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Targeting Pharma, Systems Biology Firms Tune Models for Lead Optimization


Systems biology may be young, but it’s growing up fast to lure in big pharma customers.

Two separate firms recently displayed this precocious attitude, announcing computational systems biology projects with an emphasis on the downstream applications of their technology: Systems biology veteran Entelos said it is teaming up with researchers from the Massachusetts Institute of Technology to model the intracellular pathways in T lymphocytes with the goal of improved drugs for common immune diseases; while newcomer Cellicon arrived on the scene with a proprietary gene network mapping technology that it intends to apply to anti-infective discovery.

Rather than limiting the promised benefits of their technologies to target discovery, each company is also touting its approach as an alternative to traditional lead optimization approaches. It’s a step toward “the Holy Grail” of computational systems biology, according to Chris McKenna, Entelos’ business development manager for immunology. Within a single computational model, “if you can connect a target to a clinical endpoint, and know the magnitude and the timing of that target with the small molecule, that’s essentially the definition of a drug,” he said.

The MIT collaboration will be Entelos’ first crack at subcellular modeling. Until now, the company has limited its PhysioLab computational platform to modeling tissue- and organ-level systems. The project will add a new level of fine-grained detail to the company’s “top-down” approach, “where we’ve already characterized the multi-cell environment that relates directly to the clinical outcome,” McKenna said. “That way, you can make a direct connection between a small-molecule compound and the clinical outcome, which is really what everybody’s interested in.”

Cellicon, which has developed an approach to create quantitative gene network models using a minimal amount of gene expression data, also sees an opportunity for its technology in lead optimization. “Drug companies are very good at setting up assays to identify whether a drug is hitting a target,” said Jim Collins, Cellicon co-founder and CSO, “but they don’t have the capability to identify what else the drug hits.” Cellicon’s technology, he said, can determine the direct as well as the indirect effects of a compound within the context of an entire network in order to help customers select those compounds best suited for pre-clinical trials.

Old Platform, New Model

McKenna said that Entelos’ current pharmaceutical clients spurred its new interest in subcellular modeling. “They’re being continually driven further into the intracellular domain to capture intellectual property,” he said. The company already had a relationship with Douglas Lauffenburger, co-director of MIT’s biological engineering division and a member of the steering committee for the university’s new Computational Systems Biology Initiative. “Through that, we have learned about different methodologies they were developing for systematically measuring individual cells, and then using techniques like siRNA and gene transfection to better understand how intracellular signaling pathways and the stimuli that regulated those pathways controlled cellular phenotypic output,” McKenna said.

Recognizing an opportunity “to systematically characterize these immunology and inflammatory pathways within T-cells and dendritic cells to better understand what was happening in there to support our customers,” Entelos offered to fund a research project between itself, Lauffenburger’s lab, and the lab of MIT immunologist Luk Van Parijs, McKenna said.

Entelos and the MIT collaborators will feed data from gene expression experiments, siRNA studies, gene transfection readouts, cellular readouts, and cytokine production measurements into mathematical models developed by Lauffenburger’s lab and Entelos.

“Our intention is to integrate that directly within our asthma, arthritis, and other immunology platforms,” McKenna said, but the commercialization details are still a bit fuzzy. “We have a lot of flexibility with how we can do this. We can make it a stand-alone cellular product, we can integrate it into our disease models, or we can make it into a database to analyze gene expression,” he said.

The two-year project has been underway for several months already, and McKenna said that “useful” data should start coming online in about six months.

New Platform, Old Business Model

Cellicon, the Boston-based brainchild of Boston University biomedical engineer Jim Collins, celebrated two milestones in early July: the publication of its network mapping approach in Science [2003, v. 301, pp 102-105] and the appointment of George “Skip” Shimer, former VP of research at Cubist Pharmaceuticals, as its CEO.

Shimer’s goals for the company are clear: Scaling up the technology and demonstrating proof of principle for potential academic collaborators, pharmaceutical partners, and investors. After the company was launched with a small seed round that he described as “enough to get us started,” Shimer said he is confident it can close a Series A round within the course of the next year.

Shimer’s confidence that the company will be able to attract funding stems partly from the fact that Cellicon is following a very familiar business model. “Cellicon is an early drug discovery company, not a technology platform company,” Collins stressed. The company is using its technology to develop a comprehensive functional map of the SOS pathway in E. coli that it plans to use in its in-house antibiotic drug discovery program. Cellicon scientists demonstrated the effectiveness of the approach in the Science paper by mapping a nine-gene subnetwork of the SOS pathway with 39 gene/protein connections. They are now extending that work to the entire pathway.
The method shows promise for antibacterial research, Shimer said, because it may lead to better ways to interfere with the stress response in bacteria. Further down the road, Shimer said he envisions applications for the technology in other areas such as chemogenomics. The company also plans to apply its technology in collaborations with pharmaceutical firms and other organizations.

New Platform, Old School

Whether pharmaceutical customers are willing to turn to the still-unproven methods of cellular modeling, however, remains to be seen. “The industry is still educating itself about this,” McKenna acknowledged. “I think to a large part, people don’t completely understand what this is about, but when you start to look at it as dynamic content that can be scrutinized and interrogated based on the question that you’re interested in asking, to me, that’s the exciting part of it.”

Shimer shared McKenna’s blend of caution and enthusiasm. The approach is “cutting edge and a different way to think about the problem,” he said, but so far, he’s found, “we’re talking to an educated audience. [Potential customers] are able to appreciate what this technology can do.”

According to Collins, many researchers are running up against the limits of clustering and other approaches used in gene expression profiling and are hungry for alternatives. When microarrays became broadly available, molecular biologists “collectively jumped off a cliff hoping that insights would emerge,” he said, “but it doesn’t happen that way…Clustering is a smart thing to do, and a good first step, but you have to take it to the next level.”

By taking on the challenge of its own discovery program, Cellicon is banking on its ability to prove the value of that next level on its own. It’s very likely that potential pharmaceutical customers won’t be alone in tracking its success — the burgeoning cellular modeling sector will be counting on it.

— BT

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