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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

04/08
2013

Data Analysis to Boost Drug Development for Pharma Companies

col pharma biotech strategic services business development launch analogs benchmarking ci licencing 350 150x150 Data Analysis to Boost Drug Development for Pharma CompaniesThe recent Spotfire on-demand webcast, “Data Science 2.0: Guided and In-line Analytics with Spotfire,” covers how Spotfire and data science are impacting global business across every market and industry.

For example, if you work in the pharmaceuticals industry this may mean using analytics in clinical trials to identify safety issues more quickly and bring effective therapies to market faster.

 

 

Here’s a sample clip from the pharmaceuticals segment of the webcast:

 Data Analysis to Boost Drug Development for Pharma Companies

Perhaps no sector of the economy could benefit more from big data and the use of predictive analytics to churn through vast expanses of information than the pharmaceutical industry.

After all, it’s one of the few industries where companies create entirely new products every time they set out to take potential drugs or treatments to market, notes this Forbes article.

Not only do pharmaceutical companies face challenges that most companies do not in terms of product development – car companies are relatively sure-footed in designing and manufacturing new cars – but they’re also facing the astronomical costs associated with researching and testing new medicines.

To improve the odds of successfully creating new drugs, pharmaceutical companies are increasingly trying to “pick the winners” and “kill the losers” earlier in the phased development process to avoid the high costs associated with failure later in development.

Herein lies the possible role for analytics, the article notes.

“Most pharmas employ talented biostatisticians, the companies’ analytics capabilities tend not to be exceptional,” according to David Shaywitz, the author of the article. “I’m intrigued by the possibility of a pharma company that decides to differentiate by focusing on brilliant analytics, analytics that would be the envy of the industry – and of others. Imagine if you had a team comprised of the sort of people who now work at Google, Facebook, Palantir, and turned them loose on all the different sorts of data pharma companies deal with, from basic science to marketing. It’s hard not to envision this would be radically transformative.”

Big data could create more than $100 billion in value for the pharmaceutical and medical device sector, according to a study by McKinsey Global Institute,

Pharmaceutical companies can use analytics to aggregate research data in research and development efforts to more quickly predict drug development outcomes, thus reducing the costly paths to take new drugs to market.

“Predictive modeling can shave three to five years off the approximately 13 years it can take to bring a new compound to market,” according to the report.

In addition, data analysis could be used to design clinical trials and target patients for clinical trials. Moreover, analytics can be vital in monitoring and mining clinical trial data and patient records to identify negative effects or benefits from the drug’s use, McKinsey notes.

“Analyzing the (near) real-time collection of adverse case reports enables pharmacovigilance, surfacing safety signals too rare to appear in a typical clinical trial or, in some cases, identifying events that were hinted at in the clinical trials but that did not have sufficient statistical power,” according to the report.

This data analysis could be particularly powerful to help companies avoid drug withdrawals, which hit an all-time high in 2008. For example, Merck suffered a 33% drop in shareholder value in a few days and faced $7 billion in legal and claims cost after pulling Vioxx from the market in 2004.

Finally, data analysis could be used by pharmaceutical companies to help develop personalized medicine, where genetic variation and specific drug responses are taken into account in the drug development process so that medicine can be tailored to an individual.

“Impressive initial [personalized medicine] successes have been reported, particularly in the early detection of breast cancer, in prenatal gene testing and with dosage testing in the treatment of leukemia and colorectal cancers,” McKinsey notes. “We estimate the potential for cost savings by reducing the prescription of drugs to which individual patients do not respond could be 30 to 70 percent of the total cost in some cases.”

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