Can A P3 Reduce Attrition Of Drug Candidates?

By Camille Mojica Rey, Contributing Writer

Approximately half of all Phase 2 clinical trials fail due to efficacy, according to the Tufts Center for the Study of Drug Development. With that kind of failure rate, the pharmaceutical industry has been under immense pressure to reduce costly attrition of prescription drug candidates.

Genetics, genomics, and bioinformatics are emerging as the keys to identifying the best target molecules and reducing the number of efficacy- and safety-related clinical trial failures. These fields are evolving rapidly as academic researchers develop new technology and generate vast amounts of relevant data. It is impractical for companies to work in silos duplicating these efforts. Yet, academics are eager for pharmaceutical industry researchers to apply their laboratory techniques and computing methods to target selection. Industry leaders now believe that choosing better drug targets and reducing attrition of candidate drugs may be achieved by having industry and academic researchers working side-by-side to harness the potential of large-scale, cutting-edge bench science and Big Data computing power.