Big Data On A Network: Massive Integration Of Domain Knowledge For Drug Repurposing

Sergio E. Baranzini, University of California, San Francisco

Stevenson Hall 1300
11:00 AM - 11:50 AM

The time and resources spent in drug development are exorbitant. In parallel, the probabilities that a given lead compound makes it to the clinic are minuscule. Even with the discovery of a few repurposing strategies, the search for a potential repositioning example is still very much trial and error. A paradigm shift is needed if safer, more effective therapeutics are to be developed at a pace that matches the societal demands for treating an ever-increasing segment of the population affected by chronic illnesses, including multiple sclerosis. We have developed a framework to integrate millions of experimental and clinical results in the form of a heterogeneous network, in which multiple entities (drugs, diseases, genes, etc) are connected through known relationships by mining a vast space of the entire domain knowledge in a computationally effective manner. Next machine learning approaches were used to compute the probability that any given drug would interfere with the pathogenic mechanisms of a disease of interest (as a proxy for a potential therapeutic). Our results show that a large proportion of the top predictions correspond to existing indications. However, a number of high-level predictions are not yet known indications, thus providing a compelling rationale to further explore their potential for development. The architecture of this hetnet and the initial results as well as future plans will be discussed during this presentation.