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Building Integrative Biology at Boehringer Ingelheim

By John Russell

January 5, 2009 |  Boehringer Ingelheim (BI), the German family-owned biopharmaceutical conglomerate, is making a (modest) bet on integrative biology at its Ridgefield, Conn., facility. Leading the new effort as VP of biotherapeutics and integrative biology is David de Graaf, who until last summer was Pfizer’s director of systems biology at the Research Technology Center (RTC).

That division is now under the control of Pfizer’s new Biotherapeutic and Bioinnovation Center (BBC), led by Corey Goodman, based in San Francisco. Ironically, de Graaf will be reporting to Phil Vickers, the former RTC chief who now heads BI research in the U.S. Vickers is “here to create a drug hunting culture,” says de Graaf.

At a time of economic crisis, particularly for big pharma, de Graaf relishes the opportunity to work for a successful family-owned organization and focus on a long-term mission. Having relocated to southern Connecticut, one of de Graaf’s key challenges is how to attract the same talent available around the large academic centers.

De Graaf has been assigned responsibility for product as well as for new science and enabling technologies. “A big attraction was the ability to go and get something very concrete out that would have the stamp of my organization on it,” he says.

De Graaf will be working closely with the two therapeutic areas—immunology and inflammation, and cardiovascular disease—helping to identify targets and the modalities to be deployed to affect them. He will marshal a package of biological tools, from animal models to text mining and mathematical models that put the targets in context. His group will also support both NBEs (new biotherapeutic entities) and NCEs (new chemical entities) as the start of a lead optimization process.

He sees excellent synergy between “sub-systems biology”—the understanding of discrete pieces of signal transduction—and antibody therapeutics. “You can very quickly know a lot more about antibody structures because the space in which they move is just so much less complex than intracellular targets,” he says. Not that it’s going to be easy, however; de Graaf admits “we’re starting at zero.”

De-Risking the Portfolio

Details of the new integrative effort are scarce as the effort ramps up, but De Graaf will have a group focused on biomarkers, building on BI’s existing strengths in genomics, and genetically engineered organisms. Other core strengths include protein purification and protein expression.

“We’ve just started to build an electronic biology group—e-biology—which is led by Will Loging. He’s got a big focus on predictive models, predictive biology in general.” The e-biology effort is based on “overlaying data sources from a number of different places, including text mining, as a way of getting to those data. But we’re not proud—we’ll take expression data, protein array data, whatever we can get. What we really want to do is integrate all of those data sets to help us understand the biological context of our disease and our targets.” An early project is in early safety and toxicology, identifying assays that will enable De Graaf to “start to look at de-risking our portfolio very early on for safety issues.”

Text mining is another important component. “Often people miss a different perspective or a completely separate line of research that tried to address the exact same issue,” he says. Using natural language processing and other technologies, de Graaf’s team is already delivering relevant project information to scientists.

As for the systems biology effort, de Graaf is starting with a small team before working with academic and biotech partners to expand their capabilities, aiming for about ten people over the next year or so. He projects a similar size for the e-biology team. If successful, those efforts could be translated into similar groups at other BI sites.

De Graaf sees integrative biology as “the ability to take data sources from a number of different places and integrate them to help us understand biological systems better.” That includes understanding the target in context, as well as the downstream consequences of manipulating a particular target. “Part of what we integrate is not only data but also models, whether it’s the human disease model in primary cells or animal models, we need to integrate all of those.”

He continues: “Sometimes the only thing we need is a bit more context and a bit more understanding of what’s affected by a particular modality or your target and we are able to move ahead with a decision. Sometimes we need to understand things in much greater detail, and we are going to make models that are predictive of biology, of human disease biology.”

De Graaf says he’ll know when his efforts are successful for the biotherapeutics program when the first two or three projects hit the clinic. Ideally, de Graaf would like to see external pharma partners looking at BI’s portfolio in a couple of years and say, “Wow, your portfolio looks different; your portfolio looks exciting.” Today, most pharma portfolios look depressingly alike. Says de Graaf: “What we want to do is appropriately differentiate in areas and start to take risks there in order to be able to move into new areas and not always be competing.”

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    A better understanding of the biology and the complex interaction network of different pathological processes is a mandatory requirement for novel therapeutic breakthroughs. Right now, many targets in drug discovery programs are chosen based upon a very limited amount of information, which everybody has access to. That is presumably the reason why so many pharma pipelines look so similar. In addition the medicinal chemistry makes it a point of honor to develop very selective and potent molecules.
    However, it becomes more and more clear that many chronic diseases need to be addressed with a combination of interventions, because of the redundancy of the process network in disease. The most succesfull drugs are multi-targets drugs; however they have often been found by serendipity.
    The industry needs a systematic way to identify useful multi-target drug profile and that’s where systems biology and predictive modeling come in. Using mathematical quantitative relations and supported by computer power, better insights into the effects of non-linear interactions become available, helping to understand better the biology.
    It is time for the pharma-industry to start re-engineering their drug discovery process along the lines of many other succesfull industries, such as micro-electronic and aviation. The recent Pharma2020 report by PwC suggests that only the companies that introduce this technology into their business processes will be successful and can solve the pipeline problem.
    Only then can we get really exciting new drugs that can address unmet medical need.

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