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 date. Typically, pharmaceutical companies working on an FDA submission do a set of trials as part of new drug application and integrate a summarization 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 additional analyses are requested by regulatory agencies 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 Corporation.

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 U.S. 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 re-examine clinical trial data once a drug is, say, half way 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 may 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. Specifically, 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, like the Insightful Miner, give statisticians and researchers the ability to apply a wide variety of analysis techniques to a dataset without having to be experts 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.

Are you mining existing clinical trial data? What statistical analysis tools are you using? For what purpose are you undertaking the task? Drop me a note at Salvatore_Salamone@bio-itworld.com and let me know what you are doing along these lines.