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Pfizer Digs Deeper in Mining Trial Data

By Salvatore Salamone

March 14, 2006 | Pfizer is stepping up its efforts to get more information from existing clinical trial data. The company is turning to sophisticated data-mining techniques to help improve the design of new trials, to better understand possible new uses for existing drugs, and to help examine how drugs are being used after they have been approved.

“We want to milk as much out of the data as possible,” says Mani Lakshminarayanan, director/statistical scientist at Pfizer.

While mining data from past clinical trials makes great sense, companies rarely take another look at such data. Typically, pharmaceutical companies working on an FDA submission do a set of trials as part of new drug application and integrate a summary of the drug’s efficacy and safety into that submission. After that, all other information from clinical trials is simply saved but not examined again unless regulatory agencies request additional analyses or warranted by internal marketing requirements.

“Today, once a company submits a new drug application to the FDA, all the data [from clinical trials] sits collecting dust whether or not the drug is approved,” says Michael O’Connell, director of life science solutions at Insightful Corp.

Deviating from this routine, Pfizer is doing additional exploratory analysis of clinical trial data. “We’re using data-mining techniques to look for specific or unknown patterns,” says Lakshminarayanan.

One way the information gleaned from secondary analysis is being used is to help design new studies. “There is a huge amount of clinical trial data available,” says Lakshminarayanan. “We’re going through that data (after a submission) and mining the data to better design new studies.”

To that end, the information obtained from data-mining completed studies might be used to find a sample size or population when designing a new trial. For instance, if a company wants to bring a drug approved in the United States to Japan, the company would have to do a bridging study to show that the drug works within the Japanese population.

“From existing data, you can look at the statistics from old trial data and use this information to design a new study,” says Lakshminarayanan.

In a similar vein, a company might reexamine clinical trial data once a drug is, say, halfway through its patent life. “A company might look to see if there are other uses for an already approved drug or to explore subgroups within the trial population,” says O’Connell.

Or a company might simply look, in more detail, for ways to minimize risks associated with a drug. For instance, a company could use data-mining techniques to look across many studies for drug interaction or safety issues that impact a particular population (e.g., all people with brown hair and blue eyes).

One factor helping Pfizer with its data-mining effort is the advent of newer analysis tools. While the work Lakshminarayanan is doing can be done using many standard statistical analysis applications, new tools (in this case, the data-mining and analysis workbench Insightful Miner) are helping Lakshminarayanan and his group work closely with other researchers.

In the past, statisticians might use analysis tools that required lots of programming skills. Newer tools such as the Insightful Miner give statisticians and researchers the ability to apply a wide variety of analysis techniques to a data set without having to be an expert at writing command-line programming code. With a tool like Insightful Miner, icons representing analysis steps can be dropped and dragged onto a workflow pallet. And as this is being done, the software handles much of the underlying programming, off-loading some of these tasks from the user.

Techniques such as this are something industry experts are touting as a key to future analysis work. “Insightful Miner offers a solution for mining large data sets [that includes an] easy-to-use visual workmap interface that makes it simple to deploy analytic expertise to less technical decision-makers,” says Michael Valenti, industry analyst at Frost & Sullivan. “Its pipeline architecture allows users to mine large data sets easily, extracting intelligence quickly, for better, faster decision-making.”

Another benefit of this visual approach is that Pfizer researchers are using the diagrams that illustrate an analysis workflow as a communication tool. Specifically, the researchers can use the workflow diagram to show others the steps taken in an analysis and to explain why certain data were selected and why other data might not have been used.

Besides using the information mined from past trials to help design new studies, examine new uses for a drug, or study ways to minimize risks, the data-mining efforts are also being applied to looking at how drugs on the market are being used.

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