Aug 15, 2005 | Bay-Area-based research company Symyx Technologies is sharing lessons learned from its experience with lab automation by licensing its software or collaborating with other R&D companies.
“The ultimate goal is to take laboratory data that is high-throughput and manually entered and integrate the entire R&D information and knowledge into one searchable repository, regardless of where people are located,” says Isy Goldwasser, president of Symyx. He sees the company’s concept of managing research data as a way to accelerate the discovery cycle.
Symyx was founded in 1994 by Alejandro Zaffaroni, who founded Affymetrix, and Peter Schultz (see “The Code Breakers,” January 2004 Bio-IT World, page 26). During the past decade, Symyx has pioneered high-throughput research for material science, leading to the commercialization of three materials, in collaboration with other companies. Another dozen materials are in the pipeline.
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Testing numerous materials in parallel, Symyx had to build all the R&D workflow tools itself. “We could never afford building [software] that would be unique for every platform. Instead, we had to build a software package that would work with any platform,” explains Goldwasser.
When Symyx’s customers noticed that the software could do much more than simply run high-output research workflows, Goldwasser’s management team started a software business with a flagship suite of software called Renaissance. Collaborators have included Merck, Dow Chemical, ExxonMobil, and Pfizer.
“We first got involved with Symyx to develop a workflow for doing a high throughput on experimentation for identifying crystal forms for active pharmaceutical ingredients,” says David Mathre, executive director for Process Research at Merck Research Laboratories in Rahway, New Jersey.
Today, Merck labs use the Renaissance software suite on five different workflows including screening for crystal forms, catalysis, formulation, and solubility. Says Mathre: “We basically worked with [Symyx] and told them how we currently did the work and then, using their hardware and software expertise, we put together an automated workflow for doing the same type of experimentation — but doing it in a much broader and quicker manner.”
Automation has indeed streamlined the way Mathre’s labs perform the experiments. Last year, just four scientists working on 128 polymer crystal screening projects conducted more than 36,000 individual experiments to identify some 150 novel crystal forms of active pharmaceutical ingredients. “This work, doing it the old way, would’ve taken approximately ten times as many people or ten times as long,” Mathre says.
Symyx integrates its own instruments with those of other manufacturers in the same workflow controlled by one software package. Mathre has used several vendors’ automated systems in efforts to optimize methods over the years. “The problem,” he says, “is that each one of them has its own unique software, and it required additional training to move a scientist from one system to another system.” Today, scientists need only a couple of days to learn one type of software to create, run, and control the experiment and then collect and analyze the data.
What Merck and other drug companies are doing in automation of drug research, academic institutions such as North Dakota State University (NDSU) are doing with research in coatings and polymeric materials, making ship coatings to resist barnacle formation, for example.
Both Mathre and NDSU’s Philip Boudjouk have tested various laboratory information management systems and automation software over the years and say that their scientists’ most common questions today regarding the Renaissance suite is, “Couldn’t we also use it for this?”
However, Symyx’s Goldwasser recommends that new users of automation software test the workflow in a pilot first before scaling up. “Scientists resist in the beginning because they want to see that it works. Once they see it’s there, then there’s no resistance,” he says.
Merck is looking to expand the automation to areas such as drug metabolism, but Mathre sees a natural limitation when it comes to trials with animals. “When you get further down the line doing safety assessment where you’re working with individual animals, I don’t see it applicable at that point.”