By Bernadette Toner
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 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 says.
New Platform, New Model
The MIT collaboration should bolster Entelos’ capability in subcellular modeling. Until now, the company has primarily applied 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 says. “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.”
Entelos 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 and 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 says.
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.
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,” says Jim Collins, Cellicon co-founder and Boston University biomedical engineer, “but they don’t have the capability to identify what else the drug hits.”
The company celebrated two milestones in early July: the publication of its network mapping approach in Science and the appointment of George Shimer, former VP of research at Cubist Pharmaceuticals, as its CEO. Cellicon 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.
Too New for 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 acknowledges.
“I think to a large part, people don’t completely understand what this is about,” he says, “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.”
A version of this column appeared in BioInform’s July 21 edition.