One solution is to find a technical bridge that connects the gap between the data and clinical and research expertise in cancer centers and pharmaceutical and biotech companies, enabling the conversion of the data gathered from cancer centers into actionable knowledge for drug discovery and development. Computational biology companies with the ability to take in data at scale are the best positioned to do this, utilizing the emerging data modalities of the new millennium—“omics” data (e.g., genetics, gene expression, proteomics, metabolomics, etc.)—to make novel drug and disease discoveries. Unfortunately, some critical missing pieces have forestalled the promised results of computational biology, including the lack of adequately powerful analytical tools capable of handling the increasingly rich and voluminous data being generated, coupled with an inability to learn coherent models across several data modalities at once. Fortunately, by combining the best available powerful data-driven model learning and simulation algorithms with the latest available supercomputing horsepower, actionable knowledge can be extracted from this data.  

 

With this new paradigm, there is another opportunity emerging. Cancer center oncologists involved in the generation of the data could be in the best position to interpret the outputs of the models built from such data, with the assistance of the computational team. Further, while pharma companies are, by business necessity, primarily focused on completing a trial and obtaining the results on their drugs for regulatory approval, clinicians continue to provide care for their patients and can leverage the learnings from the computational analyses on an ongoing basis in administering that care. Clinics are also incentivized to do all relevant research and explore all angles with respect to identifying the best treatments for patients, not just the angles that result in favorable information about a particular therapeutic.  

 

Cancer clinics have key capabilities and resources that can be brought to bear on the drug discovery and development problem. The requisite complementary capabilities and resources can be found in systems biology companies with approaches that are flexible enough and powerful enough to learn directly and solely from previously unseen data, and to integrate “known” biology with novel discoveries identified in a data-driven way as needed. Such groups can work with several pharma and biotech partners without fear of overlapping results, as the models learned that reflect proprietary pharma information are learned directly from datasets of interest to the pharma company. Given the high dimensional nature of the datasets involved (e.g., millions of SNPs, thousands of genes or other molecular entities and several relevant endpoints), the ideal companies for such partnerships would have technology platforms that are optimized to leverage supercomputing resources so that results can be obtained in relevant time scales—hours or days, rather than months or years.

 

Collaboration between truly data-driven systems biology groups and oncology clinics can result in the ideal modern-day research tool—a simulation model that can allow researchers to postulate a myriad of possible changes in a cancer and obtain answers and confidence levels in those answers as to how the outcomes for the system (e.g., tumor size, survival, etc.) change as a result. Unlike a specific discovery (e.g., gene 1 appears to be associated with tumor size), such models can be utilized simultaneously by many groups to make many different discoveries; these models make the “signal” inherent in a particular dataset readily accessible to many researchers. For example, a model of glioblastoma that connections genetic variation in tumors to the biological processes underlying the disease (e.g., gene expression networks) to outcomes such as survival and recurrence, might be used to identify new targets for glioblastoma (by modulating different genes in the model and identifying which gene or genes most strongly influence outcomes), and could also be used to stratify patients for a drug already in the clinic.  

 

These models are similar to the gene maps and other research being produced by cancer researchers,^{3, 4} except they are interactive and quantitative, enabling the researcher to ask many specific questions of interest, in real time, and to obtain the answers quickly. In particular, the models that reflect genetic variation will enable patient-specific recommendations via genotypes.

 

Collaborations of the type cited above also have the benefit of rapid follow-up on both the computational and clinical sides. Discoveries made in the collaborations will have already included clinicians who do not then need to be convinced of the approach and the value of the results; they will be most readily disposed to using drugs and diagnostics emerging from the approach at bedside. Further, to the extent that the initial models indicate that more data of a certain type are required in order to build truly predictive models, the relationship is already in place for the clinical group to obtain more of the appropriate data.  

 

In an increasingly computationally driven world, the seamless interaction between computation and the clinic is inexorable. These unique types of collaborations are an opportunity to create the framework for and usher in a paradigm shift in drug research and development. This collaborative work will also create the foundation for personalized medicine approaches to identify the right treatments for the right patients, using the same types of modeling approaches. The data reside in the clinics, the computational platform and ability to leverage the power of supercomputers lay in the next-generation systems biology companies and the ability to turn novel targets and biomarkers into new, better and more targeted oncology drugs lay in the pharma and biotech companies.

 

For these reasons, collaborations between data and computation-driven systems biology companies and forward-thinking oncology clinics are poised to change the face of cancer drug development and cancer itself.  

 

Thomas Neyarapally is responsible for business development, corporate development and the expansion, protection and monetization of Gene Network Sciences’ intellectual property. He was appointed to his current position in 2008, having served as GNS’ vice president of corporate strategy and intellectual property since 2006. Previously, Neyarapally served as an associate in the New York office of the law firm Frommer, Lawrence & Haug LLP, where he focused on transactional, product development and litigation matters in the pharmaceutical and biotech industries. Neyarapally previously was an associate in the corporate department at Chadbourne & Parke LLP, and held the position of analyst at Arthur D. Little. Neyarapally holds a J.D. and an M.B.A. from Cornell University. While attending Cornell's Johnson Graduate School of Management, he served as a partner with BR Ventures, the only student-run venture capital firm in the United States. Neyarapally graduated from the University of Connecticut with a B.S. in chemical engineering.  

References:  

1. Ismail Kola and John Landis, “Can the Pharmaceutical Industry Reduce Attrition Rates?” Nature Reviews: Drug Discovery 3(8) (August 2004): 711-715.

2. Ismail Kola, “The State of Innovation in Drug Development,” Clinical Pharmacology and Therapeutics 83 (2008): 227-230.

3. Li Ding, et al., “Somatic Mutations Affect Key Pathways in Lung Adenocarcinoma,” Nature 455 (2008): 1069-1075.

4. Markus Bredel, et al., “A Network Model of a Cooperative Genetic Landscape in Brain Tumors,” JAMA 302(3) (2009): 261-275.