Abstract
Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.
Authors
Eugene Melamud, D. Leland Taylor, Anurag Sethi, Madeleine Cule, Anastasia Baryshnikova, Danish Saleheen, Nick van Bruggen, Garret A. FitzGerald
Figure 2
Use of deep phenotyping to limit the number of intervention hypotheses.
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ISSN: 0021-9738 (print), 1558-8238 (online)