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Predicting the Future of Systems Biology

An in-depth conversation with Genstruct CEO, Keith Elliston.

Sept. 18, 2006 | Systems biology is once again a popular buzz phrase, though most companies actually doing it don’t call themselves systems biology companies. Buoyed by promising results from a recent collaboration with Pfizer on drug-induced vascular injury, Keith Elliston, CEO of modeling and biosimulation specialist Genstruct, offers a candid view of industry’s flirtation with systems biology and the future prospects for the field. He spoke with John Russell, Bio-IT World’s executive editor. An edited version of that conversation follows:

 Keith Elliston
Q: Are more drug companies becoming believers in systems biology?
Well, the drug discovery industry is kind of slow to adopt new approaches because people have been burned. So I don’t expect to see a huge churn. I expect to see the classic Geoffrey Moore, Crossing the Chasm, kind of thing: You’ve got a small group of companies that are visionaries and early adopters; a large group that is more conservative mainstream; and then you’ve got a number of companies that are the laggards. What we’ve been seeing lately are a couple of the visionary early adopters like Pfizer getting more active.

Will systems biology get across that chasm?
At some point everybody will adopt systems biology in some form. I think we’re a few years away from that... We’ve already had a couple of companies that we talked to [18 months] ago come back and say, “We went off and decided we were going to do systems biology on our own because you guys seemed kind of expensive. And we spent more money than we expected putting together software, training people, buying hardware, devoting time, and we’ve come up with nothing.” And I said, “We don’t look so expensive now, do we?”

That certainly suggests growing interest, doesn’t it?
A lot of interest doesn’t always translate into a lot of business... We can show success with systems biology, not just that it helps you make better decisions faster once you have the drug, but it can help you find drugs more efficiently. Period. You might say better drugs faster, but [making] better decisions faster is only a little part of it. Failing early is still failing, not succeeding.

How difficult is it to “teach” systems biology to client researchers?
I think if you’re going to try to help them understand systems biology and help them make use of the tools themselves, you’re going to have a very long road. It has to be incremental. The first step has to be familiarity. I think the brilliance of companies like Apple Computer is the approach they’ve taken, which is to wrap it up, and just get people something that they can use without having to learn all the technology behind it. When we started working with the safety sciences group at Pfizer, they didn’t have much experience with systems biology. What we were able to do is spend four months with them, take their key data, model it, define it, and work with them to give them some actionable results without them having to learn how to use our platform, tools, and methodologies. We basically delivered the iPod of systems biology. It gives us a foundation to start “teaching” systems biology if that is what the researchers want.

What mergers or alliances do you think are likely in systems biology?
You see a lot of cooperation among the different pathway houses these days. GeneGo is working with Ariadne, and we’re seeing more partnering in those spaces. We’ve done work with Ariadne and Jubilant, and use some of their tools and capabilities. We find it useful to bring in third-party technology where it helps supplement our core competencies.

I think product companies need to be product (software) companies. So I don’t see mergers of product companies with collaboration-based companies like us. What I would see is more of a combination of capabilities to build a more fully integrated company. For example, today we don’t do any data generation. One of the decisions we made early on was that if we were going to focus on what we thought the key linchpin of systems biology, it was really going to be in trying to understand what all the data meant. Since we don’t control data generation, enforcing issues of quality, capability, [and] design are based on our ability to influence our partners. Sometimes, we’re more successful than other times...

Doug Lauffenburger published the four Ms of systems biology — Measurement, Mining, Modeling, and Manipulation. It really takes those four things to do it all. Doug’s group at MIT does all four of those themselves, and thus, they can control things from end to end. Right now, we do the mining and modeling and our partners do the manipulation and measurement. But I could see the potential of building out a company that would bring all those things together. Then you have to ask yourself, if you can do all that well, do you want to do that in partnership or do you in fact want to go after the ultimate output: better compounds?

Does that say something positive for a company like Predix Pharmaceuticals?
I think it certainly would. But you’ve got to be able to actually get it done and you’ve got to prove that you’ve got some ability. The latest numbers I’ve heard is that while small companies are making up about 70 percent of new IND filings, their failure rates are almost twice that of pharma in clinical trials. So, if you’re going to be successful, you’ve got to have good output; you have to have good numbers, and you have to have high success rates. The company that can do that first is going to be very successful.

Who are your major competitors?
Entelos is a company that is doing systems modeling, though using a mathematical approach. Our approach really focuses on modeling molecular systems and uses high-throughput genomic, proteomic, and metabonomic data. Entelos seems to be focused on building virtual patient models and modeling higher-order phenomena. I actually think our approaches are complementary.

The companies that I’m keeping an eye on are some of the software providers. I look at them and say, number one, if they decide they are not going to be product companies and are going to try and do what we do, they would have a head start over people starting from scratch. Number two, if they figure out how to enable pharmaceutical companies to do systems biology in house in a way that’s profound, then I’ve got to see them as competitive. And the ones I watch are GeneGo and Ariadne, because they seem to be the most innovative.

Do you think of Compugen being in your space?
Well, Compugen is an interesting company. I guess in some ways you’d have to try and put them there because they are using a lot of computational technology to try and figure out areas of biology. It is a company I keep my eyes on, but Compugen has had a lot of changes in management of late. They’ve certainly delivered value for customers, but I’m not sure if they’ve reaped the benefits of the value they’ve generated...

Why did Google succeed in search where Netscape and Microsoft and Yahoo haven’t? Because they concentrated on one thing and are doing it well. That’s a great lesson for companies like Compugen. They’ve got to figure out what the one thing is.

I’ve seen Optimata. They look to me like they are trying to be more like Entelos. I think we’re at the level of molecules, and they’re at levels above that. I think these levels will converge, but today those are very different spaces.

Would Gene Logic fit into that category?
The one thing that’s most interesting about Gene Logic is what they’re doing in drug repositioning. They are generating a lot of data, but I think if they don’t leverage it with system modeling approaches, they’re going to face the same problem with complexity that we are often asked to tackle for our partners. It’s figuring out how to harness the power that’s in the data that is critical.

Is the necessary threshold of success being reached?
That’s a great question. Overall we’re seeing some success. I think you have to look at some of what Entelos has done and say they’ve had some good successes in applying their above-the-cell-level modeling and their virtual patient model. They’ve been able to help people make clinical decisions. If you look at trying to understand molecular events, I think we’ve shown some real successes through the work we’ve done with Pfizer and other companies. I don’t think we’re there in terms of showing the full value [of the technology], but we’ve been able to generate the kind of successes you need to keep building positive momentum.

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