ronald A. Fisher developed a mathematical framework for designing experiments. Fisher explored how experiments could be most efficiently set up to survey the interactions among different experimental factors in an effort to identify optimal combinations. His approach, along with several different mathematical underpinnings that have evolved since, has come to be known as design of experiments (DoE). Since Fisher’s book 1 was published, chemists, engineers and social scientists have relied on DoE approaches and software packages for setting up research models and complex experimental designs in fields ranging from clinical trials to petrochemical manufacturing. But surprisingly, in biology, where today’s high-throughput screens often use multiple conditions, variables and reagents, the story has been quite different. “I am not sure why more people are not using DoE in biology,” says Seth Cohen, director of Microfluidic Applications at Caliper Life Sciences in Hopkinton, Massachusetts, USA. At Caliper, Cohen and his team swear by the use of DoE format when it comes to finding optimal biochemical assay conditions, using it 100% of the time now to design reagents and optimize enzyme performance. They have developed an integrated suite of tools for approaching DoE methodology including a commercially available DoE software package they adapted for their enzyme assay development efforts, Caliper’s Sciclone ALH3000 automated liquid handling platform to set up the DoE software-generated experiment and the LabChip EZ Reader to analyze the results. Novartis © 2009 Nature America, Inc. All rights reserved. Some researchers say an eighty-year-old statistical method can make setting up and analyzing highthroughput screens and large-scale experiments faster and more efficient. So why are more biologists not flocking to use this tool? Adam Hill, who has been using DoE in his research for more than a decade, would like to see wider adoption of DoE in biology. After the results are generated, the software analysis package can identify important experimental interactions between assay components as well as provide the optimal conditions for the assay. Using this pipeline, Cohen and his assay development group can explore up to 600,000 different combinations of conditions in a single DoE experiment: 192 different conditions such as salt concentration and pH, for example, with 250 unique combinations. He says that the amount of information that can be extracted from a single experiment can often hook researchers: “Once scientists use DoE, they don’t turn back.” Waiting for the tipping point But getting to the point of using DoE methodology is the issue. And in biology, the implementation of DoE is often met with resistance. “At the onset, lead balloons have more luck,” says Adam Hill, director of the Hits Discovery group at the Novartis Institutes for Biomedical Research in Cambridge, Massachusetts, USA, who has been using DoE in his work for the past ten years. It was Hill who championed the DoE approach in assay development for highthroughput screening at Novartis when he first joined the company in 2004. “They were basically not doing it when I arrived,” he says. But Hill, whose group develops cell-based and biochemical screening assays using technologies that include liquidhandling robots and microplate scanners, wanted to speed up the screening process, and he knew from previous work that DoE was a powerful technique that could enable better use of the available equipment for rapid assay development. The quantitative nature of most biochemical assays, like the ones Hill develops at Novartis, are particularly well suited to DoE; in fact, many researchers say a quantitative output is almost a prerequisite to setting up an effective experiment using DoE. Hill says that many cell-based assays are not particularly amenable to a fractional factorial DoE approach, as researchers like to consider every condition and the controls tend to be specific to each condition. When it comes to using DoE, having an optimal number or value to attain makes building models and testing for experimental interactions within a dataset easier. But Hill and others suspect that the quantitative nature of DoE, along with its statistical underpinnings, could be one of the reasons for its limited use in biology. “For nature methods | VOL.6 NO.1 | JANUARY 2009 | 105 technology feature Stephen Chambers says protein expression studies can often be done faster using DoE approaches. but then in the second pass you can use [those] data to train a system to stay within an optimal range,” says Chris McCready, director of Global Process Analytical Technology at Umetrics. Currently Umetrics offers MODDE 8.0 for DoE and SIMCA-P for data analysis. McCready says a researcher inputs the number of experimental variables and what type of model they expect, either a response surface or screening design similar to the Stat-Ease packages, and the MODDE 8.0 program will tell the researcher what types of runs to make, whereas SIMCA-P can perform data analysis in cases when many variables are present. Although DoE software packages are proving effective for setting up experiments, the other issue novice researchers tend to encounter when first working with DoE is integration. “If someone came out with a robust software package that allowed you to design an experiment, feed that design to an automated liquid-handling instrument and analyze the results from your assay, that would be a good start towards more widespread use,” says Hill. The ability to integrate DoE software with automation could be one reason that up to this point it has really been pharmaceutical and biotechnology companies who adopted DoE and applied it to biology. “You do not have to use automation, I started off doing it by hand, but in most cases people would like to use it,” says Hill. But with more and more liquid-handling platforms being developed at lower price 106 | VOL.6 NO.1 | JANUARY 2009 | nature methods 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 Protein yield (mg l–1) biologists it can be intimidating; biologists and math usually don’t mix,” says Stephen Chambers, vice president of the Cambridgebased Abpro, a protein reagent company that has adopted DoE approaches. Still, Chambers notes that there are now many good software options available for those interested in applying DoE in setting up their high-throughput screens or assayoptimization problems. For designing experiments, several commercial packages exist for researchers to try. SAS, a software development company in Cary, North Carolina, USA, now offers JMP 8, a statistical package with programs that allow users to design their experiments with several different variables as well as offering a diagnostic (...truncated)