that we are addressing the most relevant disease opportunities, with physicians to obtain patient tumor biopsies, and we partner with groups that determine the molecular profile of the samples. To enable the generation of preclinical hypotheses for predictive biomarkers, we work side-by-side with wet [lab] biologists who perform in vitro and in vivo pharmacology on our drug candidates. Our group provides the computational framework to extract predictive hypotheses from their experiments.
X: What are the most promising future applications? And how far into the future will we see them?
PR: Computational approaches already permeate biology and there is a great deal of exciting work going on that has impact now. Speaking from the perspective of our work here at Pfizer, developing preclinical predictive biomarkers, translating these into the clinic, and then leveraging these markers to focus on patient populations that are enriched for likely responders permeates our research strategy, and has growing influence on the way in which we develop programs in the clinic. Combining the power of molecular profiling techniques including next-generation sequencing with well-characterized experimental preclinical models can be the difference between a successful registration [of a new FDA approved drug] and a failed clinical trial.
Moving forward, we are exploring opportunities to move beyond correlative analysis and increase our confidence in targets by establishing their causal relationship to disease. I think that it is critical to use computation as an enabling component of pharmaceutical research, but it is deeply misguided to consider the development of an abstract model as the objective of pharmaceutical research rather than the generation of novel chemical or biologic entities.
X: What needs to happen for this technology to reach its potential?
PR: Computational biology has many practical applications – our interest is focused on improving the abysmal and unsustainable low rate of new drug registrations. Simply put, we in research are not