12:00 AM
Sep 24, 2012
 |  BioCentury  |  Strategy

Big data: Then and now

Genomic pioneers gone; how next-generation players will solve big data

The early genomics pioneers awakened the biopharma industry to the value of capturing and using data to drive drug development. But both the pioneers and those who have followed agree the industry still has far to go to harness the power of big data.

Human Genome Sciences Inc. was the last of the independent first-generation genomics companies. Having raised $3.9 billion since its inception in 1992, it was acquired last month by GlaxoSmithKline plc for $2.9 billion. HGS and its peers believed analyzing human sequence data would unleash a wave of new cures. But it turned out everyone was naïve about the complex nature of diseases, and the fount of cures never materialized.

Recognizing the complexity was only a first step. Now, companies have to figure out how to integrate, analyze and make decisions based on not only their own data, but also data stored in hospital, clinic and payer databases.

They will have to rethink how to collect the data, as well as store, reformat, analyze and apply it.

They won't need to start from scratch. The IT, financial services and online retail sector have shown that concepts like machine learning and natural language processing can be used to collect and query millions of disparate data points. But biopharma companies or third parties will need to design algorithms and data processing technologies tailored to the needs of the sector.

Some companies are beginning to do this.

Foundation Medicine Inc. integrates genomic data from individual cancer patients with data obtained from clinical trials of investigational and approved drugs to help physicians choose the best treatments for these individuals (see BioCentury, July 18, 2011).

Last week, the biotech raised a $42.5 million series B round from new investors Deerfield Management; Casdin Capital Management; Redmile Group; Laboratory Corp. of America Holdings; Roche Venture Fund; and WuXi Corporate Venture Fund. Existing investors Third Rock Ventures; Google Ventures; and Kleiner Perkins Caufield & Byers also participated (see "Crowding into Foundation," A16).

GNS Healthcare Inc. uses postmarket and insurer claims data to determine which patients are most likely to benefit from a given drug and those most likely to suffer serious side effects.

Last week GNS partnered with CRO Covance Inc. to develop computer models that predict the likelihood of development success for a drug candidate (see "Hypothesis Free," A5).

But these are early days. To get to the starting line, companies will need to invest more in data infrastructure through partnerships, licensing deals or building their own expertise. It also will require computer savvy and IT-trained experts elevated to executive positions within biotech and pharma companies.

Big promises

In life sciences, big data now is the collection, integration and mining of large data sets to answer complex questions about diseases, patient response and clinical outcomes. The goal is to identify patterns that wouldn't be recognizable with simple statistical analyses.

HGS wasn't called a big data company when it was founded but, based on its business strategy in the 1990s, would be considered one today. The biotech planned to use its database of DNA sequences derived from its expressed sequence tag (EST) library to discover and develop cures.

Companies like Millennium Pharmaceuticals Inc., deCode genetics Inc. and Celera Genomics Corp. had similar aspirations.

But the cures never really materialized. When HGS was acquired by GSK, it had one marketed drug derived from its genomics platform - Benlysta belimumab for lupus (see BioCentury, April 23).

Millennium had a marketed oncology drug, Velcade bortezomib, when it was acquired by Takeda Pharmaceutical Co. Ltd. for $8.8 billion in 2008, but the proteasome inhibitor came via an acquisition (see BioCentury, April 14, 2008).

Celera, which won the race to sequence the human genome in 2000,...

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