Sept. 13, 2007 | For a decade or more, systems biology has struggled to establish itself as something important. Nudged into existence by powerful new instruments spewing out data and advancing computational power, systems biology (SB) is entering an awkward adolescence during which much of its value will be revealed, but remain far from fully utilized.
The label “systems biology” is pretty awful, except, of course, for the many even worse labels that have been tried. More important is what SB seeks to do: transform biology and health care into a rigorous, predictive science offering a richer understanding of biology and a vastly improved approach to drug development and medicine. SB would build on the molecular biology revolution and elucidate the wiring diagrams (and their rules) buried in the data.
Perhaps the most ambitious framing of the potential of SB is the “P4 Medicine” vision of Institute for System biology founder Lee Hood (see “ISB Is a Sure Cure,” Bio•IT World, August 2007, p. 42) - predictive, personalized, preventive, and participatory. More prosaically, SB eschews the narrow focus on individual ligand-target interactions, and emphasizes specifying entire biological networks in mechanistic detail. In theory, researchers could identify the best intervention points, the optimum agent(s), definitive biomarkers, and sidestep toxic side effects. It should also be possible to simulate network models to conduct “virtual” drug discovery and clinical trials. (See “What Is Systems Biology?”)
While big pharma wrestles with the relevance of SB and how to adopt it organizationally, a few true-believer start-ups are exploiting SB and at least one — Merrimack Pharmaceuticals — has compounds in the clinic. Circling the drug-makers is a diverse gaggle of SB technology providers, many struggling to scale up their fee-for-service and software licensing business models and wondering if they must become drug-makers to flourish long-term. Meanwhile, inside academia and government labs, SB is suddenly gaining ground (See “Systems Biology Is Hot...”).
Big pharma may pay the lion’s share of SB bills, but lately it’s sending mixed signals. Early SB advocates Novartis and GlaxoSmithKline have disbanded or deemphasized internal systems biology ‘departments.’ SB didn’t produce the desired bang-for-the-buck, while harsh financial realities have left little wiggle room for maintaining long-term technology bets.
“Systems biology at NIBR [Novartis Institutes of BioMedical Research] is no longer thought of as something distinct from the other disciplines which are brought to bear on our drug discovery process, such as biology, chemistry, and pharmacology. It is tightly integrated into all stages of the drug discovery process,” Liam O’Connor, director of quantitative biology at NIBR, told Bio•IT World in a wan e-mail.
Other observers suggest Novartis — like many of its Big Pharma brethren — is simply externalizing the risk through collaboration, which is not unreasonable. Even Hood praises Novartis as forward thinking and thinks it would make a good partner.
By contrast, Pfizer, with its $7.4 billion R&D budget, is actually expanding its internal systems biology program as part of a larger company-wide reorganization set in motion by CEO Jeffrey Kindler in January, which emphasizes therapeutic area centers at the expense of global technology centers.
Pfizer might change its SB tune if costs mount and results do not, but David de Graaf, director of systems biology at Pfizer, is betting SB’s future inside Pfizer is robust. The SB organization now “runs all the way into the early phases in the clinic rather than just providing candidate drugs,” he says. “It now needs to go through essentially proof of principle.” Pfizer management is setting up a centralized group to develop SB approaches at the RTC (see “Setting the Systems Bio Syllabus,” Bio•IT World, Jan. 2007, p. 18). “The site mission is now being focused on deep knowledge of pathways, targets, and compounds,” says de Graaf.
The Pfizer group is two years old, and will expand from eight to around thirty researchers. The wealth of local talent has made recruiting the necessary multi-discipline expertise easier, de Graaf says. Pfizer recently recruited former NIBR computational biologist, Carolyn Cho.
Early success in biologics projects and toxicity prediction, says de Graaf, has helped drive SB’s acceptance at Pfizer. “This move is a chance for us to show more broadly what systems biology can do for Pfizer,” says de Graaf. “We are beyond the tantalizing hints stage. The work we’ve done on hepatic injury, which we hope to publish very soon, helped demonstrate that. We don’t only want to show that we can sustain that and grow that incrementally. We want to go into some new areas and really change people’s perceptions. Ultimately we hope to start to grow small groups doing systems biology at all the sites.”
Pfizer’s SB team will focus on biotherapeutics and predictive tox. De Graaf says that pharma’s classical model is to invest heavily in a pathway “and have 8 or 10 projects that are all essentially directed against the same biological mechanism. We hope to not only [reduce the project] number but also to pick the ones that will be winners. That will eventually limit [SB’s] application to particular therapeutic areas. Logical bets would be places like cancer and diabetes and maybe inflammation.”
Lilly is also committed to retaining a robust internal SB effort, and recently announced a major expansion of its SB activities in Singapore. William Chin, Lilly’s VP of discovery, says the company studies protein interactions in rodent models before evaluating their usefulness in humans. “If we could just bridge that gulf a little bit better, if we could be more predictive, we’d be much better off,” says Chin. “Looking at pathways and systems is a better way of doing it. The whole idea is that these systems and pathways are probably better conserved than any single molecular target.”
Lilly’s Singapore center has produced several proprietary markers, says Chin, using some cancer pharmacokinetics and pharmacodynamic modeling. This has helped Lilly convince the FDA that, “as we enter the clinic that we have chosen the right doses.”
Merck also has early SB successes. In an upbeat review in the Journal of Lipid Research last year, Rosetta (Merck subsidiary) researchers Eric Schadt and Pek Lum suggested that reconstructed networks, “provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more robust points for therapeutic intervention.”
Colin Hill, CEO and co-founder of Gene Network Sciences, says, “They are bringing the power of SB and genomics to bear on major diseases such as metabolic syndrome with rather striking results. [Schadt and Lum] essentially claimed to have solved metabolic syndrome with SB and genomic techniques. This paper suggests a big win for Merck after five years of work.”
Here Comes the Flood
So why aren’t the floodgates opening?
One persistent challenge is organizing internal SB efforts to ensure the work is done effectively and is deemed important enough to change attitudes. Should SB groups be centralized, or is it better to build SB skill sets inside therapeutic groups? If centralized, should it act as service centers or do independent work?
“I don’t think anyone has the recipe for how to do it,” says Ruedi Aebersold, co-founder of ISB and currently the leader of Systems X.ch in Switzerland (See “Systems Biology Is Hot...”). A key asset at the ISB, Aebersold recalls, was that “people are close by and communicate, and not just in a WebEx meeting.” Communication thrived across departments, groups, and disease areas and, this being Seattle, “they actually run into each other, they sit together at the coffee table and interact informally.... That was an extremely striking experience at ISB that I’ve never experienced before or since,” says Aebersold.
But Jack Beausman, principal scientist, pathway capabilities at AstraZeneca, feels that SB “will be done within specialized groups.” Trying to transfer that thinking to super-users and biologists, he says, hasn’t been terribly successful. “At this point it’s an art that’s only going to be successful for the next 4 or 5 years within specialized groups, specialized companies for that matter,” he says.
Beausman points out that big pharma’s investment in SB is miniscule compared to what it’s spending on companies with promising compounds. His own company spent $14 billion this year to acquire MedImmune. “I don’t think people view in the very near future that systems biology, and certainly modeling, have any transformative potential. It’s incremental,” says Beausman.
Not that incremental progress is bad. Modeling is almost always involved in the target decision-making process at AZ, reports Beausman, and his small group will grow slightly this year. “We’re not really identifying targets. That’s very hard,” he admits. Rather, his group looks at a particular system, such as angiogenesis, and asks: “What would be good points at which to intervene?” Adds Beausman: “Sometimes you can identify points of attack that are clearly not going to work in systems like that, and that’s valuable information. Sometimes we don’t know enough and can’t say anything.”
Prying SB success stories from close-mouthed pharma is a challenge. Moreover, most early SB-related successes have been go/no-go decisions. While halting problematic programs early saves (big) money, it doesn’t excite drugmakers the way identifying the next blockbuster would.
Alex Bangs, CTO and co-founder of bio-simulation specialist, Entelos, says his firm’s impact has “been in cases where we’ve predicted that a target was not going to be effective,” resulting in the project being stopped. Says Bangs: “We’ve had people say, ‘Well gee, we keep giving people negative news about things.’ And the answer is, ‘Well, a lot of stuff fails.’ So it’s not surprising that there are a lot of negative answers.”
Landing truly important work is another hurdle. Therapeutic groups are often suspicious for many reasons, including reluctance to share credit or control, and internal reward systems that actually financially penalize therapeutic areas for such collaborations. At Pfizer, de Graaf is working with portfolio managers in therapeutic areas to identify projects. These decision makers worry more about advancing important projects than preserving pet projects or turf wars. He stresses a healthy dose of diplomacy is always needed.
In the end, Hood probably has right perspective: “What will convince [pharma] are examples of drugs that came out of these approaches that were produced rapidly and economically. The only question is whether or not, after you get those examples, it’s too late for them to get in the ball game... There are going to be a lot of younger companies who see that this is the way to do things.”
Hood says pharma has tended to merge with these companies, frequently taking the product and throwing the company away. “Whether they can merge with a company and retain its unique individuality is a question,” he says. “I know of only one example (Roche/Genentech) when that’s been done and that was done superbly.”
The Merrimack Method
Merrimack Pharmaceuticals, formed six years ago by a multidisciplinary group of MIT and Harvard researchers, is using SB to find drugs for cancer and autoimmune disease. The company was spun out of a DARPA program designed to get engineers, mathematicians, and biologists to do biology differently. Co-founder Ulrich Nielsen, VP of research, was a postdoc with Peter Sorger at MIT. “There was a core of people, especially chemical engineers and biologists, who saw new avenues, not just to understand biology but to change the way drug discovery is done.”
While most SB spinouts have opted to become tool companies, Merrimack bucked this trend. It also didn’t take VC money, yet raised $145 million over five years from other sources (which Nielsen declines to name). Headcount has grown to about 75. Merrimack espouses an approach called ‘network biology’ to better therapeutics. The company says it has “developed a proprietary technology platform that enables the high throughput profiling of complex biological systems... Network Biology seeks to understand the system dynamics that govern protein networks — the functional set of proteins that regulate cellular decisions. More than a research tool, Network Biology is a set of technologies designed to enhance the entire drug discovery, development, and commercialization process.”
Merrimack’s models are mechanism-based, initially informed by the literature, but quickly enhanced by the company’s extensive (and expensive) wet-lab to generate large data sets. “These are differential equation type models that describe molecular interactions by knowing the concentrations and rate constants for how proteins interact,” says Nielsen. “You can build up fairly large models of the system you’re working with, and importantly, you can use those models to simulate problems that are not necessarily in your data set.
“We’ve spent a lot of our focus on growth factors in cytokine signaling. We can ask questions like, how do you actually best inhibit a given pathway, and look at what are the most sensitive targets in the pathway. There are some really nice computational tools for doing that.”
Merrimack is focused exclusively on biologics. Its first candidate, MM-093, which targets rheumatoid arthritis, psoriasis, and multiple sclerosis, is in-licensed from McGill University and is not fully representative of the company’s approach. The RA project is in phase II trials. Of three other homegrown cancer candidates, MM-121 should enter phase I this winter. “It was the first pathway we built,” says Nielsen, “and despite being a very-well trodden area, we found insights that we believe are important and I think we’ve gotten a few years leap.”
Merrimack has modeled signaling networks regulating the biology of solid tumors, and reports developing several novel approaches to target the signals driving tumor growth. Companion diagnostics also figure prominently in Merrimack’s planning, particular given the heterogeneity of cancer. Says Neilsen: “Network biology is giving us a new view on cancer that’s not necessarily defined by ...growth physiology or even pathology. Whether the tumor comes from breast or ovarian tissue or colon, doesn’t always define the molecular pathology behind it.”
Unlike many in silico-mostly SB plays, Merrimack depends heavily on its experimental biology and also intends to manufacture its product as well. Nielsen says the company has embraced multiplexing technologies and robotics for high-throughput analysis of protein expression and phosphorylation. “Experimental biology is obviously much more expensive than literature mining and computational biology, so yeah, we invested heavily in method development, equipment, and people,” he says.
Nielsen offers an opinion why big pharma has been less successful in adopting SB. “I think most pharmaceutical companies, at least on the surface, are embracing these technologies, often by establishing systems biology institutes or departments.” By contrast, Nielsen says Merrimack stresses “the integration of this approach into the whole of drug discovery, and in fact, bringing these tools into simulating problems in drug discovery before you create a drug.” Whereas pharma has applied SB in later stages, for example identifying responders in clinical trials, this is “a very different paradigm from sort of engineering things de novo, which is what we’re doing,” Nielsen says.
Another example is Avalon Pharmaceuticals, which has had a successful IPO. Leveraging gene transcription and proprietary cell-based assay technology, Avalon’s lead candidate IMPDH (inosine monophosphate dehydrogenase) inhibitor, AVN944, is in phase I trial for heme malignancies and expected to move into Phase II trials soon.
Clinical trials are far from having a proven drug, and that’s the fuel that will truly ignite the systems biology fire. No drugs, no bonfire. But everyone seems to agree the inferno is coming, just not on when.
Technology advances and cost reduction are still needed. Perhaps more knowledge is needed too, though Nielsen scoffs at this idea. So too does Douglas Lauffenburger, director of MIT’s department of biological engineering, and an architect of many of the ideas being pursued by Merrimack. “I am entirely confident about the bright future of systems biology, even if some pharmas bail out in the short run,” says Lauffenburger. “This is a long-term revolutionary approach to biology, so it is patently infeasible for it to ‘dissipate.’”
Gustavo Stolivitsky, manager of functional genomics and systems biology at IBM’s T.J. Watson Research Center, concedes that, “systems biology hasn’t delivered some big clear success story.... But I think everybody knows that in the long run, it will be one of the ways to go, if not the only way to go.”
Aebersold hesitates to offer long-term projections but then reconsiders: “I don’t see one particular inflection. I think there are many inflections whenever someone comes up with a new technology that measures, let’s say, some contextual relationships or a software, an algorithm. It’s not an endpoint like the genome project, where if you’re Bill Clinton you go and stand in front of the camera and say, now it’s been done [even if it hasn’t], and have a press conference.”
In some ways, systems biology is both a philosophy and a methodology. The philosophy is pretty clear; try to understand in fine mechanistic detail how living systems work as integrated entities, from pathways to networks to whole cells and ultimately the entire organism. That doesn’t sound so different than just plain biology, though a change in language is sometimes needed to catalyze actions.
The methodology is more about coordinated use of the diverse tools required to connect the dots the philosophy wishes to understand. In practical terms today, it’s primarily tools to interrogate molecular (and other biochemical) systems and computational power and math to make sense of the data. In this sense, many companies have fastened onto various technologies and championed their use as demonstrating their systems biology credentials. Often such claims have left observers confused about what really constitutes systems biology.
The next decade is likely to be systems biology’s journey through adolescence to adulthood. Perhaps, as Pfizer’s newest SB department member Carolyn Cho suggested at DDT 2007, the mark of adulthood will be the disappearance of the term altogether, as it merges into the mainstream of biology.
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