Sept. 12, 2007 | Systems biology (SB) technology suppliers face several challenges and opportunities. Scaling up their businesses and participating in the intellectual property their tools and services create has turned out to be difficult. Being allowed to tout the successes they help pharma achieve is also a challenge. Conversely, biopharma’s externalizing of risk should create more business for them. Interestingly, one in silico pure-play, Entelos, managed a successful IPO, and recently a wet-lab specialist, BG Medicine, filed for an IPO.
Here are assessments and predictions from four senior executives in the systems biology technology provider community.
Keith Elliston, CEO and founder, Genstruct, specialist in modeling and identifying mechanisms of action.
“The pharma industry has felt increasing pipeline pressure and, given the whole genomics bust, turned to the biotech industry for new leads, rather than to new technologies. Given their efforts here, it is clear that pharma has a very good working knowledge of every compound presently in Phase I and II coming from biotech. It is also becoming clear that these compounds are not going to save the pharma pipeline. The way I hear it, this is causing quite a bit of depression across the industry, as they see the pharmaceutical industry declining over the next 10 to 20 years.
“The flip side of all this is that we are starting to see the cycle turn back to technology (this always seems to be a three to five year cyclic event – technology to compounds, then compounds back to technology). I think we will start seeing more pharma emphasis on technologies that can make a difference in their pipelines, particularly later stage.
“My personal opinion is that if we don’t see some serious traction over the next 12 months (that is, large, valuable strategic deal flow), then we may need to wait for systems biology 2.0 to hit before the technology will be widely adopted. In that case, what you will see is what companies like Merrimack Pharmaceuticals have done.”
Colin Hill, CEO and founder, Gene Network Sciences, a modeling, reverse engineering, and simulation specialist.
“I think that big [biopharma] companies in general make decisions for a lot of different reasons that are specific to their own circumstances and to the people making them. Some of the changes affecting SB within Big Pharma are driven by a myriad of causes -- from budget cuts from the failure of a single drug, to the politics of a new research head in place, to semantics of the name of a department or function -- and so it is hard to read a lot into each piece of news.
“That being said, what I do see is that the full integration of wet-lab work with in silico approaches had to win out. Even at GNS where we don’t collect the data ourselves, everything that we do is data-driven and our operations data-centric. In silico approaches do not make real-world validation. A number of the early efforts in SB within Big Pharma did not sufficiently integrate in silico methods with wet-lab data, or use the right in silico approaches in the first place, and were bound to fail in their first incarnation. And the evidence for huge value coming from sufficiently sophisticated efforts powered with enough data is mounting. Rosetta is a good example of a success story in SB.”
Pieter Muntendam, president and CEO, BG Medicine, specialist in proteomics wet lab and data interpretation.
“Many companies are successfully doing SB type studies without calling it that way, having a SB department, or even anyone with a SB title. I think this is the (pre)maturing of the space. Not so different from when the Internet came and companies had these small departments of Internet geeks, while the rest had no clue/interest in this (and often did not believe). Now e-commerce is a mature alternative delivery for every retailer and the Internet departments with the geeks have all been dissolved, and e/Internet things are now in the vocabulary of the majority.
“Using genomic, proteomic, and metabolomic measurements (in some combination) followed by modeling, computing, mining, etc., is in these large companies that had SB/IB departments and was much more prevalent outside the departments. Hence I feel that abandoning these departments is a natural progression. Unfortunately we still have not yet had the defining moments [in SB]. Analogous to the Internet, this maturing in the organizations occurred before the defining moments for e-commerce/Internet, and it all worked out very well.”
Zvia Agur, founder, CSO, and chairwoman, Optimata, a computational disease models (Virtual Cancer Patient platform) and simulation specialist.
“I have been to too many SB conferences reporting work where huge databases are created with no clue about what the question is and how to make heads and tails of this gigantic information. Biomathematicians, who sometimes spend months determining the appropriate shape of one curve, know that data are important, but they should be treated as garments which dress the body (the system’s dynamic models) and for validation of model predictions.
“My prediction is that in 10 years or so the current SB approach will be replaced by a more rational approach of ‘data-supported modeling.’ This is Optimata’s approach, and our experience with pharma is the opposite of the general picture. At the same time I have to say that we have shown success in predicting and thus directing some aspects of drug development. Nevertheless, the important predictions we have made in our pharma collaborations, say six months ago, will only be verified in, say couple of years’ time.”