in silico predictions establish a beachhead in the war on cancer.
A hallmark of a good predictive model for drug development is the capacity to grapple with myriad “what-ifs.” Usually, this takes real-world data, and gobs of it. But recently, it was the lack of raw data that sparked an innovation.
Consider the US National Cancer Institute’s NCI-60 panel of human cancer cell lines, a resource that has been screened with over 100,000 chemical entities: “Out of the 60 cell lines that are on the NCI panel, there are none from bladder cancer,” explains Dan Theodorescu, MD, PhD, Paul Mellon Professor of Urologic Oncology and Molecular Physiology, University of Virginia, Charlottesville, Va. “This was a motivation for me to extrapolate from the NCI-60 cell lines to a bladder cancer panel,” an effort requiring a new set of algorithms collectively known as “co-expression extrapolation” or, COXEN.
In brief, COXEN (see diagram, below) uses NCI-60 gene expression data, correlated with in vitro responses to anti-neoplastic agents for all 60 cell lines, and then compares that to expression data from bladder cancer samples. The result is a COXEN score, which is the probability of a therapeutic response, either for a given bladder tumor sample, or for the collection overall. This prognostic ability is invaluable in bladder cancer where there are so few therapeutic options. “At the very least, you want to assign patients to a drug combination that has the highest COXEN score for that particular tumor,” says Theodorescu. The greater value of this approach, however, is the ability to mull over the as-yet-untried: “We could analyze someone’s tumor by COXEN, look at scores for all the 75 FDA-approved [oncology] drugs, and then give that priority list to a medical oncologist who can create an utterly novel combination,” he says.
While this new clinical approach is being validated, COXEN analysis has been expanded to include data from the full battery of chemicals, approved and otherwise, previously thrown at the NCI-60 panel. This has already resulted in 115 hits, and one solid lead for bladder cancer. “C1311 is an analog of the top hit, an agent which has actually been used in two benign conditions, as well as two cancers which are in ongoing Phase 2 trials. It’s especially exciting,” says Theodorescu, “because it’s an orally bioavailable drug.” Furthermore, other drugs with known activity in bladder cancer such as E09 (Eoquin, Spectrum Pharmaceuticals) have also been identified among the top hits, which further supports the ability of this approach to identify effective compounds for bladder cancer.
With industry pipelines in a state of low flow, and costs skyrocketing, there is greater incentive to explore new venues. “Why not do drug discovery in an academic environment?” asks Shuxing Zhang, PhD, assistant professor in the Department of Experimental Therapeutics, M.D. Anderson Cancer Center, Houston. “M.D. is the number one cancer center in the world. We have very good basic science; we have people in clinical trials, expert clinicians in cancer treatment…” Zhang was retained last year by the department chair to establish a discovery program, with in silico investigations as its core.
Directed by Zhang, the new Molecular Modeling Service (MMS) at M.D. Anderson is being offered to speed drug discovery by incorporating the techniques of bioinformatics, chemoinformatics, and systems biology. “This is a different concept to try to put them together.” In the broadest sense, the application of these approaches is either structure-based, or ligand-based. For instance, given a protein structure, MMS can run a molecular docking search against a database of five million known compounds, with a query of 50,000 candidates taking no more than two computing days.
More complex investigations will soon be handled by a Web-based portal within MMS called “M.D. Workbench.” This service will allow outside academics the opportunity to use a relational database to discern signaling pathways crucial to tumor survival. Should this process result in a virtual drug candidate, Zhang hopes to soon have a database robust enough to assess a candidate’s ADME/Tox (absorption, distribution, metabolism, excretion and toxicity) profile—all before a single milligram of drug has been manufactured.
For now, the challenge is the data. “One thing I really need,” says Zhang, “is the ADME/tox studies. The data from academics is very limited, so we would really like to have some clean data for real drug compounds from pharma.”
Connecting the dots
The underlying problem we see in biomedical research is data fragmentation,” says John Quackenbush, PhD, professor of biostatistics and computational biology, Dana Farber Cancer Institute, Boston. “We have all this clinical data stored on a whole host of clinical databases, but a lot of it is completely divorced from research data.” This conflict was directly affecting a number of groups at Farber including the multiple myeloma group, who were being frustrated in their efforts to design new clinical trials based on data from previous investigations.
The solution, simply stated, is something like couples counseling—all parties in the same room with a moderator to facilitate communication. For Quackenbush, a timely $1 million Oracle Commitment Grant helped pay for this new technological intervention.
“If you have two databases that won’t talk to each other, the solution is to create a third database. And once we did that, we realized we could reach out and pull in all the information that’s available in the public domain,” explains Quackenbush. Data from sources like ChemBank, GenBank, or DrugBank can all be drawn into a given query: questions as mundane as ‘how many samples of EGFR over-expressers do I have in the freezer?’ to ‘Does XYZ kinase map to ABC receptor?’ At this level of sophistication, the utility for drug discovery work becomes obvious.
The multiple myeloma project is early in development, and still in-house, but the eventual goal is to make the architecture a public resource, allowing anyone to mirror the system’s elegant profile.
About the Author
Neil Canavan is a freelance journalist of science and medicine based in New York.
This article was published in Drug Discovery & Development magazine: Vol. 11, No. 5, May, 2008, pp. 26-28.
Filed Under: Drug Discovery